基于多时间尺度锂电池在线参数辨识及 SOC 和 SOH 估计

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。

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

The state of charge SOC and state of health SOH of a battery are important indicators of battery enduranceand 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 doubleextended Kalman filter was proposed. The multi-timescale DEKF algorithm based on the second-order RC equivalentcircuit model was used for the online parameter identification and SOC estimation of the 18 650 ternary lithium battery withthe slow-varying characteristics of the battery parameters and the fast-varying characteristics of the battery state. Throughthe test verification of the Federal Urban Driving Program FUDS the average absolute errors of the SOC estimation ofthe 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 referencevalue was only 0. 007 72 Ah. The experimental results show that the proposed time-scale DEKF algorithm has betterrobustness and SOC estimation accuracy and can respond to the SOH variation trend in real time.