电池2026，Vol.56Issue(1)：46-52,7.DOI:10.19535/j.1001-1579.2026.01.007
基于云平台的深度学习电池参数识别与SOH估计
Deep learning battery parameter identification and SOH estimation based on cloud platform
摘要
Abstract
Accurate state of health(SOH)estimation of Li-ion battery is crucial for the safe and reliable operation of battery management system(BMS).Traditional moving window least squares methods suffer from insufficient accuracy and poor adaptability in complex dynamic environments.A deep learning-enhanced method for battery model parameter identification and SOH estimation based on a cloud computing platform is proposed.The method fully preserves the mathematical foundation of the second-order RC equivalent circuit model and integrates a deep learning architecture combining convolutional neural networks,long short-term memory(LSTM)networks and an attention mechanism to construct a cloud-based intelligently optimized parameter identification framework.The proposed method improves SOH prediction accuracy while maintaining the theoretical integrity of the moving window least squares algorithm,reducing the mean absolute percentage error(MAPE)from 1.15％with traditional methods to 0.31％.关键词
锂离子电池/云计算/深度学习/参数识别/健康状态(SOH)估计/电池管理系统(BMS)Key words
Li-ion battery/cloud computing/deep learning/parameter identification/state of health(SOH)estimation/battery management system(BMS)分类
信息技术与安全科学引用本文复制引用
张维平,王志翠,姬莉,李国强,赵文蕾..基于云平台的深度学习电池参数识别与SOH估计[J].电池,2026,56(1):46-52,7.基金项目
国家自然科学基金面上项目(62373320),河北省自然科学基金面上项目(A2025107006) （62373320）
