自适应扩展卡尔曼滤波在不同噪声情况下的SOC估计

Using Adaptive Extended Kalman Filter to Estimate SOC under Different Noise Scenarios

针对电池长期使用过程中采样误差会发生变化的情况,提出使用自适应在线估算误差的方法,最大程度地发挥卡尔曼滤波的在线SOC修正能力。首先以二阶等效电路模型为基础,对三元材料(NCM)锂离子电池进行多个SOC点的放电静置实验。之后使用MATLAB对实验数据进行参数拟合,得出电池的全SOC工况(0%~100%)的伏安特性模型。实验后所得数据表明此模型的精度较高。在此基础之上,使用扩展卡尔曼滤波(EKF)对SOC估计进行了优化。针对电动车行驶过程中传感器的误差不可知,并且会发生突变的情况,提出一种基于信息相关的自适应扩展卡尔曼滤波(AEKF)来实时估计过程中的测量噪声。仿真结果表明,采用AEKF方法对动态电池模型进行SOC估计可以适应多种噪声,可有效降低电动汽车行驶时电池管理系统所受到的未知噪声干扰的影响,在采样误差未知且可变的情况下,SOC估计精度高于EKF方法,且具有较好的鲁棒性。

During long-time battery usage,sampling error can vary on amplitude,an adaptive method is proposed for online sampling error estimating to maximize SOC correction ability of EKF method,based on equivalent circuit model,NCM lithium-ion battery is discharged and rest under multiple SOC points.Then MATLAB is used to estimate parameters of the battery model,and a dynamic battery model is achieved under whole SOC working condition(0%~100%SOC).Experiment data has proven the model has high accuracy.Base on previous work,extended Kalman filter(EKF)is used to do real-time SOC estimation.As sensors noise of electric vehicle is unknown and can be varied during driving,an innovation-based adaptive extended Kalman filter(AEKF)method is proposed to estimate the measurement noise in real time.Simulation results have shown using AEKF method can properly estimate SOC of a dynamic battery modelunder different types of noise,the method have great potential to reduce noise interference to battery management system(BMS)during driving,AEKF has a better accuracy and provide robustness when battery sampling error is known and varying.