Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (9): 2063-2068.doi: 10.13229/j.cnki.jdxbgxb20211422

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Adaptive neural network optimal control of hybrid electric vehicle power battery

Yong-ming LI1(),Xiao-xuan PEI1,Shu-dong YI2   

  1. 1.College of Science，Liaoning University of Technology，Jinzhou 121001，China
    2.Liaoning Aerospace Linghe Automobile Co. ，Ltd. ，Chaoyang 122500，China
  • Received:2021-12-22 Online:2022-09-01 Published:2022-09-13

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Abstract:

Adaptive neural network （NN） output feedback optimal control design problem and stability analysis were studied for nonlinear lithium battery systems based on the second-order resistor-capacitor （RC） equivalent circuit model. Firstly， NN was used to approximate the uncertain nonlinear dynamic of the controlled system， and a time-varying gain nonlinear observer was designed to solve the unmeasurable problem of battery resistance and capacitance voltage and state of charge （SOC）. Under the framework of Actor-Critic network， an observer-based adaptive optimal NN control algorithm was designed. According to the Lyapunov stability theorem， it is proved that all signals of the closed-loop system are semi-global uniformly ultimately bounded （SGUUB）. Finally， the effectiveness of the proposed optimal control theory was verified by simulation.

Key words: adaptive optimal control, second-order resistor-capacitor （RC） equivalent model, state of charge estimation, adaptive neural network

CLC Number: 

  • O232

Fig.1

Schematic diagram of second orderRC equivalent circuit battery"

Fig.2

Trajectories of x1 and x?1"

Fig.3

Trajectory of controller u(t)"

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