基于等效模型扩展卡尔曼滤波锂电池SOC估算
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摘要
电池管理系统(BMS)的主要任务是对电池荷电状态(SOC)、续航里程和防止电池过充过放等进行实时诊断,其中电池荷电状态的快速精确的估计是BMS的核心技术。基于锂电池这一动态非线性系统,提出了一种更接近于真实的、改进的PNGV电池等效模型;基于改进的PNGV电池等效模型,对比了卡尔曼滤波算法(KF)和扩展卡尔曼滤波算法(EKF)诊断电池荷电状态的实验结果;分析了扩展卡尔曼滤波算法诊断的实验误差。研究表明:采用扩展卡尔曼滤波算法对电池荷电状态进行诊断得到的结果更加精确,其误差能够一直保持在5%以内。
The main task of the battery management system( BMS) is to diagnose the battery state of charge( SOC),endurance mileage and prevent overcharge and discharge of batteries in real time. The rapid and accurate estimation of SOC is the core technology of BMS. Based on the dynamic nonlinear system of lithium battery,a more realistic and improved equivalent model of PNGV battery was proposed. Based on the improved PNGV battery equivalent model,the experimental results of battery SOC diagnosis of Kalman filter( KF) and extended Kalman filter( EKF) were compared. The experimental error of EKF was analyzed. The results show that EKF is more accurate in diagnosing battery SOC,and the error can be kept within 5%.
The main task of the battery management system( BMS) is to diagnose the battery state of charge( SOC),endurance mileage and prevent overcharge and discharge of batteries in real time. The rapid and accurate estimation of SOC is the core technology of BMS. Based on the dynamic nonlinear system of lithium battery,a more realistic and improved equivalent model of PNGV battery was proposed. Based on the improved PNGV battery equivalent model,the experimental results of battery SOC diagnosis of Kalman filter( KF) and extended Kalman filter( EKF) were compared. The experimental error of EKF was analyzed. The results show that EKF is more accurate in diagnosing battery SOC,and the error can be kept within 5%.
引文
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[10]齐洋洋.电动汽车锂电池SOC估计研究[D].重庆:重庆大学,2015:18-31.QI Yangyang. Study on SOC Estimation of Electric Vehicle Lithium Battery[D]. Chongqing:Chongqing University,2015:18-31.
[2] PLETT G L. Extended Kalman filtering for battery management systems of Li PB-based HEV battery packs:Part 2:Modeling and identification[J]. Journal of Power Sources,2004,134(2):262-276.
[3] PLETT G L. Extended Kalman filtering for battery management systems of Li PB-based HEV battery packs:Part 3:State and parameter estimation[J]. Journal of Power Sources,2004,134(2):277-292.
[4] BHANQU B S,BENTLEY P,STONE D A,et al. Nonlinear observers for predicting state-of-charge and state-of-health of leadacid batteries for hybrid-electric vehicles[J]. IEEE Transactions on Vehicular Technology,2005,54(3):783-794.
[5] ZHENG Yuejiu,LU Languang,HAN Xuebing,et al. Li Fe PO4battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation[J]. Journal of Power Sources,2013,226:33-41.
[6] ANDRE D, APPEL C, SOCZKA-GUTH T, et al. Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries[J]. Journal of Power Sources,2013,224:20-27.
[7]HE Hongwen,LIU Zhentong,HUA Yin. Adaptive extended Kalman filter based fault detection and isolation for a lithium-ion battery pack[J]. Energy Procedia,2015,75:1950-1955.
[8]LIU Zhentong,HE Hongwen. Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter[J]. Applied Energy,2017,185:2033-2044.
[9]邓磊.基于改进PNGV模型的动力锂电池SOC估计和充电优化[D].哈尔滨:哈尔滨工业大学,2014:8-19.DENG Lei. SOC Estimation and Charging Optimization of Power Lithium-ion Battery Based on Improved PNGV Model[D]. Harbin:Harbin Institute of Technology,2014:8-19.
[10]齐洋洋.电动汽车锂电池SOC估计研究[D].重庆:重庆大学,2015:18-31.QI Yangyang. Study on SOC Estimation of Electric Vehicle Lithium Battery[D]. Chongqing:Chongqing University,2015:18-31.