基于无迹卡尔曼滤波估算电池SOC

为了实现在线估计汽车动力电池的荷电状态（SOC），提出了结合神经网络的无迹卡尔曼滤波算法。以Thevenin电路为等效电路模型，建立了状态空间表达式，采用最小二乘算法对模型参数进行辨识。在此基础上，利用神经网络算法拟合电池的荷电状态与模型各个参数之间的函数关系，经过多次实验，确定了神经网络算法的收敛曲线，此方法比传统的曲线拟合精度高。介绍了扩展卡尔曼滤波和无迹卡尔曼滤波的原理，并设计了等效电路模型验证实验、电池的SOC测试实验和算法的收敛性实验。实验结果表明，在不同的工况环境下，该方法估计SOC具有可在线估算、估算精度高和环境适应度高等优点，最大误差小于4%。最后验证了结合神经网络的无迹卡尔曼滤波的算法具有较好的收敛性和鲁棒性，可以有效解决初值估算不准确和累计误差的问题。

Battery SOC estimation based on unscented Kalman filtering

In order to estimate the State-Of-Charge (SOC) of automobile power lithium-ion battery online, an Unscented Kalman Filtering (UKF) algorithm was proposed combined with neural network. First of all, Thevenin circuit was treated as an equivalent circuit, the state space representation of the battery model was established and the least square method was applied to identify the parameters of model. Then on this basis, the neural network algorithm was expected to fit the functional relationships between SOC of battery and model parameters respectively. After many experiments, the convergence curve of the neural network algorithm was determined. The proposed method was more accurate than the traditional curve fitting. In addition, the Extended Kalman Filtering (EKF) principle and the UKF principle were introduced separately and some tests were designed including the validation experiment of battery equivalent circuit model, the test experiment of SOC and the convergence experiment of the algorithms. The experimental results show that, the proposed method which can be used for SOC estimation online has higher estimation precision and stronger environmental adaptability than simple extended Kalman filtering algorithm under different conditions, its maximum error is less than 4%. Finally, the proposed algorithm combining UKF and neural network has better convergence and robustness, which can be used to solve the problems of inaccurate estimation of initial value and cumulative error effectively.