安时-卡尔曼交叉运行的电池荷电状态估算策略及其微控制器在环验证

为了提高电池管理系统(BMS)的性能,研究了电池荷电状态(SOC)的估算方法,并根据SOC估算算法精度和系统实时性要求,提出了安时(AH)积分算法-卡尔曼(Kalman)算法(AH-Kalman)交叉运行的SOC估算策略。该策略用开路电压(OCV)法确定SOC初值,以实时性较强的AH积分法为主,采用间歇运行的Kalman滤波法修正安时计量法积分误差。建立了系统仿真模型,验证了卡尔曼滤波算法对安时积分法积累误差的修正作用。将控制算法生成C代码下载到目标控制器,搭建微控制器在环测试验证(PILS)平台,进行了与传统卡尔曼滤波算法的复杂度对比分析。结果表明,所提出AHKalman交叉运行的SOC估算策略在保证了SOC估算精度的同时也具有较好的实时性,便于实际应用。

A stratey for batteries' state-of-charge estimation using AH -Kalman cross operation and its processor-in-the-loop simulation

To improve the performace of battery management system ( BMS) , the estimation of batteries ' state of charge ( SOC) was studied , and according to the requirement of estimation accuracy and system timeliness , a strategy for SOC estimation using the cross operation of the Ampere hour ( AH) integration algorithm and the Kalman algo-rithm, called the SOC estimation strategy using AH-Kalman cross operation for short , was proposed .The strategy uses the open-circuit voltage ( OCV) method to determine the initial SOC value , and uses the real-time AH integral method ( playing the main role ) and the intermittent operating Kalman method to correct the accumulated error of the AH integral method .The system simulation model was established to verify the correction effect of the Kalman filter algorithm on the accumulative error .The C-code of the control algorithm was generated and downloaded to the target controller , and the processor-in-the-loop simulation ( PILS ) was built , and the algorithm complexity was compared with the traditional Kalman filter .The results show that the proposed SOC estimation strategy can ensure the accuracy of SOC estimation and has the good real-time performance , which is convenient for practical applica-tion.