【摘要】 锂离子电池荷电状态(SOC)的准确估计对新能源汽车的能量管理和续驶里程计算尤为重要。就一段估计区间而言,尽管自适应5阶容积卡尔曼滤波(AHCKF)的总体精度通常比自适应容积卡尔曼滤波(ACKF)的更高,但在估计过程中二者的精度优劣是不断变化的。为了进一步提高SOC估计的精度,提出了一种基于不同阶次容积卡尔曼滤波融合的SOC估计算法。为评估融合算法的表现,本文采用该算法与NASA提供的电池随机充放电使用工况数据进行SOC估计。结果显示,所提出算法能够较大程度地减小SOC估计的误差。相对ACKF与AHCKF,平均绝对误差分别减少了26.39%和11.67%,均方根误差分别减少了20.21%和6.25%。更多还原

【Abstract】 The accurate estimation of the state of charge( SOC) of lithium-ion batteries is essential for the energy management and remaining mileage calculation of new-energy vehicles. Although the overall accuracy of the adaptive five-degree cubature Kalman filter( AHCKF) is usually higher than that of the adaptive cubature Kalman filter( ACKF),the accuracy difference between the two is continuously changing during the estimation process. To further improve the accuracy of SOC estimation,we proposed an SOC estimation algorithm based on the fusion of different degree cubature Kalman filters. The randomized battery usage data set provided by NASA was applied to evaluate the performance of the fusion algorithm. The results showed that the fusion algorithm could significantly reduce the error of SOC estimation: compared with ACKF and AHCKF,the mean absolute error was reduced by 26. 39% and 11. 67%,and the root mean square error was reduced by20. 21% and 6. 25%,respectively.更多还原