混合电动汽车锂离子电池状态融合估计策略

Novel fusion estimation strategy for state of charge and state of health of hybrid electric vehicle Li-ion batteries

锂电池荷电状态及健康状态是电池管理系统的核心参数。以三元锂电池为研究对象,利用二阶RC等效电路实现对电池性能表征,改进扩展卡尔曼滤波算法,提出一种新型双自适应卡尔曼滤波算法,实现三种工况下荷电状态与健康状态联合估算。以安时积分估算结果作为参考,提出的荷电状态估算方法较扩展卡尔曼滤波算法,精度有显著提高。在HPPC工况下,平均误差减少1.529%,最大误差减少2.162%;在BBDST工况下,平均误差减少0.228%,最大误差减少3.580%;在DST工况下,平均误差减少0.436%,最大误差减少5.997%。以遗忘因子最小二乘法估算结果作为参照,健康状态的估算结果有效模拟了实际情况,HPPC工况下偏差在4%以内,BBDST工况下偏差在3%以内,DST工况下偏差在6%以内,能有效追踪电池状态变化。

The state of charge and state of health of Li-ion batteries are the core parameters of the battery management system.The ternary lithium battery is taken as the research object,the second-order RC equivalent circuit model is used to characterize the battery performance,and the extended Kalman filter algorithm is improved,and a new dual adaptive Kalman filter algorithm is presented to realize the joint estimation of the state of charge and state of health under three operating conditions.The result of ampere-hour integral estimation is taken as a reference,the accuracy of the state of charge estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering algorithm.Under the HPPC condition,the average error is reduced by 1.529%,and the maximum error is reduced by 2.162%.Under the BBDST condition,the average error is reduced by 0.228%,and the maximum error is reduced by 3.580%.Under the DST condition,the average error is reduced by 0.436%,and the maximum error is reduced by 5.997%.The estimation result of the forgetting factor least square method was taken as a reference,the state of health estimated by dual adaptive Kalman filtering algorithm effectively simulated the actual situation,the deviation is less than 4%under the HPPC condition,less than 3%under the BBDST condition and less than 6%under the DST condition,and which could effectively track the state change of the battery.