| 锂离子电池参数辨识与SOC估算研究 |
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| 引用本文： | 朱浩,刘云峰,赵策.锂离子电池参数辨识与SOC估算研究[J].湖南大学学报(自然科学版),2014,41(3):37-42. |
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| 作者姓名： | 朱浩 刘云峰 赵策 |
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| 作者单位： | （湖南大学 汽车车身先进设计制造国家重点实验室，湖南 长沙410082） |
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| 基金项目： | 湖南省教育厅优秀青年基金资助项目(08B042);湖南省新型工业化专项(2012GK4009) |
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| 摘 要： | 电池SOC的估算精度是影响电动汽车性能的重要因素之一.针对传统的卡尔曼滤波方法在滤波时,需要已知系统噪声统计特性这一问题,本文在采用RC等效电路模型,运用多元线性回归方法辨识得到电池模型参数后,提出了采用模糊自适应卡尔曼滤波算法来估算电池SOC.城市道路循环工况仿真对比结果表明,该算法相比传统卡尔曼滤波方法具有更高精度,且能够将误差保持在2%以内,较好地提高了SOC估算精度.
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| 关 键 词： | 模糊逻辑 卡尔曼滤波 多元线性回归 参数辨识 锂离子电池 荷电状态 |
Parameter Identification and SOC Estimation of Lithium Ion Battery |
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| ZHU Hao,LIU Yun-feng,ZHAO Ce.Parameter Identification and SOC Estimation of Lithium Ion Battery[J].Journal of Hunan University(Naturnal Science),2014,41(3):37-42. |
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| Authors: | ZHU Hao LIU Yun-feng ZHAO Ce |
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| Institution: | (State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body ,Hunan Univ, Changsha, Hunan410082,China) |
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| Abstract: | Battery SOC estimation accuracy is one of the important factors influencing the performance of electric vehicles. Considering that the traditional Kalman filter calls for the undersdanding of the statistical properties of system noise, a fuzzy adaptive Kalman filtering algorithm was presented, which was based on RC equivalent circuit model and identified by applying multiple linear regression method. Urban Dynamometer Driving Schedule simulation comparative results have shown that the proposed algorithm has higher SOC estimation accuracy than the conventional Kalman algorithm and can keep error within 2%. |
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| Keywords: | fuzzy logic Kalman filter multiple linear regression parameter identification lithium ion battery state of charge |
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