| 基于自适应卡尔曼滤波的锂离子电池SOC估计 |
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| 引用本文： | 张頔,马彦,柏庆文.基于自适应卡尔曼滤波的锂离子电池SOC估计[J].汽车技术,2011(8). |
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| 作者姓名： | 张頔 马彦 柏庆文 |
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| 作者单位： | 吉林大学; |
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| 基金项目： | 国家工信部“新能源汽车电子控制系统研发与产业化”项目,项目编号:A08-BK-2010; 教育部高校吉林大学基本科研业务费科学前沿与交叉学科创新项目,项目编号:450060323306 |
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| 摘 要： | 采用自适应卡尔曼滤波方法,基于锂离子动力电池的等效电路模型,在未知干扰噪声环境下,在线估计电动汽车锂离子动力电池荷电状态(SOC)。仿真结果表明,采用自适应卡尔曼滤波方法估计的SOC误差小于2.4%,有效降低了电动汽车行驶时电池管理系统所受到的未知干扰噪声影响,SOC估计精度高于扩展卡尔曼方法,且具有较好的鲁棒性。
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| 关 键 词： | 锂离子电池 荷电状态 自适应卡尔曼滤波 |
Estimation of State of Charge of Li-ion Battery Based on Adaptive Kalman Filtering |
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| Zhang Di,Ma Yan,Bai Qingwen.Estimation of State of Charge of Li-ion Battery Based on Adaptive Kalman Filtering[J].Automobile Technology,2011(8). |
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| Authors: | Zhang Di Ma Yan Bai Qingwen |
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| Abstract: | On-line estimation of state of charge(SOC) of electric vehicle Li-ion battery is made under unknown interfering noise based on the equivalent circuit model of the Li-ion battery,with adaptive Kalman filtering method.Simulation results show that adaptive Kalman filtering method can estimate SOC with error less than 2.4%,the method effectively minimizes effect of unknown interfering noise on battery management system during operation of electric vehicle,SOC estimate accuracy of this method is higher than exte... |
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| Keywords: | Li-ion batteries State of Charge(SOC) Adaptive Kalman Filtering |
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