基于深度学习的电池健康状态监测与预测系统设计

Design of Battery Health Monitoring and Prediction System Based on Deep Learning

文章旨在设计一套能够实时监测锂离子电池健康状态并进行准确预测的系统。通过整合改进的完全自适应噪声集合经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)信号分解算法、支持向量回归(Support Vector Regression,SVR)算法以及长短期记忆(Long Short-Term Memory,LSTM)网络模型,构建了一个综合性的电池健康管理系统。通过对锂离子电池进行恒流恒压充电、恒流放电以及阻抗测量等,利用所获取的数据进行预处理、分解及模型训练。结果显示,所提出的系统能够有效预测电池的容量、健康状态及剩余使用时间,与实际数据符合度较高。该研究为电池健康管理领域的发展提供了有效参考,具有一定的理论和应用价值。

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.