考虑锂电池多时间尺度效应的参数辨识与SOC估计方法

Parameter identification and SOC estimation methods considering multi-timescale effect lithium batteries

准确的荷电状态(state of charge,SOC)估计对新能源汽车电池管理系统的安全运行具有重要意义,而可靠的参数辨识是SOC估计的关键。考虑锂电池多时间尺度效应,提出了自适应遗忘因子递推最小二乘法(adaptive forgetting factor recursive least squares,AFFRLS)与自适应扩展卡尔曼滤波(adaptive extended Kalman filtering,AEKF)联合的参数辨识算法。首先,根据多时间尺度效应将锂电池双极化模型(dual-polarization model,DP)划分为快动态与慢动态。其次,设计AFFRLS与AEKF联合的参数辨识算法,分别辨识快、慢动态参数。最后,结合参数辨识数据利用H无穷算法估计SOC。结果表明,所提算法在不同工况与温度下具备良好的精度与鲁棒性。模型最大端电压均方根误差仅为3.329 mV,SOC均方根误差低于1%。

Accurate state of charge(SOC)estimation is of great significance for the safe operation of battery management systems in new energy vehicles,and reliable parameter identification is the key to SOC estimation.In this paper,a joint parameter identification algorithm based on adaptive forgetting factor recursive least squares(AFFRLS)and adaptive extended Kalman filter(AEKF)is proposed considering the multi-timescale effect of a lithium battery.First,a lithium battery dual-polarization model(DP model)is classified into fast dynamic and slow dynamic based on multi-timescale effects.Secondly,the AFFRLS and AEKF based-joint parameter identification algorithm is designed to identify the parameters of fast dynamic and slow dynamic,respectively.Finally,the SOC is estimated using an H-infinity algorithm with the parameter identification data.The results show that the proposed algorithm has good accuracy and robustness in different driving cycles and at different temperatures.The maximum root-mean-square error of the terminal voltage is only 3.329 mV,and the SOC root mean square error is less than 1%.