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首页> 外文期刊>Energies >Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm
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Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

机译：基于最小二乘法和卡尔曼滤波算法的电动汽车动力电池充电状态联合估计

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摘要

An estimation of the power battery state of charge ( SOC ) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC . Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.
机译：动力电池充电状态（SOC）的估计与能量管理，电池循环寿命和电动汽车的使用成本有关。在电动汽车中使用锂离子动力电池时，在工作条件和环境等随机因素的影响下，SOC表现出非常强的时间依赖性非线性。因此，对电动汽车动力电池SOC的估算进行研究具有重要的理论意义和应用价值。在本文中，根据放电过程中动力电池端电压的动态响应，首先将二阶RC电路用作动力电池的等效模型。随后，在此模型的基础上，将具有遗忘因子的最小二乘法（LS）和自适应无味卡尔曼滤波器（AUKF）算法一起用于动力电池SOC的估计。仿真实验表明，本文提出的联合估计算法比单个AUKF算法具有更高的精度和初始值误差的收敛性。

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