A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine

Abstract

Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance.

Keywords

• Multiform coupling
• time series forecasting
• Bayesian LSTM
• deep fusion predictor