同济大学学报（自然科学版）2024，Vol.52Issue(z1)：215-222,8.DOI:10.11908/j.issn.0253-374x.24737
锂离子电池老化估计和预测的深度学习混合模型
Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction
摘要
Abstract
The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.关键词
锂离子电池/健康状态/深度学习/弛豫过程Key words
lithium-ion battery/state of health/deep learning/relaxation process分类
信息技术与安全科学引用本文复制引用
项越,姜波,戴海峰..锂离子电池老化估计和预测的深度学习混合模型[J].同济大学学报（自然科学版）,2024,52(z1):215-222,8.基金项目
国家重点研发计划政府间国际科技创新合作专项(2022YFE0207900) （2022YFE0207900）
国家自然科学基金(52307248) （52307248）
