Adaptive neural network optimal control of hybrid electric vehicle power battery

被引：0
|
作者：
Li Y.-M. [1 ]
Pei X.-X. [1 ]
Yi S.-D. [2 ]
机构：
[1] College of Science, Liaoning University of Technology, Jinzhou
[2] Liaoning Aerospace Linghe Automobile Co.，Ltd., Chaoyang
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 09期
关键词：
adaptive neural network; adaptive optimal control; second-order resistor-capacitor（RC）equivalent model; state of charge estimation;
D O I：
10.13229/j.cnki.jdxbgxb20211422
中图分类号：
学科分类号：
摘要：
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. © 2022 Editorial Board of Jilin University. All rights reserved.
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页码：2063 / 2068
页数：5
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