摘要
卡尔曼滤波法在估计动力电池的剩余容量(SOC)时,由于系统噪声的不确定,可能导致算法不收敛,而且算法的估计性能受模型精度的影响,笔者采用自适应卡尔曼滤波法来动态地估计电动汽车用磷酸铁锂动力电池的SOC。首先对电池模型进行了研究,建立了适用于SOC估计的电池模型,然后设计了相应的电池充放电实验检测到模型的参数,并进行了验证,最后将自适应卡尔曼滤波法应用到该模型,在未知干扰噪声环境下,在线估计电池的SOC。仿真结果表明:自适应卡尔曼滤波法能够实时修正微小的模型误差带来的SOC估计误差,估计精度高于卡尔曼滤波法,且自适应卡尔曼滤波法对初值误差具有修正作用。实车循环行驶实验表明算法适用于磷酸铁锂动力电池的SOC估计。
The Kalman filter algorithm can be used to estimate the state of charge （SOC） of power batteries, however, it easily causes divergence due to uncertain of system noise and its estimation performance is affected by model. An adaptive Katman filter algorithm is adopted to dynamically estimate SOC of lithium iron phosphate batteries for application in electric vehicles. At first, an equivalent circuit model, appropriate for SOC estimation is built after studying battery models. Then some charging and discharging experiments are carried out for parameter identification and the results are verified. At last, the adaptive Kalman filter algorithm is used on this model for on line SOC estimation under unknown interfering noise. Simulation results show that adaptive Kalman filter method can correct SOC estimation error caused by tiny model error online, and the estimate accuracy is higher than Kalman filter method. Adaptive Kalman filter algorithm can also correct the initial error. Full-cycle test in electric vehicles proves that the algorithm is appropriate for SOC estimation of lithium iron phosphate battery.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第1期68-74,共7页
Journal of Chongqing University
基金
中央高校基本科研业务费资助项目(CDJXS11151156)
