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Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Author

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  • Yuying Wang

    (Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Xiaohong Jiao

    (Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Zitao Sun

    (Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Ping Li

    (Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Abstract

This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.

Suggested Citation

  • Yuying Wang & Xiaohong Jiao & Zitao Sun & Ping Li, 2017. "Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 10(11), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1894-:d:119267
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    References listed on IDEAS

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    1. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    2. Xiaohong Jiao & Tielong Shen, 2014. "SDP Policy Iteration-Based Energy Management Strategy Using Traffic Information for Commuter Hybrid Electric Vehicles," Energies, MDPI, vol. 7(7), pages 1-28, July.
    3. Hanho Son & Hyunsoo Kim, 2016. "Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses," Energies, MDPI, vol. 9(6), pages 1-18, May.
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    Citations

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    Cited by:

    1. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    2. Wang, Weida & Guo, Xinghua & Yang, Chao & Zhang, Yuanbo & Zhao, Yulong & Huang, Denggao & Xiang, Changle, 2022. "A multi-objective optimization energy management strategy for power split HEV based on velocity prediction," Energy, Elsevier, vol. 238(PA).
    3. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    4. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    5. Aimin Du & Yaoyi Chen & Dongxu Zhang & Yeyang Han, 2021. "Multi-Objective Energy Management Strategy Based on PSO Optimization for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-18, April.
    6. Zhang, Shuo & Hu, Xiaosong & Xie, Shaobo & Song, Ziyou & Hu, Lin & Hou, Cong, 2019. "Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 256(C).
    7. Wang, Feng & Zhang, Jian & Xu, Xing & Cai, Yingfeng & Zhou, Zhiguang & Sun, Xiaoqiang, 2019. "A comprehensive dynamic efficiency-enhanced energy management strategy for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 247(C), pages 657-669.
    8. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.
    9. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    10. Santiago Martinez-Boggio & Javier Monsalve-Serrano & Antonio García & Pedro Curto-Risso, 2023. "High Degree of Electrification in Heavy-Duty Vehicles," Energies, MDPI, vol. 16(8), pages 1-20, April.
    11. Ali Saleh Aziz & Mohammad Faridun Naim Tajuddin & Mohd Rafi Adzman & Makbul A. M. Ramli & Saad Mekhilef, 2019. "Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy," Sustainability, MDPI, vol. 11(3), pages 1-26, January.
    12. Qi Wu & Shouheng Sun, 2022. "Energy and Environmental Impact of the Promotion of Battery Electric Vehicles in the Context of Banning Gasoline Vehicle Sales," Energies, MDPI, vol. 15(22), pages 1-18, November.

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