| 基于RLS与EKF算法的锂电池SOC估计 |
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| 引用本文： | 刘江,史仪凯,袁小庆,曹玉丽. 基于RLS与EKF算法的锂电池SOC估计[J]. 测控技术, 2013, 32(8): 123-125 |
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| 作者姓名： | 刘江 史仪凯 袁小庆 曹玉丽 |
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| 作者单位： | 西北工业大学机电学院,陕西西安,710072 |
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| 基金项目： | 国家自然科学基金资助项目(51105316)；西北工业大学研究生创业种子基金资助项目(Z2012044) |
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| 摘 要： | 准确估计荷电状态是电池管理系统高效和安全运行的关键因素之一.以Thevenin模型为基础,运用递推最小二乘法,对模型参数进行估计并且定期更新.采用扩展卡尔曼滤波算法实现了对锂电池荷电状态的估算.仿真结果表明,该估算策略能保持很高的精度,并对观测噪声有很强的抑制作用.
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| 关 键 词： | 扩展卡尔曼滤波(EKF) 荷电状态(SOC) 递推最小二乘(RLS) 锂电池 |
SOC Estimation of Lithium Battery Based on RLS and EKF |
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| LIU Jiang , SHI Yi-kai , YUAN Xiao-qing , CAO Yu-li. SOC Estimation of Lithium Battery Based on RLS and EKF[J]. Measurement & Control Technology, 2013, 32(8): 123-125 |
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| Authors: | LIU Jiang SHI Yi-kai YUAN Xiao-qing CAO Yu-li |
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| Abstract: | Accurate estimation of the state of charge(SOC) is one of key factors to ensure highly effective and safe working of battery management system (BMS).Based on Thevenin model,the model parameters are estimated and updated regularly through recursive least squares(RLS) algorithm.Then,the extended Kalman filter(EKF) algorithm is utilized to achieve SOC estimation of the lithium battery.The simulation results show that the estimation strategy can maintain high accuracy,and produce very strong inhibition of observation noise. |
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| Keywords: | extended Kalman filter(EKF) state of charge(SOC) recursive least squares(RLS) lithium battery |
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