储能科学与技术2025，Vol.14Issue(4)：1585-1595,11.DOI:10.19799/j.cnki.2095-4239.2024.0964
基于短时随机充电数据和优化卷积神经网络的锂电池健康状态估计
State of health estimation for lithium batteries based on short-term random charging data and optimized convolutional neural network
摘要
Abstract
In practical applications,complete charge-discharge curves are often unavailable.To address this issue,this study proposes a lithium-ion battery state of health(SOH)estimation method using short-term stochastic charging data and an optimized convolutional neural network(CNN).The goal is to develop an efficient technique that accommodates the random and disordered nature of charging processes in real-world scenarios.The proposed method segments the original charging voltage time-series data of lithium batteries to generate randomized charging data.A shallow CNN comprising four convolutional layers is then constructed to adaptively extract aging-related features from the data.In addition,the dung beetle optimization algorithm is employed to optimize the model parameters,resulting in a multistage model.The experimental results demonstrate that the proposed method can accurately estimate the SOH of Li-ion batteries during stochastic charging,even when using only five consecutive seconds(100 data points)of raw voltage time-series data.The practicality and accuracy of the proposed model were validated under various charging conditions and rates.The results demonstrate that the model continues to exhibit a low prediction error.The average absolute error of the SOH estimation is less than 2.07%under constant current-voltage charging mode and less than 1.22%under multistage constant current charging mode.In the model comparison,the proposed CNN-based method achieved an average mean absolute error of 1.17%,outperforming integrated prediction models,such as the CNN-Long Short-Term Memory Network and CNN-Gated Recurrent Unit,in terms of estimation accuracy and stability.In addition,the randomized voltage segment used in the CNN accounted for 88.9%of the total charging time,is higher than the other two prediction models.The experimental results demonstrate the effectiveness of the proposed method in addressing the stochastic nature of battery charging data,demonstrating excellent accuracy and high adaptability in the context of charging rates,charging modes,and random charging voltage data over short periods.This results of this study provide a crucial technical reference for future advancement in battery health monitoring and battery management systems.关键词
健康状态/随机充电/数据分割/卷积神经网络/锂离子电池Key words
state of health/random charging/data segmentation/convolutional neural network/lithium-ion battery分类
信息技术与安全科学引用本文复制引用
申江卫,折亦鑫,舒星,刘永刚,魏福星,夏雪磊,陈峥..基于短时随机充电数据和优化卷积神经网络的锂电池健康状态估计[J].储能科学与技术,2025,14(4):1585-1595,11.基金项目
国家自然科学基金(52162051,52267022),云南省基础研究计划项目(202301AT070423),昆明理工大学自然科学研究基金项目(KK23202202021),汽车零部件先进制造技术教育部重点实验室开放课题基金(2023KLMT02),重庆市自然科学基金(CSTB2024NSCQ-MSX0389). （52162051,52267022）
