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Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Author

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  • Angel Recalde

    (Faculty of Electrical and Computer Engineering, Escuela Superior Politecnica del Litoral, ESPOL, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil 090902, Ecuador)

  • Ricardo Cajo

    (Faculty of Electrical and Computer Engineering, Escuela Superior Politecnica del Litoral, ESPOL, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil 090902, Ecuador)

  • Washington Velasquez

    (Faculty of Electrical and Computer Engineering, Escuela Superior Politecnica del Litoral, ESPOL, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil 090902, Ecuador)

  • Manuel S. Alvarez-Alvarado

    (Faculty of Electrical and Computer Engineering, Escuela Superior Politecnica del Litoral, ESPOL, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil 090902, Ecuador)

Abstract

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

Suggested Citation

  • Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3059-:d:1419340
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    References listed on IDEAS

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    1. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, November.
    2. Omkar Parkar & Benjamin Snyder & Adibuzzaman Rahi & Sohel Anwar, 2023. "Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle," Energies, MDPI, vol. 16(13), pages 1-21, June.
    3. Okay, Kamil & Eray, Sermet & Eray, Aynur, 2022. "Development of prototype battery management system for PV system," Renewable Energy, Elsevier, vol. 181(C), pages 1294-1304.
    4. Philip K. Agyeman & Gangfeng Tan & Frimpong J. Alex & Jamshid F. Valiev & Prince Owusu-Ansah & Isaac O. Olayode & Mohammed A. Hassan, 2022. "Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines," Energies, MDPI, vol. 15(19), pages 1-23, September.
    5. Maroto Estrada, Pedro & de Lima, Daniela & Bauer, Peter H. & Mammetti, Marco & Bruno, Joan Carles, 2023. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach," Applied Energy, Elsevier, vol. 329(C).
    6. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
    7. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    8. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    9. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    10. Wang, Hanchen & Ye, Yiming & Zhang, Jiangfeng & Xu, Bin, 2023. "A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle," Energy, Elsevier, vol. 266(C).
    11. Prashant Kumar & Prince Kumar & Ananda Shankar Hati & Heung Soo Kim, 2022. "Deep Transfer Learning Framework for Bearing Fault Detection in Motors," Mathematics, MDPI, vol. 10(24), pages 1-14, December.
    12. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    13. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    14. Muhammad Rizalul Wahid & Bentang Arief Budiman & Endra Joelianto & Muhammad Aziz, 2021. "A Review on Drive Train Technologies for Passenger Electric Vehicles," Energies, MDPI, vol. 14(20), pages 1-24, October.
    15. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
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