摘要
电池的荷电状态(SOC)是锂离子电池最重要的状态参数之一,是电池充放电控制和续航里程估计的依据,但现有研究中基于扩展卡尔曼滤波的SOC估计方法没有解决观测方程局部线性化带来的误差问题。为此,提出了一种基于自适应两步滤波的电池SOC估计算法,实现对电池SOC的精确估计,并提高其对测量噪声的鲁棒性。实验结果表明该方法可在不同工况和不同电池老化条件下将电池SOC估计的平均绝对误差减小至2%以内。
The battery state-of-charge(SOC) was one of the most important state parameters of a lithium-ion battery, and it was the basis for battery charging/discharging control and driving mileage estimation. However, the researches on the SOC estimation method based on extended Kalman filter did not solve the error problem which caused by the linearization of the observation equation. Therefore, this paper proposed a battery SOC estimation algorithm based on adaptive two-step filter to achieve accurate estimation of battery SOC and improve its robustness to measurement noise. The experimental results show that the method can reduce the average absolute error of battery SOC estimation to less than 2% under different operating conditions and different battery aging conditions.
作者
蔡亦山
杨林
邓忠伟
邓昊
CAI Yi-shan;YANG Lin;DENG Zhong-wei;DENG Hao(School of Mechanical Engineering,Shanghuai Jiao Tong University,Shanghai 200240,China)
出处
《电源技术》
CAS
北大核心
2019年第6期1022-1026,共5页
Chinese Journal of Power Sources
基金
国家自然科学基金(51741707)
关键词
荷电状态
参数辨识
自适应两步滤波
状态估计
电动汽车
state-of-charge
parameter identification
adaptive two-step filter
state estimation
electric vehicle
