College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
China FAW Group Corporation, Changchun 130011, China.
Comput Intell Neurosci. 2023 May 29;2023:8288527. doi: 10.1155/2023/8288527. eCollection 2023.
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.
首次采用在线策略学习算法解决电池荷电状态 (SOC) 观测器的最优控制问题。基于二阶 (RC) 等效电路模型，研究了用于非线性动力锂电池系统的自适应神经网络 (NN) 最优控制设计。首先，利用 NN 逼近系统的未知不确定性，并设计一个时变增益非线性状态观测器，以解决电池的电阻电容电压和 SOC 无法测量的问题。然后，为了实现最优控制，设计了一种基于策略学习的在线算法，其中仅需要评价 NN，而去除了最优控制方法设计中广泛使用的动作 NN。最后，通过仿真验证了最优控制理论的有效性。
