Adaptive Optimal Control of Hybrid Electric Vehicle Power Battery via Policy Learning

Qinglin Zhu; Huanli Sun; Ziliang Zhao; Yixin Liu; Jun Zhao

doi:10.1155/2023/8288527

Adaptive Optimal Control of Hybrid Electric Vehicle Power Battery via Policy Learning

Comput Intell Neurosci. 2023 May 29:2023:8288527. doi: 10.1155/2023/8288527. eCollection 2023.

Authors

Qinglin Zhu¹, Huanli Sun², Ziliang Zhao¹, Yixin Liu², Jun Zhao³

Affiliations

¹ College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
² China FAW Group Corporation, Changchun 130011, China.
³ College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Abstract

An online policy learning algorithm is used to solve the optimal control problem of the power battery state of charge (SOC) observer for the first time. The design of adaptive neural network (NN) optimal control is studied for the nonlinear power battery system based on a second-order (RC) equivalent circuit model. First, the unknown uncertainties of the system are approximated by NN, and a time-varying gain nonlinear state observer is designed to address the problem that the resistance capacitance voltage and SOC of the battery cannot be measured. Then, to realize the optimal control, a policy learning-based online algorithm is designed, where only the critic NN is required and the actor NN widely used in most design of the optimal control methods is removed. Finally, the effectiveness of the optimal control theory is verified by simulation.

MeSH terms

Algorithms
Computer Simulation
Electric Power Supplies
Neural Networks, Computer*
Nonlinear Dynamics*