The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index; none of the 25 sources its claims cite is checked for support yet.
Smart electric vehicle charging management for smart cities
Open accessAbstract
In recent years, attraction to alternative urban mobility paradigms such as electric vehicles (EVs) is increasing since EVs can significantly minimise fossil fuel dependency and reduce carbon emission in urban areas. Nonetheless, there are several barriers toward widespread adoption of EVs. Moreover, as EV penetration increases in urban areas, uncoordinated charging may cause power outage. Deployment of EV charging network can allow EVs to communicate with the service provider to coordinate charging activities. Taking into account, increased growth of EVs, number of charging facilities will be inadequate in urban areas, so efficient EV charging management is required for managing and allocating scarce charging station (CS) resources. In this study, the authors have designed and implemented a smart EV charging management system utilizing charging strategy that includes effective reservation management and efficient slot allocation of CSs. Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, their scheduling scheme shall furnish a set of optimal solutions. Viewing user discontent factor and average waiting time, they have evaluated performance of proposed strategy. The proposed charging strategy is effective than the existing one in terms of average waiting time.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper designs and implements SecCharge, a reservation-based smart EV charging management system that uses a multi-objective slot allocation (MOSAL) approach based on multi-objective simulated annealing to allocate scarce charging station slots. Evaluation shows the proposed MOSAL strategy is more effective than the existing CSAH approach in terms of average waiting time.
Key findings
- MOSAL outperforms the CSAH approach, with average waiting time about 20% lesser than CSAH when the number of EV requests is higher.
- Average user discontent factor increases as the number of EV charging requests increases; with 120 EVs, reservation options may not be satisfying and user discontent factor is relatively high. Scenario 2, with more high-power chargers, is much better than Scenario 1.
- Charger type and vehicle acceptance rate strongly affect charging time and discontent: for a 50% SoC increment, Nissan Leaf (6.6 kWh acceptance) takes about 3 h 22 min with either 7.3 kW or 11 kW chargers, while BMW i3 (11 kWh acceptance) takes about 3 h 11 min with 7.3 kW but about 2 h 8 min with 11 kW.
- With 3.6/3.7 kW maximum charging power, average user discontent factors for Chevrolet Bolt, Nissan Leaf, and BMW i3 are unacceptably high.
Methods
- The authors designed SecCharge, a centralised smart EV charging management system with reservation management and slot allocation, and formulated the charging assignment problem as an integer linear programming (ILP) problem minimising a composite cost of waiting time, estimated charging time, estimated charging cost, user discontent factor, and CS congestion impact.
- The MOSAL approach is based on multi-objective simulated annealing (MOSA), which seeks a Pareto set of optimal solutions; performance was evaluated via simulation using the Simulation of Urban Mobility tool, AC Level 2 chargers, and BEV specifications from Table 1, with Poisson-distributed EV request arrivals.
- Evaluation metrics were average waiting time and average user discontent factor, compared against the CSAH approach across cases with different numbers of charging points, charger power levels, and EV requests.
Limitations
- The paper does not include a dedicated limitations section. It states that the real-world EV charging network is still immature, most existing public charging facilities provide best-effort services, and only a few apps furnish real-time EVSE availability or allow reservation.
- The text notes that actual charging sessions may differ from reservation duration, and received energy may be less than requested due to busy operation hours.
- Evident from the text: the evaluation relies on simulation with specific assumptions, including Poisson arrivals, only AC Level 2 chargers, and particular CS/EV configurations, which may limit generalisability.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
15 Sources not found · 10 Own result · 9 Own interpretation · 2 Own method · 1 No basis given
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
“Fig. 6 shows that by varying number of EV charging requests, in both scenarios, the average user discontent factor increases.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.3 Case II:
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, for BMW i3, using the charger with 7.3 kW, the charging time will be about 3h 11 min, whereas, using the charger with 11 kW, the charging time will be about 2 h 8 min:”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plainstates a causeSingle case- Asserted: passes — The authors put it forward as true.
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A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, in Fig. 7c, average user discontent factors for all three EV models are unacceptably high.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With a large number of requests (i.e. 120 EVs), the reservation options may not be satisfying such that the user discontent factor shall be relatively high.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.3 Case II:
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“From Fig. 5, it can be seen that the MOSAL approach can have an average waiting time, about 20% lesser than the CSAH approach when the number of EV requests is higher.”
- Also stated in: § 8 Conclusion · plain
“Proposed charging strategy is effective than the existing charging strategy in terms of average waiting time.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.2 Case I:
What the sources say
- Not matchedcites [29]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainstates a causeSingle case- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 8 Conclusion · plain
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proposed charging strategy is effective than the existing one in terms of average waiting time.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > Abstract
plainstates a cause- 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.
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A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Fig. 7b, average user discontent factors for Chevrolet Bolt are relatively higher than other two EV models.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Fig. 7a, average user discontent factors for BWM i3 are significantly lower than the other two EV models.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, for BMW i3, using the charger with 7.3 kW, the charging time will be about 3h 11 min, whereas, using the charger with 11 kW, the charging time will be about 2 h 8 min:”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plainstates a causeSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Then, for Nissan Leaf, using either the charger with 7.3 kW or the charger with 11 kW, the charging time will be about 3 h 22 min.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, their scheduling scheme shall furnish a set of optimal solutions.”
- Also stated in: § IET Smart Cities · plain
“Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, our scheduling scheme shall furnish a set of optimal solutions.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > Abstract
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Also stated in: § IET Smart Cities · plain
In the paper
“Appropriate deployment of smart grid technology and EV-grid integration can allow EVs to communicate with the energy service provider (ESP) that, in turn, coordinates the charging activities [3].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 1 Introduction
What the sources say
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainstates a possible cause- Asserted: passes — The authors put it forward as true.
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In the paper
“A large number of public charging station (CS) networks may not come shortly in some urban areas due to many constraints such as financial, technological limitations etc. [4].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 1 Introduction
What the sources say
- Not matchedcites [4]
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hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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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
“Reservation mechanisms could allow EV users to book the EVSE before they even reach the charging facilities [17][18][19].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2 Motivation
What the sources say
- Not matchedcites [17]
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hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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- 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
“Furthermore, selection of the charger type may depend not only various factors such as estimated waiting time, estimated charging time and estimated charging cost but also on the dwell time at the CS as these factors shall be counted on while determining the user discontent factor.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2 Motivation
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A statement the paper makes itself — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In most of the above research works, they have discussed several smart charging strategies as well as reservation mechanisms for EVs; however, none has presented an integrated solution that considers multiple charging options, waiting time, charging time and CS congestion impact.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2.1 Recent works
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Indubitably, the charging QoE might be degraded if the improper reservation is made for recharging [20].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2 Motivation
What the sources say
- Not matchedcites [20]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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- 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
“Thus, provisioning reservation services in the EV charging portal would help to improve the charging quality of experience (QoE).”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2 Motivation
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 12
- It may state two separate claims as one. 12
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
“For instance, investing in fast-charging infrastructure may be improbable in the places with lower EV adoption rates because it is not economically viable.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 1 Introduction
hedgedstates a possible cause- 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.
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
“Constructing public EV charging infrastructure in smart cities would substantially encourage EV users to use EVs as well as promote commercial electric fleets, for instance, electric taxi and ridesharing.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 1 Introduction
What the sources say
- Not matchedcites [5]
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hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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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
“Using its web/mobile application, the system can not only alleviate challenges related to the allocation of scarce CS resources in busy urban area [12] but also assure convenience of coordinated charging strategies viewing user's perspective and emobility patterns [13].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > IET Smart Cities
What the sources say
- Not matchedcites [12]
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plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“Offering reservation services could minimise the anxiety of waiting time [16] and alleviate congestion at the CSs (i.e.recharging congestion) [4].”
- Also stated in: § 4 Reservation mechanism · plain
“By deploying such a reservation mechanism, the EV users can reserve recharging schedule ahead of time, so such a strategy can not only minimise waiting time and charging time but also alleviate congestion at the CSs (i.e.recharging congestion).”
- Also stated in: § 4 Reservation mechanism · plain
“By offering reservation service, the EV users can book recharging schedule ahead of time, so such a strategy can not only minimise waiting time but also alleviate congestion at the CSs (i.e.recharging congestion).”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2 Motivation
What the sources say
- Not matchedcites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [4]
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plainstates a cause- 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.
- Also stated in: § 4 Reservation mechanism · plain
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 due to using chargers with 3.7 kW maximum charging power.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
hedgedstates a possible cause- 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.
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
“Park-and-charge problems [32], parking spot problems [33], congestion avoidance in emobility [10] and other problems [34] have been solved effectively by using SA.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.1 SA algorithm
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [33]
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- Not matchedcites [10]
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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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
“From Table 1, it can be observed that the vehicle acceptance rate of Nissan Leaf is 6.6 kWh, whereas that of BMW i3 is 11 kWh.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Such MOSA can find a set of solutions called the Pareto set, as all the Pareto solutions are equivalently important since they are the global optimal.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > IET Smart Cities
What the sources say
- Not matchedcites [11]
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plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“The electric circuit [36], which is the largest public charging network for EVs in Quebec and Eastern Ontario, has 2354 CSs that include 290 fast-charge stations.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.1 Preliminaries:
What the sources say
- Not matchedcites [36]
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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. 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
“It can be seen that Scenario 2 is much better than Scenario 1 since the system can provide more high-power chargers.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.3 Case II:
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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 due to the fact that BMW i3 has higher vehicle acceptance rate.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
plainstates a cause- 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.
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
“To evaluate the performance of the MOSAL approach, we considered in experiments a realistic scenario using simulation of urban mobility simulation tool.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2 Performance evaluation
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“SA [31] is a well known probabilistic approach for optimisation with provable global convergence.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.1 SA algorithm
What the sources say
- Not matchedcites [31]
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.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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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
“Nonetheless, the number of available charging facilities could be inadequate, so an efficient smart EV charging management would be required for managing and allocating the limited EV charging facilities [7][8][9].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 1 Introduction
What the sources say
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Not matched
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
“In the experiments, we have considered only AC level 2 chargers as they are currently the most widely spread.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.1 Preliminaries:
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 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
“Furthermore, just a few works have realworld implementations but not considered prevailing EV standards such as open charge point protocol (OCPP).”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2.1 Recent works
plain- Asserted: passes — The authors put it forward as true.
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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
“For instance, if the dwell time is sufficiently long (i.e. during shopping at the mall), AC Level 2 charger with loweroutput power may be acceptable since the longer charging time may not have a significant impact on the user discontent factor if it is within the dwell time.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 2 Motivation
hedgedstates a possible cause- 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.
In the paper
“SA is a local search-based algorithm that has a mechanism to avoid the local optima for seeking a global optima.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.1 SA algorithm
What the sources say
- Not matchedcites [31]
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
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
“Thus, a higher EV adoption rate can be achieved if adequate such infrastructures are accessible [5,6].”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 1 Introduction
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.
plainstates a possible cause- 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
The paper relies on this without support.
The EV charging network or public EV infrastructure is still immature.
“In the real-world scenarios, the EV charging network or the public EV infrastructure is still immature.”
The paper relies on this without support.
Most existing charging facilities in urban areas provide best-effort services and may not provide actual EVSE status or allow reservation.
“Still, most of the existing charging facilities in the urban areas provide 'best-effort' services, that means, EV supply equipment (EVSEs) in public places such as parking lots, shopping malls may not provide the actual status of EVSEs, i.e. availability status or allow reservation of EVSEs.”
The paper relies on this without support.
Only a few EV mobile apps furnish real-time information on EVSEs such as availability status and allow reserving EVSEs before going to charging points.
“Even though several EV mobile apps have been popped up these days for getting various information on CSs that will provide user convenience [14,15], only a few of them furnish real-time information on EVSEs such as availability status and allow to reserve EVSEs before going to the charging points.”
The paper relies on this without support.
Range anxiety is one of the major issues in EV recharging.
“It can be observed that range anxiety is one of the major issues in EV recharging.”
The paper relies on this without support.
EV users are concerned with charging operations because charging time periods are still significantly longer, especially for AC level 2 chargers.
“EV users are concerned with charging operations since charging time periods are still significantly longer, especially for AC level 2 chargers.”
The paper relies on this without support.
Long charging times may cause considerable delays due to the EV charging process and waiting times at busy charging points.
“Possibly, long charging times may cause considerable delays, owing to not only the EV charging process but also the waiting times due to busy charging points.”
The paper relies on this without support.
With increasing numbers of EVs in urban areas, EV users may experience significant congestion at several charging stations.
“In the future, number of EVs will increase significantly in the urban areas, and then they may experience significant congestion at several CSs.”
The paper relies on this without support.
Waiting (queuing) time is one of the major aspects influencing EV users' satisfaction.
“Waiting (queuing) time is one of the major aspects of influencing EV users' satisfaction.”
The paper relies on this without support.
Estimated waiting time varies directly proportional to the number of EVs charging during peak hours.
“Customarily, the estimated waiting time varies directly proportional to the number of EVs charging during peak hours.”
The authors state this as an assumption.
The number of EVs is usually higher than the number of charging stations.
“It can be assumed that I > J since the number of EVs is usually higher than the number of CSs.”
The authors state this as an assumption.
A charging session of an EV is non-pre-emptive, meaning it cannot be interrupted and restarted later.
“It is assumed that charging session of an EV is non-pre-emptive that means it cannot be interrupted and restarted later.”
The paper relies on this without support.
Charging cannot start before the stated start time, and it must be interrupted within the specified deadline.
“Moreover, the charging cannot start before the stated start time, and it must be interrupted within the specified deadline.”
The paper relies on this without support.
During charging operations, the actual charging start time may be later than the reservation start time and/or the actual charging finish time may be earlier than the reservation finish time.
“It should be noted that during the charging operations, t i cs may be later than t i rs and/or t i cf may be earlier than t i rf .”
The paper relies on this without support.
The price per kWh may increase with higher recharge power.
“Furthermore, the price/kWh may increase with higher recharge power.”
The paper relies on this without support.
The selection of charger type is vital because output power defines the charging rate.
“The selection of charger type is quite vital as the output power shall define the charging rate.”
The paper relies on this without support.
Recharging EVs timely is a key factor to guarantee a certain degree of mobility of EV users.
“Recharging the EVs timely is a key factor to guarantee a certain degree of mobility of the EV users.”
The paper relies on this without support.
If these issues are not appropriately addressed, widespread adoption of EVs would likely be hindered.
“If not appropriately addressed these issues, widespread adoption of EVs would likely be hindered.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
“the vehicle acceptance rate of Nissan Leaf is 6.6 kWh, whereas that of BMW i3 is 11 kWh.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 8 Conclusion
“| | kWh | kW | (EPA | rating, kWh/ | | | | | rating) | 100 km | | | | | km | | | Nissan, Leaf, | 40 | 6.6 | 243 | 18.7 | | 2019 | | | | | | BMW, i3 | 42.2 | 11 | 246 | 18.5 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 5.3 Problem formulation
“Constraints ( 12) and ( 13) indicate that each EV can only be charged by one charging port at a time, and each charging port can be assigned to only one EV at a time.”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 5.3 Problem formulation
“ε i rec ≤ ε i req , ∀i ∈ V (12 ) ∑ i ∈ V x i j = 1, ∀ j ∈ P (13 ) ∑ j ∈ P x i j ≤ 1, ∀i ∈ V (14”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 8 Conclusion
“Figure: 7 Fig. 7 Fig. 7 Average user discontent factor with respect to τ/t dt for various EV models (i.e.Bolt, Leaf, i3) (a) For output power 11 W, (b) For output power 7.3 kW, (c) For output power 3.6 kW”
§ IET Smart Cities Special Issue: Smart Transport for Smart Cities Smart electric vehicle charging management for smart cities > 6.2.4 Case III:
“(a) for output power 11 kW (b) for output power 7.3 kW (c) for output power 3.6 kW”
Citations
Cites
Smart Charging Strategy for Electric Vehicle Charging Stations
Moghaddam, Z., Ahmad, I., Habibi, D., & Phung, Q. V. (2017) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2017.2753403 · Cited by 412
MOSA method: a tool for solving multiobjective combinatorial optimization problems
Ulungu, E. L., Teghem, J., Fortemps, P., et al. (1999) · Journal of Multi-Criteria Decision Analysis · doi:10.1002/(sici)1099-1360(199907)8:4 · Cited by 385
Optimal Charging Scheduling by Pricing for EV Charging Station With Dual Charging Modes
Zhang, Y., You, P., & Cai, L. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2876287 · Cited by 319
Mehta, R., Srinivasan, D., Khambadkone, A. M., Yang, J., & Trivedi, A. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2550559 · Cited by 271
Charging scheduling with minimal waiting in a network of electric vehicles and charging stations
Qin, H., & Zhang, W. (2011) · doi:10.1145/2030698.2030706 · Cited by 232 · cited 2×
“Offering reservation services could minimise the anxiety of waiting time [16] and alleviate congestion at the CSs (i.”
BackgroundA Smart Parking Lot Management System for Scheduling the Recharging of Electric Vehicles
Kuran, M. Ş., Viana, A. C., Iannone, L., Kofman, D., Mermoud, G., & Vasseur, J.-P. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2403287 · Cited by 232
Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis
Tian, Z., Jung, T., Wang, Y., Zhang, F., Tu, L., Xu, C., Tian, C., & Li, X. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2539201 · Cited by 231
A simulated annealing heuristic for the hybrid vehicle routing problem
Yu, V. F., Redi, A. A. N. P., Hidayat, Y. A., & Wibowo, O. J. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.12.027 · Cited by 227
An integrated planning framework for different types of PEV charging facilities in urban area
Zhang, H., Hu, Z., Xu, Z., & Song, Y. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2436069 · Cited by 209
A Survey on Modeling and Optimizing Multi-Objective Systems
Cho, J.-H., Wang, Y., Chen, I.-R., Chan, K., & Swami, A. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2698366 · Cited by 208
Cited by
Sorted by citations
A study on trends and developments in electric vehicle charging technologies
Hemavathi, S., & Shinisha, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105013 · Cited by 305
Hui, C. X., Dan, G., Alamri, S., & Toghraie, D. S. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104985 · Cited by 288
Pardo-Bosch, F., Pujadas, P., Morton, C., & Cervera, C. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102957 · Cited by 153
A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation
Teimoori, Z., & Yassine, A. (2022) · Sustainability · doi:10.3390/su142114100 · Cited by 72
Algafri, M., Alghazi, A., Almoghathawi, Y., et al. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123775 · Cited by 56
Metadata
- Authors
- Vaidya, B., & Mouftah, H. T.
- Affiliations
- University of Ottawa (CA)
- Corresponding author
- Vaidya, Binod
- Venue
- IET Smart Cities
- Published
- 2020-03
- Volume
- 2
- Issue
- 1
- Pages
- 4-13
- ISSN
- 2631-7680 (ISSN-L)
- OpenAlex
- W3007249796
- Cited by
- 65
- References
- 35
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 14 · 2023: 16 · 2022: 4 · 2021: 7 · 2020: 4
- FWCI
- 2.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IET Smart Cities (publishedVersion, cc-by-nc)
- Funders
- Government of Ontario
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Social Sciences › Transportation › Smart Parking Systems Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- electric vehicle charging, charging management, smart charging stations, charging reservation, charging scheduling, user waiting time, charging station congestion, smart cities, urban mobility
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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