• CN:11-2187/TH
  • ISSN:0577-6686

›› 2009, Vol. 45 ›› Issue (6): 95-101.

• 论文 • 上一篇    下一篇

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利用双卡尔曼滤波算法估计电动汽车用锂离子动力电池的内部状态

戴海峰;孙泽昌;魏学哲   

  1. 同济大学汽车学院
  • 发布日期:2009-06-15

Estimation of Internal States of Power Lithium-ion Batteries Used on Electric Vehicles by Dual Extended Kalman Filter

DAI Haifeng;SUN Zechang;WEI Xuezhe   

  1. School of Automotive Studies, Tongji University
  • Published:2009-06-15

摘要: 以电动汽车的研发为背景,建立用于电动汽车中作为辅助动力源的锂离子动力蓄电池的等效物理模型及其离散形式的状态空间方程,然后分别介绍如何基于卡尔曼滤波算法在线估计电池内部的荷电状态和寿命状态。在此基础上,介绍利用双卡尔曼滤波算法同时在线估计荷电状态和寿命状态的算法原理,并设计出相关的电池测试试验,利用在此试验过程中所采集的包括电流、电压等数据对电池的内部状态进行估计。对试验结果的分析表明,利用双卡尔曼滤波算法在线估计电池内部状态是有效的,并且估计精度也相对较高,可以较好地反映电池内部的真实状态。

关键词: 电动汽车, 锂离子动力电池, 双卡尔曼滤波, 状态估计

Abstract: For the development of electric vehicles, power lithium-ion battery is an option of assistant power source for such kind of vehicles. An equivalent physical model of the power lithium-ion battery pack is founded which is then described in a discrete state-space form. The principle of how to use Kalman filtering to estimate the internal states including state of charge and state of health independently is introduced, and based on this, how to estimate state of charge and State of health synchronously by using dual extended Kalman filtering is proposed. A corresponding experiment is designed in which the data such as current and voltage are acquired to test the algorithm, and the testing results show that internal state estimation of battery by the method of dual extended Kalman filtering is effective and results in a relatively hiyh estimation accuracy, thus well reflecting the actual internal state of the battery pack.

Key words: Dual extended Kalman filtering, Electric vehicles, Power lithium-ion battery, State estimation

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