SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

Abstract

State of charge (SOC) estimation has always been a hot topic in the field of both power battery and new energy vehicle (electric vehicle (EV), plug-in electric vehicle (PHEV) and so on). In this work, aiming at the contradiction problem between the exact requirements of EKF (extended Kalman filter) algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period, a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed. The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery. Through the combination of data-based and model-based SOC estimation method, the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods, and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment (such as in an EV or PHEV). A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

摘要

电池荷电状态(SOC)估计一直是动力电池和新能源汽车(含电动汽车(EV)、插电式电动汽车 (PDEV)等)领域的研究热点。本文针对传统EKF(扩展卡尔曼滤波)算法对于动力电池模型精确性要求 与全生命周期内的动态性要求之间的矛盾问题,提出了一种基于数据驱动EKF 算法的SOC 估计方法。 该方法的创新之处在于:数据驱动的EKF 动力电池SOC 估计可以在动力电池内部参数未知的情况下, 利用可观测的端电压和电流数据,实现动力电池SOC 的在线准确估计。通过将基于数据的SOC 估算 方法与基于模型的SOC 估计算法有效结合,有效避免了传统基于数据的SOC 估计算法的累积误差问 题以及基于模型的SOC 估计算法对于电池模型精度的依赖性,使其更加适用于环境复杂多变 (例如 EV 和PHEV)的动力电池全生命周期SOC 估算。实验结果表明,本文所提出的基于数据驱动的EKF 动力电池SOC 估计算法具有更好的鲁棒性和实用性。