重庆理工大学学报2024，Vol.38Issue(21)：18-26,9.DOI:10.3969/j.issn.1674-8425(z).2024.11.003
锂电池实时遗忘因子在线参数辨识与状态估计
Online parameter identification and state estimation of lithium batteries based on real-time forgetting factor
摘要
Abstract
The accuracy of State of Charge(SOC)is an important basis for energy management,range estimation,power system control,and other functions of electric vehicles.The accurate model parameters are the very foundations for correctly determining the SOC of power batteries.Traditional offline parameter identification uses fixed model parameters to describe the performance and response of batteries.However,under the influence of different discharge rates and durations,some internal parameters of the batteries experience changes accordingly.If fixed model parameters are employed,significant deviations may occur in predicting and estimating the battery state.We propose a self-adjusting forgetting factor recursive least squares method to identify the various parameters of the battery model online,and the obtained model parameters are imported into the extended Kalman filtering algorithm for real-time estimation of the battery's SOC.Through comparative analysis and verification,our method converges to within 1％of SOC estimation error under different operating conditions,demonstrating fairly good model parameter identification accuracy and robustness,and significantly improving SOC estimation accuracy.关键词
SOC估计/最小二乘法/扩展卡尔曼滤波/参数辨识Key words
SOC estimation/least squares method/extended Kalman filtering/parameter identification分类
交通工程引用本文复制引用
阚英哲,杨敏,孙华泽,谢云飞..锂电池实时遗忘因子在线参数辨识与状态估计[J].重庆理工大学学报,2024,38(21):18-26,9.基金项目
国家自然科学基金项目(52205052) （52205052）
重庆市教委科学技术研究项目(KJQN202201125) （KJQN202201125）
重庆理工大学科研启动基金项目(2021ZDZ015) （2021ZDZ015）
