基于多时间尺度锂电池在线参数辨识及SOC和SOH估计

On-line Parameter Identification and SOC and SOH Estimation of Lithium Battery Based on Multi-time Scale

电池的荷电状态和健康状态是衡量电池续航和寿命的重要指标,为解决电池参数的时变性问题,提高电池SOC(State of Charge)估算精度,减少硬件计算量,提出一种多时间尺度在线参数辨识双扩展卡尔曼滤波联合算法。以18650三元锂电池为研究对象,采用基于二阶RC等效电路模型的多时间尺度DEKF算法,针对电池参数的慢变特性和状态的快变特性进行双时间尺度在线参数辨识和SOC估算;通过联邦城市驾驶计划(FUDS)测试验证,得出多时间尺度DEKF算法和传统离线辨识EKF算法对SOC估计的平均绝对误差分别为0.97%和2.46%,均方根误差为1.19%和2.69%,容量估计值对参考值最大误差仅为0.007 72 Ah;实验结果表明:所提出的多时间尺度DEKF算法,具有更好的鲁棒性和SOC估算精度并能实时反应SOH变化趋势。

The state of charge(SOC)and state of health(SOH)of a battery are important indicators of battery endurance and lifetime.In order to solve the problem of time-varying battery parameters,improve the accuracy of SOC estimation,and reduce the hardware computation,a joint multi-timescale online parameter identification algorithm with a double-extended Kalman filter was proposed.The multi-timescale DEKF algorithm based on the second-order RC equivalent circuit model was used for the online parameter identification and SOC estimation of the 18650 ternary lithium battery with the slow-varying characteristics of the battery parameters and the fast-varying characteristics of the battery state.Through the test verification of the Federal Urban Driving Program(FUDS),the average absolute errors of the SOC estimation of the multi-time scale DEKF algorithm and the traditional offline identification EKF algorithm were 0.97%and 2.46%,respectively,the rms errors were 1.19%and 2.69%,and the maximum error of the capacity estimation to the reference value was only 0.00772 Ah.The experimental results show that the proposed time-scale DEKF algorithm has better robustness and SOC estimation accuracy and can respond to the SOH variation trend in real time.