为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特...为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。展开更多
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
准确估计电池的荷电状态(state of charge,SOC)对电动汽车具有重要意义。针对单一的锂电池开路电压曲线对基于模型SOC估计方法的局限性,提出了一种应用多开路电压曲线结合扩展卡尔曼滤波的锂电池SOC融合估计方法。利用SOC与对应开路电...准确估计电池的荷电状态(state of charge,SOC)对电动汽车具有重要意义。针对单一的锂电池开路电压曲线对基于模型SOC估计方法的局限性,提出了一种应用多开路电压曲线结合扩展卡尔曼滤波的锂电池SOC融合估计方法。利用SOC与对应开路电压之间的离散数据,通过多项式拟合和含有对数函数的复合函数拟合方式,获得了两种开路电压曲线。分别基于这两种开路电压曲线并结合扩展卡尔曼滤波算法,获得了各自的SOC估计结果。利用加权求和对获得的SOC进行融合,得到最终的SOC估计结果。在动态应力测试工况和美国联邦城市驾驶工况下,验证了所提方法的有效性。两种工况下,SOC融合估计的平均绝对误差和均方根误差均出现了明显下降。展开更多
文摘为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。
文摘In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
文摘准确估计电池的荷电状态(state of charge,SOC)对电动汽车具有重要意义。针对单一的锂电池开路电压曲线对基于模型SOC估计方法的局限性,提出了一种应用多开路电压曲线结合扩展卡尔曼滤波的锂电池SOC融合估计方法。利用SOC与对应开路电压之间的离散数据,通过多项式拟合和含有对数函数的复合函数拟合方式,获得了两种开路电压曲线。分别基于这两种开路电压曲线并结合扩展卡尔曼滤波算法,获得了各自的SOC估计结果。利用加权求和对获得的SOC进行融合,得到最终的SOC估计结果。在动态应力测试工况和美国联邦城市驾驶工况下,验证了所提方法的有效性。两种工况下,SOC融合估计的平均绝对误差和均方根误差均出现了明显下降。
