Optimization of Lithium Battery Pole Piece Thickness Control System Based on GA-BP Neural Network

The electrode thickness control system of lithium battery has the characteristics of nonlinearity, uncertainty and time change. The traditional thickness control method cannot meet the user requirement for the thickness precision of lithium battery electrode. To solve this problem, a prediction model based on neural network for thickness control of polar plates is proposed in this paper. The BP neural network is introduced into the polar slice thickness control system. The topology and parameters of the BP neural network are determined according to the main factors. Finally, the MATLAB software is used to simulate the related data model and analyze the effectiveness of the lithium battery electrode thickness prediction thickness. In order to predict the error of predicting the thickness of lithium batteries by BP neural network, a prediction model of polar slice thickness control of BP neural network optimized by genetic algorithm is designed. Based on MATLAB simulation platform, the thickness of lithium battery plate is simulated. The predicted results are very close to the expected thickness, which can meet the user's requirements.