Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles
Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles
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DOI：
10.1016/j.apenergy.2011.08.005
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发表时间：
2012-01-01
期刊：
影响因子：
11.2
通讯作者：
Guo, Hongqiang
中科院分区：
文献类型：
--
作者：
He, Hongwen;Xiong, Rui;Guo, Hongqiang
电池内部参数和荷电状态（SoC）的准确估计是电池管理系统的关键问题之一，这在很大程度上取决于合适的模型和相应的高效、高精度算法。提出了一种基于模型的基于自适应扩展卡尔曼滤波（AEKF）算法的电动汽车LiFePO4电池在线估计方法。选用Thevenin等效电路模型对LiFePO4电池进行建模，并对其数学方程进行了一定的推导。此外，详细阐述了AEKF算法的实现，并将其用于LiFePO4电池模型的在线参数估计。为了说明在线参数估计的优点，在混合脉冲功率特性（HPPC）试验和城市测功机驾驶计划（UDDS）试验中，对在线估计和离线计算的终端电压进行了对比分析。在此基础上，提出了一种基于开路电压（OCV）在线估计结果的高效在线SoC估计方法。实验结果表明，基于OCV-SoC的在线SoC估计可以有效地将误差限制在0.041以下。(C) 2011 Elsevier Ltd.版权所有。
The accurate estimation of internal parameters and state-of-charge (SoC) of battery, which greatly depends on proper models and corresponding high-efficiency, high-accuracy algorithms, is one of the critical issues for the battery management system. A model-based online estimation method of a LiFePO4 battery is presented for application in electric vehicles (EVs) by using an adaptive extended Kalman filter (AEKF) algorithm. The Thevenin equivalent circuit model is selected to model the LiFePO4 battery and its mathematics equations are deduced to some extent. Additionally, an implementation of the AEKF algorithm is elaborated and employed for the online parameters' estimation of the LiFePO4 battery model. To illustrate advantages of the online parameters' estimation, a comparison analysis is performed on the terminal voltages between the online estimation and the offline calculation under the Hybrid pulse power characteristic (HPPC) test and the Urban Dynamometer Driving Schedule (UDDS) test. Furthermore, an efficient online SoC estimation approach based on the online estimation result of open-circuit voltage (OCV) is proposed. The experimental results show that the online SoC estimation based on OCV-SoC can efficiently limit the error below 0.041. (C) 2011 Elsevier Ltd. All rights reserved.