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Effects of Battery Model on the Accuracy of Battery SOC Estimation Using Extended Kalman Filter under Practical Vehicle Conditions Including Parasitic Current Leakage and Diffusion Of Voltage
International Journal of Automotive Technology ( IF 1.6 ) Pub Date : 2021-09-26 , DOI: 10.1007/s12239-021-0116-1
Hyunjong Oh 1 , Jeehwan Jeon 1 , Sungjin Park 1
Affiliation  

Accurate estimation of battery State of Charge (SOC) is a crucial factor for the safe and efficient usage of the batteries in hybrid electric vehicles. The combined method of Coulomb counting and Open circuit Voltage (OCV) is already under practical usage for the estimation of battery SOC, but the methods have significant error when there is parasitic current leakage (dark current) or short rest period. Thus, Extended Kalman Filter (EKF) is one of the battery SOC estimation methods used to overcome such drawbacks. And, most importantly, due to structural dependency of EKF upon battery model, the battery model used for the EKF contributes significantly to the accuracy of EKF. Thus, in this paper, 3 types of battery Equivalent Circuit Models (ECMs) including second order RC model, first order RC model, and R model are compared under practical vehicle driving conditions. To simulate the vehicle driving condition, a micro Hybrid Electric Vehicle (micro-HEV) is modeled and simulation is conducted under NEDC condition.



中文翻译:

在包括寄生电流泄漏和电压扩散在内的实际车辆条件下,使用扩展卡尔曼滤波器的电池模型对电池 SOC 估计精度的影响

准确估计电池荷电状态 (SOC) 是混合动力汽车中安全有效使用电池的关键因素。库仑计数和开路电压(OCV)相结合的方法已经在实际用于电池SOC的估计中,但是当存在寄生电流泄漏(暗电流)或短休息时间时,该方法存在显着误差。因此,扩展卡尔曼滤波器 (EKF) 是用于克服这些缺点的电池 SOC 估计方法之一。而且,最重要的是,由于 EKF 对电池模型的结构依赖性,用于 EKF 的电池模型对 EKF 的准确性有显着贡献。因此,在本文中,3 种类型的电池等效电路模型 (ECM) 包括二阶 RC 模型、一阶 RC 模型、和R模型在实际车辆行驶条件下进行比较。为模拟车辆行驶工况,对微型混合动力电动汽车(micro-HEV)进行建模,并在NEDC工况下进行模拟。

更新日期:2021-09-27
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