基于SQKF的锂离子电池剩余寿命预测

Square-root quadrature Kalman filtering for remaining useful life prediction in lithium-ion battery

针对锂离子电池剩余寿命(remaining useful life, RUL)难以精准预测的问题,建立单指数经验容量衰退模型,提出能够有效解决电池非线性问题的平方根求积分卡尔曼滤波(square-root quadrature kalman filtering, SQKF)算法。现有的最优估计方法中,求积分卡尔曼滤波(quadrature kalman filtering, QKF)是一种高精度采样算法。研究发现,QKF的估计误差易引起非对称、非正定协方差的传播,影响算法稳定性。在QKF算法上进行平方根扩展,并对单变量求积节点进行多维扩展,将SQKF算法应用于电池容量跟踪估计;另外,从理论上证明SQKF的稳定性。使用NASA公开数据集对算法进行仿真验证,并与现有的扩展卡尔曼滤波、无迹滤波、QKF算法对比。结果表明,在一定条件下,SQKF的RUL预测误差在6%以内,数值精度以及数值稳定性有很大提高,并且研究发现SQKF受锂离子电池个体差异性的影响较小,文中方法在锂离子电池RUL预测的实际应用方面具有参考价值。

In order to improve the prediction accuracy of Remaining Useful Life(RUL)of lithium-ion battery, a single-exponential empirical capacity degradation model is established, and a Square-Root Quadrature Kalman Filtering(SQKF)algorithm is proposed to solve the nonlinear estimation problem of battery.Among the existing optimal estimation methods, Quadrature Kalman Filtering(QKF)is a high-precision sampling algorithm.According to the researches, the estimation error of QKF tends of trigger the propagation of asymmetric and non-positive covariance, which affects the stability of the algorithm.In this paper, the square root extension is carried out on QKF,the multi-dimensional extension of the univariate integral point is accomplished, and SQKF is applied to battery capacity tracking estimation.Meanwhile, the stability of the approach is theoretically proved.The algorithm is simulated and verified using NASA’s public dataset, and compared with Extended Kalman Filtering, Unscented Filtering, and QKF.The results show that under certain conditions, the prediction error of SQKF on RUL is within 6%,both the numerical accuracy and stability are greatly improved, and SQKF is less affected by the individual differences of lithium-ion batteries.SQKF has reference value in the practical application of RUL prediction for lithium-ion battery.