| 摘 要: 针对非线性系统锂电池剩余电量(State of Charge, SOC)估计常用算法——安时积分法初值精度要求高、累计误差大的问题，提出了基于扩展卡尔曼滤波(Extended Kalman Filter, EKF)算法的SOC估计方法。通过建立合理的电池模型，利用MATLAB仿真在恒流工况下证明：安时积分法的平均误差为2.7%，EKF算法在无初始误差和存在初始误差两种工况下降低平均误差分别为0.97%和1.3%。最后通过ADVISOR 2002软件仿真验证了锂电池工作在两种动态电流工况下，基于EKF的SOC估计平均误差分别为1.06%、1.13%，符合SOC估计精度要求。 |
| 关键词: EKF算法；SOC估计；ADVISOR；非线性系统 |
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中图分类号: TP39
文献标识码: A
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| Research on the Estimation of State of Charge of Lithium Battery based on Extended Kalman Filter Algorithm |
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LI Tianfeng, WANG Fuzhou, XU Xiaofan
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(School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)
18301926973@163.com; 2766554390@qq.com; xu.xiaofan@163.com
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| Abstract: As a traditional algorithm for estimating the state of charge (SOC) of lithium battery in nonlinear systems, ampere-hour integral method requires high initial value accuracy and produces large cumulative error. In view of these problems, this paper proposes an SOC estimation method based on Extended Kalman Filter (EKF). By establishing a reasonable battery model, MATLAB (Matrix & Laboratory) simulation under constant current conditions shows that the average error of ampere-hour integral method is 2.7%, and the average error of EKF algorithm is reduced to 0.97% without initial error and 1.3% with initial error. Finally, through the simulation of ADVISOR 2002 software, it is verified that under two dynamic current conditions, the average error of SOC estimation based on EKF is 1.06% and 1.13% respectively, which meets the requirements of SOC estimation accuracy. |
| Keywords: EKF algorithm; SOC estimation; ADVISOR; nonlinear system |
