观测模型误差不确定的锂电池SOC估计方法

建立的锂电池非线性系统中存在不确定的观测模型误差时,会影响滤波器估计的精度和稳定性,严重时还会导致估计结果发散。针对这一问题,基于变分贝叶斯自适应滤波方法,提出了一种鲁棒UKF算法。该算法构建虚拟观测噪声用来补偿观测模型误差,并采用逆Wishart分布对虚拟观测噪声协方差建模。在变分迭代过程中,实现对系统状态和虚拟观测噪声协方差的联合后验概率估计,使估计结果自适应地逼近到真实分布。利用无迹卡尔曼滤波对系统状态进行更新。结合锰酸钾锂电池非线性模型进行仿真实验表明,该算法估计锂电池荷电状态具有很好的精度、跟踪速度以及鲁棒性。

Research on SOC estimation method for lithium batteries with uncertain model errors

When the error of observation model is uncertain in the established lithium battery nonlinear system, the accuracy and stability of filter estimation will be affected, and the estimation results will diverge in serious cases. To solve this problem, a robust UKF algorithm is proposed based on the variational Bayesian adaptive filtering method. Firstly, the algorithm constructs a virtual observation noise to compensate the error of the observation model, and uses the inverse Wishart distribution to model the covariance of the virtual observation noise. Secondly, in the process of variational iteration, the joint posterior probability estimation of the covariance of system state and virtual observation noise is realized, which makes the estimation result approximate to the real distribution adaptively. Finally, the unscented Kalman filter is used to update the system state. The simulation results based on the non-linear model of lithium potassium manganate battery show that the algorithm has good accuracy, tracking speed and robustness in estimating the state of charge of lithium battery.