中国电机工程学报2025，Vol.45Issue(24)：9632-9643,中插13,13.DOI:10.13334/j.0258-8013.pcsee.241735
基于AEKF-KELM融合模型的锂电池内部温度在线估计方法
On-line Estimation Method for Internal Temperature of Lithium Battery Based on AEKF-KELM Fusion Model
摘要
Abstract
Thermal runaway is one of the critical causes of safety issues in lithium battery.To accurately estimate the thermal state of lithium battery,this paper proposes an algorithm consisted of adaptive extended Kalman filter(AEKF)and kernel extreme learning machine(KELM)to estimate the internal temperature of lithium battery.The equivalent circuit of lithium battery temperature estimation based on Bernardi heat generation model is established,its parameters are identified by genetic algorithm(GA),and the internal temperature of lithium battery is then estimated by AEKF algorithm.With the terminal voltage,working current,surface temperature estimated value and internal temperature estimated value of lithium battery as input and internal temperature estimated error as output,KELM is used to establish temperature estimation error compensation model to compensate the temperature estimated result.In order to verify the effectiveness of the method,experiments in constant current charge-discharge and dynamic stress test(DST)conditions are carried out at different ambient temperatures.The experimental results show that the estimation errors produced by this method are all less than 0.34℃in each test condition,and estimation accuracy and robustness are significantly improved compared with other methods.关键词
锂电池/内部温度估计/自适应扩展卡尔曼滤波/核极限学习机/误差补偿Key words
lithium battery/internal temperature estimation/adaptive extended Kalman filter/kernel extreme learning machine/error compensation分类
信息技术与安全科学引用本文复制引用
刘世林,孙波,孙超,张宇,程凡永..基于AEKF-KELM融合模型的锂电池内部温度在线估计方法[J].中国电机工程学报,2025,45(24):9632-9643,中插13,13.基金项目
安徽省重点研究与开发计划项目(202004a05020014) （202004a05020014）
安徽未来技术研究院企业合作项目(2023qyhz32).Key Research and Development Program of Anhui Province(202004a05020014) （2023qyhz32）
Cooperation Project of Anhui Future Technology Research Institute and Enterprise(2023qyhz32). （2023qyhz32）
