摘要
本文中提出了一种基于电池表面温度和增量容量的健康状态(SOH)估计方法,分析了恒流充电过程中的温度变化曲线,从温度变化曲线中提取了3个几何特征作为健康因子,并与增量容量曲线的峰值结合作为反向传播神经网络的输入来建立模型估算SOH。试验结果验证了该方法的有效性,SOH平均估计误差仅在2%以下。
In this paper,a state of health(SOH)estimation method based on the surface temperature and incremental capacity of battery is proposed.The differential temperature curves during constant charging are analyzed,from which three geometric features are extracted as health factors,and combined with the peak value of incremental capacity curve as the input of BP neural network to establish a model for SOH estimation.The results of the test verify the effectiveness of the method,and the average error of SOH estimation is less than 2%.
作者
林名强
吴登高
郑耿峰
武骥
Lin Mingqiang;Wu Denggao;Zheng Gengfeng;Wu Ji(Collage of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108;Quanzhou Equipment Manufacturing Research Institute,Haixi Research Institute,Chinese Academy of Sciences,Jinjiang 362216;Fujian Special Equipment Inspection and Research Institute,Fuzhou 350008;Department of Vehicle Engineering,Hefei University of Technology,Hefei 230009;Anhui Intelligent Vehicle Engineering Laboratory,Hefei 230009)
出处
《汽车工程》
EI
CSCD
北大核心
2021年第9期1285-1290,1284,共7页
Automotive Engineering
基金
国家自然科学基金(61903114,61803138)
工信部智能制造综合标准化项目(GXSP20181001)
泉州市科技计划项目(2020C010R)资助。
关键词
锂离子电池
健康状态
DT曲线
神经网络
lithium-ion battery
state of health
differential temperature curse
neural network
