The DOI matches its Crossref record; 123 of the 125 references it cites were found in a registry or an index.
Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions
Open accessAbstract
In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“ML methods have also been applied to battery energy management (for example charge estimation [330] or optimization in hybrid EVs [15]), and to detect faults and lateral misalignment in wireless charging of EVs [780].”
From Tackling climate change with machine learning (2023) · cites this paper as [15]
In the citing paper
“Hybrid EVs, which are more efficient than combustion engines alone, rely on power management methods that can be improved with ML [15].”
From Tackling climate change with machine learning (2023) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Model predictive control: Theory and practice—A survey
García, C. E., Prett, D. M., & Morari, M. (1989) · Automatica · doi:10.1016/0005-1098(89)90002-2 · Cited by 5,392
A review of energy sources and energy management system in electric vehicles
Tie, S. F., & Tan, C. W. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.11.077 · Cited by 1,518
Stability of extremum seeking feedback for general nonlinear dynamic systems
Krstić, M., & Wang, H.-H. (2000) · Automatica · doi:10.1016/s0005-1098(99)00183-1 · Cited by 1,394
Optimal Control of Parallel Hybrid Electric Vehicles
Sciarretta, A., Back, M., & Guzzella, L. (2004) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2004.824312 · Cited by 882
Salmasi, F. R. (2007) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.899933 · Cited by 868
Peng, J., He, H., & Xiong, R. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.12.031 · Cited by 747
Emadi, A., Rajashekara, K., Williamson, S. S., & Lukic, S. M. (2005) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2005.847445 · Cited by 735
Flagged first, then by citations
2 of the 125 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Ehsani, M., Gao, Y., and Emadi, A. (2009). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, CRC Press.
- (2007). Optimal Control with Engineering Applications, Springer.
- Onori, S., and Serrao, L. (2011, January 6–7). On Adaptive-ECMS strategies for hybrid electric vehicles. Proceedings of the International Scientific Conference on Hybrid and Electric Vehicles, Rueil-Malmaison, France.
Cited by
Sorted by citations
Items 101–133 of 133
Yao, Z., & Yoon, H. (2023) · SAE International Journal of Electrified Vehicles · doi:10.4271/14-12-03-0016 · Cited by 2
Bose, B., Tayal, V. K., & Moulik, B. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7664-2_5 · Cited by 2
Aubeck, F., Birmes, G., & Pischinger, S. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917068 · Cited by 2
Pedda, G., & Raju, P. S. (2021) · IEEE Bombay Section Signature Conference (IBSSC) · doi:10.1109/ibssc53889.2021.9673405 · Cited by 2
Aubeck, F., Peter, T., Birmes, G., Savelsberg, R., & Pischinger, S. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814104 · Cited by 2
Lane Change Driver Assistance System for Online Operation Optimization of Connected Vehicles
Aubeck, F., Oetermann, T., Birmes, G., & Pischinger, S. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917535 · Cited by 2
Steindl, C., & Hofmann, P. (2023) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2023-01-0494 · Cited by 1
A Study on Comparison of Energy Management Strategies for a Series Hybrid Tracked Vehicle
Göçer, İ., & Başlamışlı, S. Ç. (2025) · International Conference on Control and Automation · doi:10.1109/icca65672.2025.11129754 · Cited by 1
Brudeli, G., Rebouças, G. F. D. S., Vestgaard, B., & Fagerås, H. (2026) · Proceedings · doi:10.1007/978-3-658-49720-0_2 · Cited by 1
On Machine Learning-Based Control for Energy Management in Construction Machines
Raduenz, H. (2022) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789179294168 · Cited by 1
An Efficient Battery Management System for Electric Vehicles
Shobana, S., Amudha, A., GOWTHAM, S., Nagarajan, N., Shanmugam, R., Thirumalai Nambi Rajan, M., & Arunmozhi, R. V. (2023) · Annual International Conference on Emerging Research Areas and International Conference on Microelectronics, Communications and Renewable Energy · doi:10.1109/aicera/icis59538.2023.10420215 · Cited by 1
Ali, A. M., Moulik, B., Beganović, N., & Söffker, D. (2019) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2019.v11i1.783 · Cited by 1
Self-citationModel Predictive Power Allocation for Hybrid Battery Balancing Systems
de Castro, R. P., & Araújo, R. E. (2019) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc46532.2019.8952557 · Cited by 1
Modeling, simulation and control of hybrid power systems for vessels.
Peixoto, C. D. S. (2022) · doi:10.11606/d.3.2022.tde-30112022-081832 · Cited by 1
Ali, A. M. (2019) · DuEPublico (University of Duisburg-Essen) · doi:10.17185/duepublico/70563
Self-citationKrishna, P. S. P., Jayalakshmi, N. S., & Sudarshan, A. (2020) · IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) · doi:10.1109/discover50404.2020.9278073
Supervisory Control Systems for Heavy-duty Electric Vehicles
Arora, S., Abkenar, A. T., Jayasinghe, S. G., & Tammi, K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818126-3.00009-9
VERMA, M., & Srivastava, S. (2021) · International Journal of New Technology and Research · doi:10.31871/ijntr.7.4.13
Peng, H., Chen, Z., Deng, K., & Hameyer, K. (2021) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc53923.2021.9699211
Al-Flehawee, M., & Al-Mayyahi, A. (2022) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.18.1.11
Abulifa, A. A., Soh, A. C., Hassan, M. K., Ahmad, R. M. K. R., & Radzi, M. A. M. (2022) · International Journal of Science and Technology Research Archive · doi:10.53771/ijstra.2022.3.2.0116
Battery Management System to Find Accurate Timing
Boopathy, S., Bharathi Priya, M., Suruthiksha, L., & Nandhini, G. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.50487
Mild Hybrid Multi-Energy Systems: Waste Energy Recovery by Bottom ORC System
Capata, R. (2023) · International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS) · doi:10.52202/069564-0244
Guo, Q., Lian, R., Ma, K., Hou, L., & Wu, Y. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394355
Robustness Evaluation of Energy Management Strategies for Hydrogen-Based Railway Vehicles
Olmos, J., Saez-de-Ibarra, A., Gaztañaga, H., Lopez, D., Nieva, T., & Aizpuru, I. (2023) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc60535.2023.10403272
Development and Test Validation of Energy Management System for Marine Hybrid Power System
Sun, X., Gao, Y., & Yao, C. (2024) · Journal of The Institution of Engineers (India) Series C · doi:10.1007/s40032-024-01103-6
Battery management system to find accurate timing using machine learning
Geethamani, R., Kumar, K. K. K., Bhalan, P. K., Prabu, M. K., Kowsik, R., & Madhumita, A. K. (2025) · AIP conference proceedings · doi:10.1063/5.0263763
Mission profiles and energy management strategies for electric trucks with fuel cell range extenders
Cruz, I. W. S. L., Buck, L., Wyszynski, T., Schramm, M., & Frerichs, L. (2025) · Transportation Engineering · doi:10.1016/j.treng.2025.100356
Dual-layer Integrated MPC and SMC-based Energy Management Strategies for Hybrid Electric Vehicles
Lavudya, S., Sen, S., Basetti, V., & Shiva, C. K. (2025) · IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST) · doi:10.1109/giest66547.2025.11387392
Mulik, R. V., Porpatham, E., & Arumugam, S. K. (2026) · IEEE Access · doi:10.1109/access.2026.3696059
Development of a Methodology for Optimizing Fuel Cell Control in Fuel Cell Electric Heavy Trucks
Kampker, A., Heimes, H., Hausmann, J., Bayerlein, M., & Kemmer, S. (2025) · International Conference on Production Technologies and Systems for E-Mobility (EPTS) · doi:10.1109/epts67931.2025.11547577
Li, Q., Chen, X., & Li, Z. (2026) · Asia Energy and Electrical Engineering Symposium (AEEES) · doi:10.1109/aeees69423.2026.11556812
Song, D., Chen, L., Zeng, X., & Ni, L. (2026) · SAE International journal of commercial vehicles · doi:10.4271/02-19-04-0026
Metadata
- Authors
- Ali, A., & Söffker, D.
- Affiliations
- University of Duisburg-Essen (DE)
- Corresponding author
- Ali, Ahmed
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2018-02-25
- Volume
- 11
- Issue
- 3
- Pages
- 476
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2789725222
- Cited by
- 134
- References
- 125
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 7 · 2023: 23 · 2022: 24 · 2021: 22 · 2020: 22 · 2019: 17 · 2018: 3
- FWCI
- 8.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- hybrid electric vehicles, power management, real-time control, powertrain efficiency, rule-based control, optimization-based control, artificial intelligence, adaptive control, fuel consumption reduction, emission reduction
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.
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
