基于EKF-Markov方法的动力电池SOC预测
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摘要
针对工况环境下动力电池SOC的变化具有非线性的特点,对未来SOC状态进行精确预测。首先采用EKF预测模型对动力电池SOC值进行预测,根据预测结果划分SOC状态区间,进一步得到SOC值的Markov状态转移矩阵,然后将EKF模型与Markov状态转移矩阵相结合对SOC进行预测。设计了UDDS工况下的实验验证方案来获取动力电池SOC数据样本,对比分析表明,EKF-Markov方法能够有效地削弱EKF方法所产生的预测误差累积效应,平均预测误差相较EKF降低了83.3%,可对动力电池SOC做出更精确的预测。
Aiming at the non-linear character of the change of SOC of power battery which operated at working conditions. In order to make reasonably accurate prediction of SOC at some indeterminate point in the future, firstly,through the application of EKF model for predicting the SOC of power battery, basised on the results of prediction,dividing the state interval of SOC, state transfer matrix of Markov of SOC can be got futher, then combing with EKF model and state transfer matrix of Markov for predicting the SOC. The data sample of the SOC of power battery was presented by designing the experimental validation scheme in the working condition of UDDS. A comparative analysis revealed that the predictive error of the approach of EKF-Markov is reduced 83.3% than EKF,which can make relatively accurate prediction for the SOC of power battery, which can make more exact prediction of SOC.
Aiming at the non-linear character of the change of SOC of power battery which operated at working conditions. In order to make reasonably accurate prediction of SOC at some indeterminate point in the future, firstly,through the application of EKF model for predicting the SOC of power battery, basised on the results of prediction,dividing the state interval of SOC, state transfer matrix of Markov of SOC can be got futher, then combing with EKF model and state transfer matrix of Markov for predicting the SOC. The data sample of the SOC of power battery was presented by designing the experimental validation scheme in the working condition of UDDS. A comparative analysis revealed that the predictive error of the approach of EKF-Markov is reduced 83.3% than EKF,which can make relatively accurate prediction for the SOC of power battery, which can make more exact prediction of SOC.
引文
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[2]HE W,WILLIARD N,CHEN C C,et al.State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation[J].International Journal of Electrical Power&Energy Systems,2014,62:783-791.
[3]HU X S,SUN F C,CHENG X M.Recursive calibration for a lithium iron phosphate battery for electric vehicles using extended Kalman filtering[J].Journal of Zhejiang University-Science A(Applied Physics&Engineering),2011,11:818-825.
[4]陈虹,宫洵,胡云峰,等.汽车控制的研究现状与展望[J].自动化学报,2013,39(4):322-346.
[5]BHANGU B S,BENTLEY P,STONE D A,et al.Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles[J].IEEE Trans,2005,54:783-794.
[6]HURIA T,CERAOLO M,GAZZARRI J,et al.Simplified extend Kalman filter observe for SOC estimation of commercial power-oriented LFP lithium battery cells[J].SAE International,2013(1):1544-1554.
[7]程水英.无味变换与无味卡尔曼滤波[J].计算机工程与应用,2008,44(24):25-35.
[8]盛鲁英.基于隐马尔科夫模型的电动汽车电池寿命的研究[D].哈尔滨:哈尔滨工业大学,2012.
[9]HUANG Y J,YIN C L,ZHANG J W.Development of the energy management strategy for parallel hybrid electric urban buses[J].Chinese Journal of Mechanical Engineering,2008(4):44-50.
[10]塔拉.电动汽车用动力电池模型仿真及寿命特性研究[D].北京:清华大学,2011.
[11]夏超英,张术,孙宏涛.基于推广卡尔曼滤波算法的SOC估算策略[J].电源技术,2007,31(5):414-417.