首页|基于FFRLS和GSAPF的锂电池剩余电量估计

基于FFRLS和GSAPF的锂电池剩余电量估计

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新能源汽车的稳定运行依赖于锂电池的剩余电量估计.对此,提出一种基于遗忘因子递推最小二乘法(FFRLS)和黄金正弦粒子滤波算法(GSAPF)的锂电池剩余电量估计方法.这一方法在使用递推最小二乘法对锂电池进行参数辨识的基础上,引入遗忘因子,通过遗忘因子重新分配旧数据和新数据的权重,从而减少递推最小二乘法的数据饱和现象,并且联合黄金正弦粒子滤波算法对锂电池进行剩余电量估计.试验结果表明,这一方法具有较高的精度.
The stable operation of new energy vehicle depends on the SOC estimation of lithium battery.In this regard,an SOC estimation method of lithium battery based on FFRLS and GSAPF was proposed.On the basis of using RLS to identify the parameter of lithium battery,the forgetting factor was introduced,and the weights of old data and new data were redistributed through the forgetting factor,so as to reduce the data saturation phenomenon of RLS,and the SOC of lithium battery was estimated by combining GSAPF.Experimental result shows that this method has high precision.

Lithium BatterySOCEstimationFFRLSGSAPF

王郑、卢昊、程静、蒋兆国、刘建晟

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江苏理工学院汽车与交通工程学院 江苏常州 213001

锂电池 剩余电量 估计 遗忘因子递推最小二乘法 黄金正弦粒子滤波算法

2024

装备机械
上海电气(集团)总公司

装备机械

影响因子:0.158
ISSN:1662-0555
年,卷(期):2024.(3)