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Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles

Author

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  • Fang Zhou

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Feng Xiao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Cheng Chang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Yulong Shao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Chuanxue Song

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

Abstract

This paper deals with the energy management strategy (EMS) for an on-board semi-active hybrid energy storage system (HESS) composed of a Li-ion battery (LiB) and ultracapacitor (UC). Considering both the nonlinearity of the semi-active structure and driving condition uncertainty, while ensuring HESS operation within constraints, an adaptive model predictive control (AMPC) method is adopted to design the EMS. Within AMPC, LiB Ah-throughput is minimized online to extend its life. The proposed AMPC determines the optimal control action by solving a quadratic programming (QP) problem at each control interval, in which the QP solver receives control-oriented model matrices and current states for calculation. The control-oriented model is constructed by linearizing HESS online to approximate the original nonlinear model. Besides, a time-varying Kalman filter (TVKF) is introduced as the estimator to improve the state estimation accuracy. At the same time, sampling time, prediction horizon and scaling factors of AMPC are determined through simulation. Compared with standard MPC, TVKF reduces the estimation error by 1~3 orders of magnitude, and AMPC reduces LiB Ah-throughput by 4.3% under Urban Dynamometer Driving Schedule (UDDS) driving cycle condition, indicating superior model adaptivity. Furthermore, LiB Ah-throughput of AMPC under various classical driving cycles differs from that of dynamic programming by an average of 6.5% and reduces by an average of 10.6% compared to rule-based strategy of LiB Ah-throughput, showing excellent adaptation to driving condition uncertainty.

Suggested Citation

  • Fang Zhou & Feng Xiao & Cheng Chang & Yulong Shao & Chuanxue Song, 2017. "Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1063-:d:105581
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    References listed on IDEAS

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    Cited by:

    1. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    2. Vishnu P. Sidharthan & Yashwant Kashyap & Panagiotis Kosmopoulos, 2023. "Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles," Energies, MDPI, vol. 16(3), pages 1-26, January.
    3. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    4. Bin Tang & Di Zhang & Haobin Jiang & Yinqiu Huang, 2020. "Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm," Energies, MDPI, vol. 13(2), pages 1-13, January.
    5. Mahammad A. Hannan & Mohammad M. Hoque & Pin J. Ker & Rawshan A. Begum & Azah Mohamed, 2017. "Charge Equalization Controller Algorithm for Series-Connected Lithium-Ion Battery Storage Systems: Modeling and Applications," Energies, MDPI, vol. 10(9), pages 1-20, September.
    6. Jorge Nájera & Pablo Moreno-Torres & Marcos Lafoz & Rosa M. De Castro & Jaime R. Arribas, 2017. "Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging," Energies, MDPI, vol. 10(11), pages 1-16, October.
    7. Shun-Chung Wang & Chun-Yu Liu & Yi-Hua Liu, 2018. "A Fast Equalizer with Adaptive Balancing Current Control," Energies, MDPI, vol. 11(5), pages 1-15, April.

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