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Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle

Author

Listed:
  • Zhangyu Lu

    (Hunan Institute of Engineering, College of Computer and Communication, Xiangtan 411104, China)

  • Xizheng Zhang

    (Hunan Institute of Engineering, College of Computer and Communication, Xiangtan 411104, China)

Abstract

The underlying circuit control is a key problem of the hybrid energy-storage system (HESS) in electric vehicles (EV). In this paper, a composite non-linear control strategy (CNC) is proposed for the accurate tracking current/voltage of the fully-active HESS by combining the exact feedback linearization method and the sliding mode variable structure control technology. Firstly, by analyzing the circuit characteristics of HESS, the affine non-linear model of fully-active HESS is derived. Then, a rule-based energy management strategy (EMS) is designed to generate the reference current value. Finally, the HESS is linearized by the exact feedback linearization method, and the proposed CNC strategy is developed combined with sliding mode variable structure control technology to ensure fast response, high performance, and robustness. At the same time, the stability proof based on the Lyapunov method is given. Moreover, the performance of the CNC strategy is thoroughly investigated and compared with simulation studies with the traditional PI control and a modified sliding mode control, and its effectiveness under different driving conditions is fully verified.

Suggested Citation

  • Zhangyu Lu & Xizheng Zhang, 2022. "Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle," Energies, MDPI, vol. 15(4), pages 1-15, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1567-:d:754153
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    References listed on IDEAS

    as
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    2. Song, Ziyou & Hou, Jun & Hofmann, Heath & Li, Jianqiu & Ouyang, Minggao, 2017. "Sliding-mode and Lyapunov function-based control for battery/supercapacitor hybrid energy storage system used in electric vehicles," Energy, Elsevier, vol. 122(C), pages 601-612.
    3. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    4. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
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