Design of Battery Health Monitoring and Prediction System Based on Deep Learning
The purpose of this paper is to design a system that can monitor the health status of lithium-ion batteries in real time and make accurate predictions.By integrating the improved signal decomposition algorithm of improved complete ensemble empirical mode decomposition with adaptive noise,Support Vector Regression(SVR)algorithm and Long Short-Term Memory(LSTM)network model,a comprehensive battery health management system is constructed.Through constant current and constant voltage charging,constant current discharging and impedance measurement,the obtained data are used for pretreatment,decomposition and model training.The results show that the proposed system can effectively predict the capacity,health status and remaining service time of the battery,and it is highly consistent with the actual data.This study provides an effective reference for the development of battery health management,and has certain theoretical and application value.
battery health managementlithium-ion batteryreal-time monitoringImproved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)
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