摘要
锂离子电池健康状态(SOH)的准确估计是电池管理系统(BMS)的关键技术。该文提出一种基于数据驱动与经验模型组合的在线SOH预测方法。通过电池容量增量分析(ICA),找出与SOH相关性较高的两个电压升片段下所耗时间作为电池外部健康特征(HF),并使用高斯过程回归(GPR)的方法建立电池老化的数据驱动模型。利用数据驱动模型对电池工作初期的SOH进行预测,并使用预测值拟合指数经验模型。之后,电池各循环下的SOH用指数经验模型来预测,并且每隔固定循环次数使用观测器对指数模型参数进行一次修正,以保证电池SOH预测的准确性。实验结果表明,该文所提的方法可以在减轻电池监测设备负担的前提下将预测精度保持在较高水平。
The accurate estimation of the state of health(SOH)of lithium-ion batteries is a key technology in battery management system(BMS).This paper presents an online SOH prediction method combined with data-driven and empirical models.Through incremental capacity analysis(ICA),the time spent under two voltage rise segments with high correlation with SOH was identified as the external health factor(HF)of the battery,and a data-driven model of battery aging was established using gaussian process regression(GPR).The data-driven model was used to predict the SOH of the battery in the initial working period,and the exponential empirical model was fitted with the predicted values.Then the SOH of the subsequent cycle was predicted by the exponential model.And the parameters of the exponential model were modified by the observer every fixed number of cycles to ensure the accuracy of SOH prediction.The experimental results show that the proposed method can keep the prediction accuracy at a high level while reducing the burden of battery monitoring equipment.
作者
王萍
弓清瑞
张吉昂
程泽
Wang Ping;Gong Qingrui;Zhang Ji’ang;Cheng Ze(School of Electrical and Information Engineering Tianjin University,Tianjin 300072 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2021年第24期5201-5212,共12页
Transactions of China Electrotechnical Society
基金
国家自然科学基金资助项目(61873180)。
关键词
锂离子电池
健康状态
指数模型
高斯过程回归
观测器
Lithium-ion battery
state of health
exponential model
Gaussian process regression
observer
