Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (2): 277-287.doi: 10.11959/j.issn.2096-6652.202230

• Papers and Reports • Previous Articles    

TD3-based energy management strategy for hybrid energy storage system of electric vehicle

Jiacheng LIU1, Xiangwen ZHANG1,2   

  1. 1 School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
    2 Guangxi Key Laboratory of Automatic Detection Technology and Instruments, Guilin 541004, China
  • Online:2022-06-01 Published:2022-06-01
  • Supported by:
    The National Natural Science Foundation of China(51465100);The Natural Science Foundation of Guangxi(2018GXNSFAA281282);The Key Laboratory of Automatic Detection Technology and Instrumentation Foundation of Guangxi(YQ17110);Graduate Education Innovation Program of Guilin University of Electronic Technology(2021YCXS120)

Abstract:

Combining batteries and super capacitors into a composite power system (CPS) with an effective energy management strategy can significantly improve the energy utilization, and increase the service life of the energy storage system.To minimize the energy loss of the system, an energy management strategy based on the twin delayed deep deterministic policy gradient (TD3) algorithm was designed.Compared with the deep deterministic policy gradient (DDPG) algorithm, TD3 algorithm solved the problem of overestimation of Q value and less energy loss.A MATLAB/Simulink simulation model based on the TD3 algorithm was built, and tested with the electric vehicle driving equation and the equivalent circuit model of CPS.The outcomes indicate that the proposed energy management strategy can effectively reduce the impact of high current on the battery, and compared with DDPG algorithm, the energy utilization efficiency is improved by 1.36%, the peak of output current of the battery is reduced by 14.68%, the temperature rise of the battery is reduced by 3.52%, the total energy consumption of the system is reduced by 2.17%.

Key words: electric vehicle, composite power system, energy management, deep reinforcement learning

CLC Number: 

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