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Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
Open accessAbstract
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper describes the Adaptive Charging Network (ACN), a deployed smart EV charging architecture, and its Adaptive Scheduling Algorithm (ASA), which uses convex optimization and model predictive control to manage large-scale charging under real infrastructure constraints. Using Caltech ACN data and simulations, ASA improves energy delivery in congested systems and can increase operator profit by 3.4 times over uncontrolled charging.
Key findings
- The ACN was first deployed at Caltech in early 2016 and operates at over 100 other U.S. sites; the Caltech ACN grew from 54 custom level-2 EVSEs to 136 commercial level-2 EVSEs plus six 50 kW DC fast chargers, delivering over 1,103 MWh as of July 7, 2020.
- In profit-maximization simulations using September 2018 data and SCE TOU EV-4 tariffs, ASA achieved $2,835/month (98.1% of offline optimal) in an idealized setting and $2,600/month (90% of offline optimal) with non-ideal batteries and EVSEs, compared with $763/month for uncontrolled charging—a 3.4× increase.
- For energy delivery under constrained infrastructure, ASA-QC performed within 0.4% of optimal in scenario II and outperformed baselines by as much as 14.1% compared with EDF at 30 kW capacity; non-ideal EVSEs reduced ASA-QC performance, while non-ideal batteries had relatively small effect.
- Providing a peak hint to ASA-PM increased met energy demands to 97.8% versus 95.6% without hints; ASA-PM was within 3.6% of optimal profit, and with the hint within 1.9%.
Methods
- The paper presents the ACN architecture and ASA, an online model predictive control and convex optimization framework with configurable utility functions and constraints for unbalanced three-phase infrastructure, EVSE pilot-signal quantization, and non-ideal battery behavior.
- Data come from the Caltech ACN from May 1 to October 1, 2018, when only 54 EVSEs on transformer t1 were active; this period included 10,415 charging sessions and 92.78 MWh delivered. Simulations use ACN-Sim with 5-minute time slots, a 12-hour optimization horizon, and five scenarios in Table II.
- The analysis compares ASA variants against least laxity first, earliest deadline first, round-robin, uncontrolled charging, and an offline optimal benchmark under varying transformer capacities (20–150 kW) and SCE TOU EV-4 tariff and demand-charge conditions.
Limitations
- The paper states that due to limited space it cannot address all possible ACN and ASA use cases; it assumes drivers provide accurate departure-time and energy-request estimates for simplicity; it does not consider V2G; DCFCs are not directly controlled; and load/generation forecasting and future-arrival information are outside scope.
- The text makes evident that ASA performance is evaluated through simulations using historical Caltech ACN data rather than live field tests of the scheduling algorithm, and the workload data come from a period when charging was free and only 54 EVSEs on one transformer were active.
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71 Own interpretation · 42 No basis given · 30 Own result · 29 Sources not found · 3 Own method · 2 Not checked yet
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“allows for significant over-subscription of electrical infrastructure”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“supports electric vehicle (EV) charging at scale”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“Through our experience building large-scale charging facilities, we find that common assumptions made in theoretical models do not hold in many practical systems.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“consistently outperforms baseline algorithms when delivering energy in highly congested systems”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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In the paper
“our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“demonstrates ASA's effectiveness in realistic scenarios through simulation.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“From Table I, we observe a significant difference in system usage between weekdays and weekends.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Data Collected
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“Also, charging sessions on weekends tend to be shorter than those during the week.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Data Collected
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In the paper
“In our data, we observe that without proper control, these phase imbalances can be significant, as seen in Fig. 5.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Infrastructure Modeling
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“we present the Adaptive Scheduling Algorithm (ASA) framework for online (causal) smart charging algorithms based on convex optimization and model predictive control (MPC).”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“The architecture enables real-time monitoring and control”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“ACN-Live, a framework for safely field testing algorithms directly on the Caltech ACN.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“which was first deployed on the Caltech campus in early 2016”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“ACN-Sim, an open-source simulator that uses data from ACN-Data and realistic models derived from actual ACNs to provide researchers with an environment to evaluate their algorithms and test assumptions [3]”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [3]
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In the paper
“ACN-Data, a collection of real finegrained charging data collected from the Caltech ACN and similar sites [2]”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“We demonstrate how the Adaptive Scheduling Algorithm handles these challenges”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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“the ACN has led to the creation of the ACN Research Portal.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“These include national laboratories, universities, schools, businesses, apartment complexes, hotels, and public parking facilities.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
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“It has spawned a company, PowerFlex Systems, which operates over 100 similar charging systems around the United States.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
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“is currently operating at over 100 other sites in the United States”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
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In the paper
“In the ACN, phase AB has 26 stations, whereas phases BC and CA each have 14.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Drivers
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In the paper
“As discussed in Section III-D, the electrical infrastructure within the ACN is oversubscribed and often unbalanced.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Infrastructure Modeling
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In the paper
“These EVSEs have delivered over 1,103 MWh as of July 7, 2020.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > III. ADAPTIVE CHARGING NETWORK ARCHITECTURE
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“During this period, the system served 10,415 charging sessions and delivered a total of 92.78 MWh of energy.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Data Collected
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“On weekdays we see a peak in arrivals between 7:00 -10:00 followed by a peak in departures between 16:00 -19:00.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Battery Tail Capacity Reclamation
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In the paper
“Weekends, however have a more uniform distribution of arrivals and departures.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Battery Tail Capacity Reclamation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Since its installation in early 2016, the Caltech ACN has grown from 54 custom-built level-2 electric vehicle supply equipment (EVSEs) in a single garage [25], to 136 commercially available level-2 EVSEs and six 50 kW DC Fast Chargers (DCFCs), spread between three parking garages on campus.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > III. ADAPTIVE CHARGING NETWORK ARCHITECTURE
What the sources say
- Not matchedcites [25]
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plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“Since each EVSE in the pod has a maximum charging rate of 32 A, these lines are oversubscribed by 3.2 times.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Physical System
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The Caltech ACN also has a much smaller peak in arrivals beginning around 18:00, which is made up of community members who use the site in the evening including some patrons of the nearby campus gym.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Battery Tail Capacity Reclamation
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“For reference, the actual transformer in our system is 150 kW, and a conventional system of this size would require 362 kW of capacity.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“Using real workload data collected from the Caltech ACN and accurate models of ACN infrastructure, we demonstrate that ASA offers significant improvements in terms of energy delivered with constrained infrastructure when continuous pilots are allowed and performs comparably to baselines when pilots are restricted to a discrete set of values.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
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In the paper
“This results in an maximum increase in value of $0.000050 / kWh, which is an three orders of magnitude lower than the minimum cost of energy in Table III.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The ACN and its scheduling algorithm (ASA) have been proven at scale through deployments around the United States, including the first ACN installed on the Caltech campus in 2016.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, both ASA-PM algorithms (and the offline optimal) trade-off higher energy costs for much lower peaks resulting in lower overall costs.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
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In the paper
“To facilitate this new frontier of research, ACN Research Portal provides open-access data from the Caltech and JPL ACNs and an open-source simulation environment based on the ACN architecture.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
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In the paper
“While EVSE quantization and non-ideal batteries each reduce the operator's profit, even in scenario V, ASA-PM w/Hint still produces 90% of the optimal profit.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Even with the peak hint, ASA-PM does not meet 100% of demands even though the offline optimal does.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“RR uses no information aside from which EVs are currently present, and as such, performs the worst.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Uncontrolled, LLF and RR result in lower energy costs, but incur very high demand charges.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Profits from both ASA-PM and ASA-PM w/ Hint, are within 3.6% and 1.9% of the optimal respectively, and far exceed the profits of all baseline algorithms.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Reference [2] uses data from the ACN to predict user behavior, size solar generation for EV charging systems, and evaluate the potential of EV charging to smooth the duck curve.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“There is also a tremendous literature on EV charging algorithms; see surveys [9], [10] for extensive pointers to the literature.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Reference [20] proposes a pricing scheme to allocate costs (including demand charge) to charging sessions.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [20]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The Smart Energy Plaza (SEP) at Argonne National Laboratory consists of six controllable level-2 EVSEs [4].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Meanwhile, the Parker project utilized a testbed of 10 bi-directional EVSEs at a commercial site to investigate the potential of EVs to provide frequency regulation services and adapt to marginal emissions signals [8].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“References [22], [23] use data to quantify fleet-level flexibility within a charging facility”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [23]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithms can also enable additional revenue streams by providing grid services.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Many large-scale charging sites will provide charging during daytime hours.
None of the sources cited for this claim have been checked yet.
In the paper
“Several works have been based on the ACN and data collected from it.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“scheduling algorithms can reduce operating costs by optimizing for time-of-use tariffs, demand charges, and on-site renewable generation”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Most sites are unable to install more than a few charging ports due to limited infrastructure capacity and fear of high electricity bills.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Smart charging allows sites to scale their port capacity without costly infrastructure upgrades.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Most sites are unable to install more than a few charging ports due to limited infrastructure capacity and fear of high electricity bills.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Some recent work specifically related to large-scale EV charging includes [11]- [19].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [19]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Reference [21] clusters sessions based on their charging behavior using time series of charging current collected from the ACN.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [21]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Several smart EV charging systems have been developed, though usually at a smaller scale than the ACN.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Of particular relevance to this work are [14] and [19], both of which use a MPC framework for scheduling EV charging subject to TOU rates.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [19]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“this approach was first proposed in [20].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [20]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Likewise, the WinSmartEV system at UCLA consists of quad-port level-2 EVSEs capable of sharing a single oversubscribed circuit and multiple level-1 chargers with binary control [5], [6].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The My Electric Avenue project tested a control system for 200 Nissan LEAFs to manage congestion in the distribution system [7].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since L(t) and G(t) are unknown for t > 0 this formulation relies on a prediction of these functions into the future.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Charging quickly:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Future load L(t) and on-site generation G(t) can be predicted for the profit objective.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our work adds to the existing body of research insights from building real-world EV charging systems.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The constraints in (1) are second-order cone constraints, which are convex and can be handled by many offthe-shelf solvers such as ECOS, MOSEK, and Gurobi.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Infrastructure Modeling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“An exception to this is the work of De Hoog et al. [29], which considers an unbalanced three-phase distribution system but only in the case of wyeconnected EVSEs.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Infrastructure Modeling
What the sources say
- Not matchedcites [29]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These periodic computations close the control loop and account for discrepancies between the control signal sent to each EV and its actual current draw.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Control algorithms:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be difficult to diagnose why a car is charging below its allocated pilot signal since the J1772 standard does not provide a way to gather the EV's state of charge.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > B. EVSEs and Battery Management System
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The utility functions described so far are not strictly concave in r and hence the optimal solution, r * , is generally non-unique.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Minimizing total load variations:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here U QC encourages the system to deliver energy as quickly as possible, which helps free capacity for future arrivals.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This constraint helps prevent contactor wear in the EVSEs and improves user experience since most vehicles will notify their owner when charging stops.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Algorithm 1: Adaptive Scheduling Algorithm (ASA)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: Most vehicles will notify their owner when charging stops.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unfortunately, these constraints are discrete, making it difficult to incorporate them into optimization-based algorithms.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Algorithm 1: Adaptive Scheduling Algorithm (ASA)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For our simulations, we use ACN-Sim, which includes realistic models for each of the scenarios above.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > B. Practical Scenarios
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The pilot signal constraints imposed by EVSEs described in Section IV-C are discrete and intractable in general for large problems.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Quantization of Pilot Signal
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This work extends [25], [26], which describe the ACN in earlier stages of development”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [25]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We include the regularizer u ES (r) to promote equal sharing between similar EVs and force a unique solution.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“In contrast, non-ideal batteries follow the constant current, constant voltage model described in [3], where the initial state of charge and battery capacity are fit to maximize tail behavior, and the tail begins at 80% state-of-charge.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > B. Practical Scenarios
What the sources say
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that our system must be able to handle large numbers of flexible sessions on weekdays and smaller numbers of relatively inflexible sessions on weekends.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Data Collected
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Caltech ACN also has a much smaller peak in arrivals beginning around 18:00, which is made up of community members who use the site in the evening including some patrons of the nearby campus gym.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Battery Tail Capacity Reclamation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“[24] uses ACN-Sim to train reinforcement learning agents to schedule large-scale EV charging.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [24]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This behavior precludes simple solutions such as installing large numbers of level-1 chargers, which would be too slow on weekends and for low laxity weekday sessions, or small numbers of level-2 chargers, which would be insufficient for the number of concurrent sessions on weekdays.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > A. Data Collected
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Other models rely on machine learning to learn the relationship between state of charge and current draw [32].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > B. Battery Management System Behavior
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is equivalent to cost minimization when π = 0.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Charging quickly:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By building and operating the Caltech ACN, we have identified several important features of the physical system that have not been addressed in the EV charging literature but pose real problems for implementing practical EV scheduling algorithms.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Drivers
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“by providing a more thorough description of the ACN”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > Abstract
What the sources say
- Not matchedcites [25]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With this scheme, we can quickly reclaim capacity during the tail region, while still allowing EVs to throttle back up if this reclamation was premature.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > E. Battery Tail Capacity Reclamation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is a common use case when electricity prices are static or when user satisfaction is the primary concern.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“These baseline algorithms are very common in the deadline scheduling literature and have been applied previously to the EV charging domain [34], [35].”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
What the sources say
- Not matchedcites [34]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [35]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Load management like that found in the ACN is necessary in places without available electrical capacity, but is still helpful in all sites, since we are able to minimize demand charges / responds to TOU rates, even if electrical infrastructure is not a problem.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is reasonable since EVs' onboard charger generally includes power factor correction and voltage phase angles can be easily measured.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, the round robin algorithm is a generalization of the equal sharing algorithm used by many charging providers today.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Beyond its operational role of charging hundreds of EVs each week, the Caltech ACN and similar sites at research institutions like JPL, NREL, SLAC, and UC San Deigo, provide a valuable platform for research in managed EV charging.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These algorithms are not price aware.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We also include the quick charge objective as a regularizer, which encourages the scheduling algorithm to front-load charging within a TOU period.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, the J1772 standard does not allow pilot signals below 6 A (except 0).”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Places that generally fit this description are workplaces, schools, multi-unit dwellings, and public charging facilities with long dwell times.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When even more information is available, i.e., a model of the vehicle's battery or predictions of future EV arrivals, this information can also be accounted for in the constraint set R and objective U(r).”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only ASA-QC actively optimizes over infrastructure constraints, allowing it to better balance phases (increasing throughput) and prioritize EVs including current and anticipated congestion.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be hard to reason about exactly why this occurs, though it appears that the postprocessing step leads to initial conditions for the next solve of OPT to produce a higher revenue, higher cost solution.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Within the Caltech ACN, we have five types of EVSEs.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“EVSEs from ClipperCreek only support five pilot signals {0, 8, 16, 24, 32} for 32 amp EVSEs and {0, 16, 32, 48, 64} for 64 amp EVSEs.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We fix the transformer capacity to 150 kW and consider the previous baselines along with uncontrolled charging, which is the most common type of charging system today.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is, however, important that hints not be too large, as the algorithm can increase the peak as needed, but once a high peak is set, the demand charge cannot be lowered.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, LCFS credits for EV charging have averaged between $0.13 -$0.16 / kWh in 2018-2019.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may be because the minimum current constraint leads to better phase balancing.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ACN sites can incorporate low laxity sessions as well, but these should be a small fraction of the total sessions to get the maximum benefit from load management.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > VII. CONCLUSION
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Likewise, EDF uses only information about departure time, while LLF also makes use of the EVs energy demand.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This percentage is chosen based on maximum historic month-to-month variability in the optimal peak (+11%/-16%).”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > D. Profit Maximization With TOU Tariffs and Demand Charge
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A key feature of the ASA framework is its ability to account for all available information cleanly.”
§ Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging > C. Energy Delivery With Constrained Infrastructure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
Most sites are unable to install more than a few charging ports due to limited infrastructure capacity and fear of high electricity bills.
“However, with current technology, most sites are unable to install more than a few charging ports due to limited infrastructure capacity and fear of high electricity bills.”
The paper relies on this without support.
Many large-scale charging sites will provide charging during daytime hours.
“Since many of these sites will provide charging during daytime hours, they can make use of abundant solar energy production and enable EVs to provide grid services throughout the day.”
The paper relies on this without support.
Smart charging will substantially reduce the environmental footprint of transportation.
“Smart charging will substantially reduce the environmental footprint of transportation while unlocking immense potential for demand-side management.”
The paper relies on this without support.
Providing charging at diverse sites can reduce range anxiety and provide an alternative to personal charging ports for those who cannot install them at their homes.
“Doing so can reduce range anxiety and provide an alternative to personal charging ports for those who cannot install them at their homes.”
The paper relies on this without support.
Driver demands on the system are stochastic.
“due to the stochastic nature of driver demands on the system”
The paper relies on this without support.
Most EVSEs on the market today only support a finite set of pilot signal values and require quantization of the control signal.
“most EVSEs on the market today, including the ClipperCreek, AeroVironment, Webasto, and Tesla EVSEs in the Caltech ACN, only support a finite set of pilot signal values and require quantization of the control signal.”
The paper relies on this without support.
Human behavior can add significant randomness to the system.
“Human behavior can add significant randomness to the system.”
The paper relies on this without support.
Drivers may arrive, depart, or change their input parameters at any time.
“Drivers may arrive, depart, or change their input parameters at any time.”
The paper relies on this without support.
Most previous work on charging algorithms has focused on single-phase or balanced three-phase systems, making them inapplicable to charging systems like the ACN.
“While many algorithms have been proposed to handle charging with an aggregate power limit or even a hierarchy of limits, most previous work has focused on single-phase or balanced three-phase systems, making them inapplicable to charging systems like the ACN.”
The authors state this as an assumption.
Each charging EV has a unity power factor.
“We assume in this model that each charging EV has a unity power factor”
The authors state this as an assumption.
The voltage phase angles are separated by ±120°.
“which we assume are separated by ±120 •”
The paper relies on this without support.
For level-2 EVSEs, each EV's onboard charger and battery management system (BMS) controls its charging rate.
“For level-2 EVSEs, each EV's onboard charger and battery management system (BMS) controls its charging rate.”
The paper relies on this without support.
Requiring the charging rate to be non-zero from when a car plugs in until its charging demand is met helps prevent contactor wear in the EVSEs.
“This constraint helps prevent contactor wear in the EVSEs and improves user experience since most vehicles will notify their owner when charging stops.”
The paper relies on this without support.
Most vehicles will notify their owner when charging stops.
“since most vehicles will notify their owner when charging stops.”
The paper relies on this without support.
Current charging hardware does not provide state of charge information.
“Since this information is not available with current charging hardware, we use a model-free approach to estimate battery behavior in real-time and use closed-loop control to account for modeling errors.”
The authors state this as an assumption.
Nominal voltage is assumed when expressing r_i(k) in amps and e_i(k) and ê_i(k) in δ × amps.
“For simplicity of notation, we express r i (k) in amps and e i (k) and êi (k) in δ × amps, assuming nominal voltage.”
The authors state this as an assumption.
The maximum time between scheduler calls is set to the length of one period, so only the first charging rate in r* will be applied.
“For simplicity, we assume that the maximum between scheduler calls (see Algorithm 1) is set to the length of one period, so that only the first charging rate in r * will be applied.”
The authors state this as an assumption.
The system does not consider V2G.
“Constraints (5a) ensure that the charging rate in each period is non-negative (we do not consider V2G) and less than its upper bound defined by the EV's BMS and the maximum pilot supported by the EVSE.”
The paper relies on this without support.
Future load L(t) and on-site generation G(t) can be predicted for the profit objective.
“Since L(t) and G(t) are unknown for t > 0 this formulation relies on a prediction of these functions into the future.”
The paper relies on this without support.
Equal sharing among EVs minimizes line losses along the lines feeding each EVSE.
“This regularizer also promotes equal sharing among the EVs, which is desirable for the operator and drivers and minimizes line losses along the lines which feed each EVSE.”
The authors state this as an assumption.
Drivers provide accurate estimates of their departure time and energy request.
“For simplicity we assume that drivers provide accurate estimates of their departure time and energy request.”
The authors state this as an assumption.
Ideal batteries follow the pilot signal exactly.
“Finally, ideal batteries are assumed to follow the pilot signal exactly.”
The authors state this as an assumption.
It is feasible to deliver a minimum charging rate to each EV.
“This constraint implicitly assumes that it is feasible to deliver a minimum charging rate to each EV, thus charging infrastructure should be designed with this constraint in mind if the operators want to ensure a minimum charging rate to each EV.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, due to the random and relatively high EV charging power [2], the sudden pulse-like and high charging load of the charging stations strains the distribution grid [3].”
From Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility (2023) · cites this paper as [2]
Experimental — accuracy not yet measured
Citations
Cites
A Review of Charge Scheduling of Electric Vehicles in Smart Grid
Mukherjee, J. C., & Gupta, A. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2356559 · Cited by 415 · cited 1×
“There is also a tremendous literature on EV charging algorithms; see surveys [9], [10] for extensive pointers to the literature.”
BackgroundLee, Z. J., Li, T., & Low, S. H. (2019) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3307772.3328313 · Cited by 411 · cited 5×
“Input through the mobile app can be highly inaccurate, as shown in [2].”
MethodInfluentialSelf-citationSmart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective
Wang, Q., Liu, X., Du, J., & Kong, F. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2518628 · Cited by 275 · cited 1×
“There is also a tremendous literature on EV charging algorithms; see surveys [9], [10] for extensive pointers to the literature.”
BackgroundOptimal Charging of Electric Vehicles Taking Distribution Network Constraints Into Account
de Hoog, J., Alpcan, T., Brazil, M., Thomas, D. A., & Mareels, I. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2014.2318293 · Cited by 241 · cited 1×
“[29], which considers an unbalanced three-phase distribution system but only in the case of wyeconnected EVSEs.”
BackgroundTwo-Stage Economic Operation of Microgrid-Like Electric Vehicle Parking Deck
Guo, Y., Xiong, J., Xu, S., & Su, W. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2424912 · Cited by 230 · cited 1×
“Of particular relevance to this work are [14] and [19], both of which use a MPC framework for scheduling EV charging subject to TOU rates.”
BackgroundData-driven smart charging for heterogeneous electric vehicle fleets
Frendo, O., Graf, J., Gaertner, N., & Stuckenschmidt, H. (2020) · Energy and AI · doi:10.1016/j.egyai.2020.100007 · Cited by 179 · cited 1×
“Other models rely on machine learning to learn the relationship between state of charge and current draw [32].”
BackgroundZhang, L., & Li, Y. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2505298 · Cited by 170
Two-Stage Mechanism for Massive Electric Vehicle Charging Involving Renewable Energy
Wang, R., Wang, P., & Xiao, G. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2523256 · Cited by 138
Quiros-Tortos, J., Ochoa, L., & Butler, T. (2018) · IEEE Power and Energy Magazine · doi:10.1109/mpe.2018.2863060 · Cited by 126 · cited 1×
“The My Electric Avenue project tested a control system for 200 Nissan LEAFs to manage congestion in the distribution system [7].”
MethodiEMS for large scale charging of electric vehicles: Architecture and optimal online scheduling
Chen, S., & Tong, L. (2012) · IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgridcomm.2012.6486056 · Cited by 83 · cited 1×
“Some recent work specifically related to large-scale EV charging includes [11]–[18].”
Background
Research works printed but not linked: 4
- lee, 2021, acnportal-Experiments
- 2017, General Service Time-of-Use Electric Vehicle Charging—Demand Metered
- 2020, Caltech ACN Dashboard
- 2020, Global EV outlook 2020: Entering the decade of electric drive?, International Energy Agency
Cited by
Sorted by citations
Sadeghian, O., Oshnoei, A., Mohammadi‐Ivatloo, B., Vahidinasab, V., & Anvari‐Moghaddam, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105241 · Cited by 448
“The information system includes a communication interface, data storage, mobile app, and control algorithm [191].”
Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects
Li, Y., Yu, C., Shahidehpour, M., Yang, T., Zeng, Z., & Chai, T. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3303358 · Cited by 223
“In addition, it has witnessed the rapid development of EVs in recent years, which has already become a critical SG component [81], [82], [83], as shown in Fig.”
BackgroundSaner, C. B., Trivedi, A., & Srinivasan, D. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3140927 · Cited by 169
Ahmed, I., Rehan, M., Basit, A., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3258859 · Cited by 109
Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility
Yan, D., Ma, C., & Chen, Y. (2023) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2022.3213173 · Cited by 96
“However, due to the random and relatively high EV charging power [2], the sudden pulse-like and high charging load of the charging stations strains the distribution grid [3].”
BackgroundA Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques
Rashid, M., Elfouly, T., & Chen, N. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3457499 · Cited by 91
“This dataset can be used to understand user behavior and smooth grid demand through the development of adaptive charging algorithms [36], [42].”
MethodElectric vehicle hosting capacity analysis: Challenges and solutions
Karmaker, A. K., Prakash, K., Siddique, M. N. I., Hossain, M. A., & Pota, H. R. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113916 · Cited by 90
Leon Fidele Nishimwe, H., & Yoon, S. (2021) · Energies · doi:10.3390/en14113152 · Cited by 57
Taha, F. A., Vincent, T. L., & Bitar, E. Y. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3384871 · Cited by 64
“In this example, we show how to construct individual flexibility sets from EV charging models used by real-world EV smart charging systems [1], [2].”
MethodWang, Y., Wang, H., Razzaghi, R., et al. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123547 · Cited by 53
Smart charging business model framework for electric vehicle aggregators
Afentoulis, K. D., Bampos, Z. N., Vagropoulos, S. I., et al. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120179 · Cited by 49
Karmaker, A. K., Behrens, S., Hossain, M. J., & Pota, H. R. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139145 · Cited by 39
Varone, A., Heilmann, Z., Porruvecchio, G., et al. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113845 · Cited by 36
Saklani, M., Saini, D. K., Yadav, M., & Gupta, Y. C. (2024) · IEEE Access · doi:10.1109/access.2024.3470218 · Cited by 39
“Controlled charging, however, includes advanced features to communicate with the power grid to optimise charging times and tariffs based on various factors such as energy demand, pricing, and grid stability [21], [22].”
BackgroundPareto optimality in cost and service quality for an Electric Vehicle charging facility
Woo, S., Bae, S., & Moura, S. J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116779 · Cited by 36
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research
Lee, Z. J., Sharma, S., Johansson, D., et al. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3103156 · Cited by 35
“In addition, ACN-Sim has been used to design dynamic pricing schemes and cost-optimal scheduling [28], train reinforcement learning agents for EV charging systems [29], and examine the effect of non-ideal batteries and EVSE pilot quantization on model predictive control and baseline algorithms [30].”
MethodSelf-citationAldossary, M., Alharbi, H. A., & Ayub, N. (2024) · Mathematics · doi:10.3390/math12172627 · Cited by 35
Ahmadian, A., Ghodrati, V., & Gadh, R. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121884 · Cited by 34
An industry 4.0 approach to electric vehicles
Athanasopoulou, L., Bikas, H., Papacharalampopoulos, A., Stavropoulos, P. G., & Chryssolouris, G. (2022) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2022.2081363 · Cited by 34
“…and circularity (Gallagher and Nelson 2014) ● Product-Service System, as a hybrid technicalbusiness model, to solve intellectual property rights (IPR) data (Meier, Roy, and Seliger 2010) ● Blockchain towards certification and IPR ● Safety Network protocols (CIP Safety, PROFIsafe, openSAFETY, FSoE…”
MethodAn Intelligent Electric Vehicle Charging System in a Smart Grid Using Artificial Intelligence
Senthilkumar, T., Sivaraju, S. S., Anuradha, T., et al. (2025) · Optimal Control Applications and Methods · doi:10.1002/oca.3252 · Cited by 31
Hybrid Machine Learning Forecasting for Online MPC of Work Place Electric Vehicle Charging
McClone, G., Ghosh, A., Khurram, A., Washom, B., & Kleissl, J. P. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3296014 · Cited by 34
“The data used in this work comes from EVs that charged with ChargePoint [22], but it is assumed that there is an ALM system in place based on the ALM developed by PowerFlex [7], [23].”
MethodDynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks
Aoun, A., Adda, M., Ilinca, A., Ghandour, M., & Ibrahim, H. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15070324 · Cited by 35
“In article [19], the authors investigated the role of smart charging algorithms in optimizing EV charging schedules.”
BackgroundKramer, J., Riza, L., & Petzoldt, T. (2023) · Energy Research & Social Science · doi:10.1016/j.erss.2023.103054 · Cited by 32
Robust EV Scheduling in Charging Stations Under Uncertain Demands and Deadlines
Sone, S. P., Lehtomäki, J. J., Khan, Z., Umebayashi, K., & Kim, K. S. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3466514 · Cited by 31
“To address the limited availability of real-world EV charging data in the ACN data [3], this section introduces a synthetic data generation model designed to capture real-world EV charging station arrival patterns.”
Harmonics Measurement, Analysis, and Impact Assessment of Electric Vehicle Smart Charging
Senol, M., Bayram, I. S., Hunter, L., et al. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3505778 · Cited by 29
“These frameworks are designed to mitigate voltage drops and reduce transformer stress, particularly for domestic charging [15], as well as balance phases in commercial applications [16].”
Analysis of AC/DC/DC Converter Modules for Direct Current Fast-Charging Applications
Piasecki, S., Zaleski, J., Jasiński, M., Bachman, S., & Turzyński, M. (2021) · Energies · doi:10.3390/en14196369 · Cited by 30
EV Charging Scheduling Under Demand Charge: A Block Model Predictive Control Approach
Yang, L., Geng, X., Guan, X., & Tong, L. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3260804 · Cited by 26
“The on-off charging model assumed in A2 is a reasonable approximation to the actual charging profile 3 [40], and it can be extended to cases with continuously varying charging rates [8].”
BackgroundMethodInfluentialLu, C.-F., Liu, G. P., Yu, Y., & Cui, J. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3488075 · Cited by 24
“Lee et al. [8] proposed an adaptive charging optimization algorithm based on convex optimization and MPC, accounting for three-phase imbalance and nonideal battery behavior, and validated its effectiveness through real-world deployment.”
A New Charging Scheme Based on Mobile Charging Robots Cluster: A Three-Level Coordinated Perspective
Zhang, Z., Dong, Z. Y., & Yip, C. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3352182 · Cited by 22
“…strategy with considering reducing degradations in batteries is proposed in [5], and an adaptive charging network for the smart EV charging service is introduced in [6], where some of the practical challenges in real-world charging systems are demonstrated and handled by the scheduling algorithm.”
Diaz-Londono, C., Fambri, G., Maffezzoni, P., & Gruosso, G. (2024) · eTransportation · doi:10.1016/j.etran.2024.100326 · Cited by 24
“The power is computed with the 208 V of the EVSE reported by ACN [38].”
Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models
Gopinathan, N., & Shanmugam, P. (2022) · Energies · doi:10.3390/en15145230 · Cited by 22
“[127], 2021 Despite its relevance in scheduled charging, pa‐ rameters affecting grid stability need to be well addressed.”
BackgroundTruong, V. B., & Le, L. B. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107141 · Cited by 21
Wireless Wearable Devices and Recent Applications in Health Monitoring and Clinical Diagnosis
Su, M., Hua, J., Sun, X., Liu, Z., Shi, Y., & Pan, L. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00141-5 · Cited by 23
“In order to solve these problems, many wireless charging networks also have emerged [26, 27].”
Qian, K., Fachrizal, R., Munkhammar, J., Ebel, T., & Adam, R. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105453 · Cited by 19
VSI Controller Integrated DC-DC Converter for Electrical Vehicle Charging System in PV Solar Grid
Aditya, P. V. S., & Rubavathy, S. J. (2023) · Electric Power Components and Systems · doi:10.1080/15325008.2023.2270593 · Cited by 18
Soliman, I. A., Tulsky, V. N., el‐Ghany, H. A. A., & ELGebaly, A. E. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109836 · Cited by 18
Operational challenges and solution approaches for low voltage distribution grids — A review
Kotsonias, A., Hadjidemetriou, L., Asprou, M., & Panayiotou, C. G. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111258 · Cited by 23
Online electric vehicle charging detection based on memory-based transformer using smart meter data
Kamoona, A. M., Song, H., Jalili, M., Wang, H., Razzaghi, R., & Yu, X. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126353 · Cited by 16
Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications
Ali, Z., Putrus, G. A., Marzband, M., Gholinejad, H. R., Saleem, K., & Subudhi, B. (2022) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2022.3164999 · Cited by 17
“[33] present an adaptive charging network, which considers request from users and information from the grid for”
BackgroundPlug-in electric vehicle load modeling for charging scheduling strategies in microgrids
Güzel, İ., & Göl, M. (2022) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2022.100819 · Cited by 16
Vehicle-to-Grid Fleet Service Provision Considering Nonlinear Battery Behaviors
Jaworski, J., Zheng, N., Preindl, M., et al. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3305235 · Cited by 16
“Some EVCS smart charging algorithms partially accounted for nonlinear charging/discharging characteristics [11], [12], [29], [30], but few were able to model all nonlinear factors in a computation efficient approach.”
BackgroundMethodEV Smart Charging in Distribution Grids–Experimental Evaluation Using Hardware in the Loop Setup
Yu, Y., Herdt, L. D., Shekhar, A., Mouli, G. R. C., & Bauer, P. (2024) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2024.3352265 · Cited by 16
“Besides, the EV charging current can only be adjusted with discrete steps, and the step size depends on the manufacturing design of the electric vehicle supply equipment (EVSE) and the EV [18], [19].”
BackgroundWang, Z., Sasse, J.-P., & Trutnevyte, E. (2025) · Energy · doi:10.1016/j.energy.2025.135452 · Cited by 15
Predictability and fairness in load aggregation and operations of virtual power plants
Mareček, J., Roubalik, M., Ghosh, R., et al. (2022) · Automatica · doi:10.1016/j.automatica.2022.110743 · Cited by 15
Lee, Z. J., Sharma, S., & Low, S. H. (2021) · IEEE Electrification Magazine · doi:10.1109/mele.2021.3093597 · Cited by 14
Self-citationInduction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
Salahuddin, H., Imdad, K., Chaudhry, M. U., et al. (2022) · Applied Sciences · doi:10.3390/app12094647 · Cited by 13
Liu, G., Wang, B., Li, T., Deng, N., Song, Q., & Zhang, J. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125790 · Cited by 12
Using EV charging control to provide building load flexibility
Bhundar, H. S., Golab, L., & Keshav, S. (2023) · Energy Informatics · doi:10.1186/s42162-023-00261-8 · Cited by 12
Nath, A. P., Rather, Z. H., Mitra, I., et al. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3252273 · Cited by 11
“require robust communication infrastructure between different entities in the EV ecosystem [10], [11], as well as cooperation between the involved entities.”
BackgroundData-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea
Kim, K., Kim, G., Yoo, J. W., et al. (2024) · Sensors · doi:10.3390/s24216800 · Cited by 11
“A typical approach is utilizing statistical features from EV time-series data, such as average within a day [17–19].”
MethodElectric Vehicles Security and Privacy: Challenges, Solutions, and Future Needs
Brighente, A., Conti, M., Donadel, D., et al. (2026) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3801968 · Cited by 11
“The signals exchanged through the control pilot either control the amount of current delivered to the EV [41] or are used to check the connection status and remove power from the adapter in case of disconnection to prevent the user injuries [9].”
Quantum Informative Analysis in Smart Power Distribution
Ahanger, T. A., Bhatia, M., & Aldaej, A. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3691350 · Cited by 11
“The smart electricity grid functions to acquire data on energy usage to ensure a cost-effective, stable, and secure electricity provision [6].”
Zhang, T., Tang, D., Fan, P., et al. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3458932 · Cited by 10
Xu, L., Gu, X., & Song, Z. (2025) · Applied Energy · doi:10.1016/j.apenergy.2024.125232 · Cited by 10
Sarmokadam, S., Suresh, M., & Mathew, R. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.03.032 · Cited by 10
Stenstadvolden, A., Hansen, L., Zhao, L., Kapourchali, M. H., & Lee, W. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3292812 · Cited by 10
“Currently,differentEVchargingmechanismsdevelopedand conductedextensivelyintheexistingliteraturetoaddressthe EVchargingchallengesonpowergrids[4],[5],[6],[7],[8], [9],[10],[11],[12],[13],[14],[15],[16],[17],[18],[19], [20].”
BackgroundInnovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets
Mangini, A. M., & Roccotelli, M. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123699 · Cited by 10
“[11] propose an MPC, adopted by charging stations for reducing operating costs, that manages EVs as flexible loads.”
BackgroundMethodKardous, F., Mejdi, L., & Grayaa, K. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.123660 · Cited by 9
Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management
Mejdi, L., Kardous, F., & Grayaa, K. (2024) · IEEE Access · doi:10.1109/access.2024.3374897 · Cited by 9
“Other works tried to combine these two categories such as in [5] and [12].”
BackgroundMethodInfluentialGraph reinforcement learning for power grids: A comprehensive survey
Hassouna, M., Holzhüter, C., Lytaev, P., Thomas, J., Sick, B., & Scholz, C. (2026) · Energy and AI · doi:10.1016/j.egyai.2025.100671 · Cited by 24
“While current GRL approaches demonstrate strong performance in simulation, real-world deployment remains in an early stage, though pilot projects for EV charging management exist [68].”
Soliman, I. A., Tulsky, V. N., & el‐Ghany, H. A. A. (2024) · International Ural Conference on Electrical Power Engineering (UralCon) · doi:10.1109/uralcon62137.2024.10718911 · Cited by 8
“There is no accurate charging profile for EVs as there is uncertainty in the behavior of EVs and EVs drivers [25].”
BackgroundMinimizing System Entropy: A Dual-Phase Optimization Approach for EV Charging Scheduling
Yuan, W., & Guan, L. (2025) · Entropy · doi:10.3390/e27030303 · Cited by 8
“Lee et al. [23] proposed the Adaptive Charging Network (ACN) framework, which uses a scheduling algorithm based on convex optimization and model predictive control to address challenges such as infrastructure imbalance and non-ideal battery charging behavior in electric vehicle charging.”
Long-Distance Charge-Route Planning for Electric Vehicles: A Multi-Solution Approach
Wang, X.-C., Gong, Y., Huang, T., et al. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3563094 · Cited by 8
“Meanwhile, research on EV routing predominantly focuses on business aspects such as power system optimization [11], [12], [13], EV fleet coordination [14], [15], [16], [17], and CS management [18], [19], [20], [21], [22], with limited emphasis on individual EV charging-route planning.”
Varone, A., Porruvecchio, G., & Romanino, A. (2025) · Energy Conversion and Management · doi:10.1016/j.enconman.2025.120957 · Cited by 8
Stecca, M., Vermeer, W., Soeiro, T. B., et al. (2022) · IEEE Transportation Electrification Conference & Expo (ITEC) · doi:10.1109/itec53557.2022.9814040 · Cited by 8
“Intelligent charging strategies, which aim to spread the maximum available charging power of the station among the charging vehicles, instead of providing full power to all of them, can achieve this [6], [7].”
BackgroundEffect of Penetration Levels for Vehicle-to-Grid Integration on a Power Distribution Network
Simarro-García, A., Villena‐Ruiz, R., Honrubia‐Escribano, A., & Gómez‐Lázaro, E. (2023) · Machines · doi:10.3390/machines11040416 · Cited by 9
Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks
Bostandoost, R., Sun, B., Joe‐Wong, C., et al. (2023) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3575813.3576878 · Cited by 8
“The work in [17] has the charging station’s charging capacity constraint.”
BackgroundA Design of Smart Charging Architecture for Battery Electric Vehicles
Riza, R., Margowadi, Y., Aji, P., et al. (2023) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope56780.2023.10408308 · Cited by 8
“An adaptive charging network was proposed and implemented in a more complex smart charging garage [8].”
MethodCoordinated Charging of Electric Vehicle Group Using Smart Meters
Li, J., Yu, H., Huo, Y., et al. (2023) · IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) · doi:10.1109/icpsasia58343.2023.10295023 · Cited by 7
“In [8], algorithms for the adaptive charging network (ACN) are proposed.”
MethodOnline Deadline-Aware EV Charging Considering State-of-Charge-Dependent Peak Charging Rate
Wang, X., Chen, Y., Du, L., et al. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3591027 · Cited by 6
“Unlike MPC-based methods [13], [16], which are prediction-dependent and suffer from performance sensitivity to prediction accuracy, Lyapunov optimization does not require any prediction and is robust to various random processes.”
Heinekamp, J. F., Mendy, R.-C., Smitmans, L., et al. (2024) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm60555.2024.10738054 · Cited by 6
Optimization of spatial infrastructure for EV charging
Paganini, F., Espindola, E., Marvid, D., et al. (2022) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc51059.2022.9993162 · Cited by 6
“The charge scheduling problem within a single EV parking/charging installation is studied in [7], trading off the competing objectives of quick service and limiting power consumption peaks.”
BackgroundA Brief Overview and Key Points in Electric Vehicle Charging Systems
Çolak, İ., Irmak, E., & Tepe, İ. F. (2023) · International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · doi:10.1109/summa60232.2023.10349473 · Cited by 6
Liberati, F., Atanasious, M. M. H., Santis, E. D., & Giorgio, A. D. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101963 · Cited by 5
An Efficient Greedy Algorithm for Real-World Large-Scale Electric Vehicle Charging
Hegele, M., Metzler, P., Beichter, S., Wiegel, F., & Hagenmeyer, V. (2023) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3575813.3597349 · Cited by 6
“Even though no found literature papers directly reference this norm, some express phase balance analogously as a hard constraint [5, 12, 25, 26].”
BackgroundMethodInfluentialProbabilistic Harmonic Impact Assessment of Multiple Electric Vehicle Fast Charging
Senol, M., Bayram, I. S., & Galloway, S. (2024) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec60657.2024.10598987 · Cited by 5
“It is noteworthy that, in the existing data-driven literature, initial and final SoC levels are often based on assumptions [20].”
BackgroundTowards balanced three-phase charging: Phase optimization in adaptive charging networks
Ye, Z., Li, T., & Low, S. H. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108322 · Cited by 5
Self-citationSimolin, T., Rauma, K., Rautiainen, A., et al. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120034 · Cited by 5
Deep Learning Tackles Temporal Predictions on Charging Loads of Electric Vehicles
Cadete, E., Alva, R., Zhang, A., et al. (2022) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce50734.2022.9947901 · Cited by 5
“First, all raw data are downloaded from the ACN website as JSON files.”
BackgroundMethodInfluentialSing, K., Mertiny, P., & Pruckner, M. (2022) · Winter Simulation Conference · doi:10.1109/wsc57314.2022.10015432 · Cited by 5
“For this purpose, the input data derived from the connectionTime, doneChargingTime and kWhDelivered fields as described by Lee et al. (2016) and Lee et al. (2021).”
MethodZhang, B., Ma, Y., Wang, F., et al. (2024) · Energy Informatics · doi:10.1186/s42162-024-00398-0 · Cited by 4
Efficient charging coordination of electric vehicles: A consensus tracking control approach
Abdelsalam, H. A., Attia, E. M., Arzani, A., & Mahajan, S. M. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101771 · Cited by 4
Optimum Scenarios of EV Charging Infrastructure: A Case Study for the Saudi Arabia Market
Azab, M. A. (2023) · Energies · doi:10.3390/en16135186 · Cited by 4
Yoon, S., & Dargaville, R. (2024) · Energies · doi:10.3390/en17102267 · Cited by 4
Design and validation of a power modulation system for residential demand-side management
Goldin, A., Buechler, E., Rajagopal, R., et al. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.110374 · Cited by 4
Bajpai, R. S., Prakash, S., Srivastava, A., & Singh, A. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3568189 · Cited by 4
“ANN-based control units, which are a segment of artificial intelligence (AI) and machine-learning (ML) technology, are being widely used, specifically for identifying issues [17], voltage sag categorization [18], short-term forecasting [19], minimal computational burden [20], etc.”
Detection and isolation of battery charging cyberattacks via Koopman operator
Ghosh, S. P., & Roy, T. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126695 · Cited by 4
Alshehhi, B., Karapetyan, A., Elbassioni, K., et al. (2023) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3575813.3595205 · Cited by 4
“Figure 5: Performance comparison of DclEVerNet, GreedyU, and PTAS∗ algorithms on the synthetic and ACN datasets. the polynomial might depend on 1n .”
BackgroundMethodInfluentialUdomparichatr, P., Vateekul, P., & Rojviboonchai, K. (2023) · International Electrical Engineering Congress (iEECON) · doi:10.1109/ieecon56657.2023.10126775 · Cited by 4
“The ASA framework is based on MPC and convex optimization.”
BackgroundMethodInfluentialSimolin, T., Rauma, K., Rautiainen, A., et al. (2023) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12885 · Cited by 4
Benz, P., & Pruckner, M. (2023) · Winter Simulation Conference · doi:10.1109/wsc60868.2023.10407436 · Cited by 4
“Theview on a larger scale is given by Lee et al. (2021).”
BackgroundMethodInfluentialDynamics and Optimization in Spatially Distributed Electrical Vehicle Charging
Paganini, F., & Ferragut, A. (2024) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2024.3487650 · Cited by 4
“…a single facility of this kind, in particular, the scheduling of charging opportunities taking into account EV deadlines and installation limitations [10], [25]; 2) integration of EV charging to the smart grid [14], [23]; 3) facility location problems, i.e., where to deploy EV charging [9], [15].”
Wireless E-Vehicle Charging Station with Automatic Billing System
Pawar, S., Pathan, S., Chavan, P., & Diware, R. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11254273 · Cited by 3
“[ 25].”
Zatsarnaja, J., Reiter, K., Mohnen, A., & Nordfjærn, T. (2025) · Ecological Economics · doi:10.1016/j.ecolecon.2025.108805 · Cited by 3
An AI-Powered Integrated Management Model for a Sustainable Electric Vehicle Charging Infrastructure
D’Ulizia, A., D’Andrea, A., Pirrone, M., & Su, D. (2026) · Sustainability · doi:10.3390/su18073257 · Cited by 3
Electric Vehicle Charge Scheduling Based on Circle-Inspired Optimization Algorithm
Mahato, D., Aharwal, V. K., & Sinha, A. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-99-1767-9_40 · Cited by 3
Narayanan, A., Nagarathinam, S., Misra, P., & Vasan, A. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3632410.3632421 · Cited by 3
“…heating system control; [35] for temperature control; [8, 43] for indoor air quality (IAQ) optimization; [31] for dynamic air temperature control; and [27] for chiller plant control, etc. EV charging management: Rule-based and MPC techniques were proposed in [5, 7, 18, 23, 26, 29, 46, 47, 50].”
MethodModeling and Simulation of Electric Vehicles Charging Services by a Time Colored Petri Net Framework
Mangini, A. M., Fanti, M. P., Silvestri, B., et al. (2025) · Energies · doi:10.3390/en18040867 · Cited by 3
Optimal Control Approach to Scheduling Power Supply Facilities: Theory and Heuristics
Rosset, F., Casagrande, D., Jafarpisheh, B., et al. (2022) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2022.3165019 · Cited by 3
“A similar approach is used in [19] and [20], which proposes an LP formulation for the VR case considering many different objectives.”
BackgroundMethodA Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases
Tucker, N., & Alizadeh, M. (2022) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm52983.2022.9961029 · Cited by 3
“Now we will discuss several utility functions that could be included by a charging facility operator (some of the following utility functions were adopted from [19], [20] which present a similar real-time smart charging algorithm and case studies; however, they make the assumption that user input for energy request am…”
Background
Metadata
- Authors
- Lee, Z. J., Lee, G., Lee, T., Jin, C., Lee, R., Low, Z., Chang, D., Ortega, C., & Low, S. H.
- Affiliations
- California Institute of Technology (US)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2021-09
- Volume
- 12
- Issue
- 5
- Pages
- 4339-4350
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W3112320897
- Semantic Scholar
- 5de6a0fef702431a52a7d1724499404a9ac66aee
- Cited by
- 174
- References
- 32
- Citations by year
- 2026: 24 · 2025: 43 · 2024: 41 · 2023: 37 · 2022: 23 · 2021: 6
- FWCI
- 7.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Transactions on Smart Grid (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation through the Graduate Research Fellowship Program (1745301)
- NSF AIR-TT (1602119)
- NSF Division of Electrical, Communications and Cyber Systems (1932611)
- NSF Division of Computing and Communication Foundations (1637598)
- Resnick Sustainability Institute Graduate Fellowship
- Caltech Rocket Fund
- Caltech CI2 Grant
- Emerging Technologies Coordinating Council of Utilities
- Well Fargo/NREL IN2
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- electric vehicle charging, charging scheduling, convex optimization, model predictive control, deadline scheduling, real-time monitoring and control
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