基于人工神经网络的镍氢动力电池荷电状态预估
Estimation of the state of charge of NI-MH power battery based on artificial neural network
设计了一种用于预测混合动力汽车镍氢动力电池荷电状态（SOC）的人工神经网络。人工神经网络的输入为当前电流、电压以及前一时刻的SOC，输出为当前SOC。仿真结果表明，设计出的人工神经网络能够准确预测镍氢动力电池的SOC，平均误差在10%以内；对初始SOC值的依存度低，只需90秒即能自适应到目标值。
o track the state of charge (SOC) of the hybrid electric vehicle’s NI-MH battery, an artificial neural network (ANN) is designed in this paper. Current, voltage and the previous SOC are used to ANN inputs, and output is SOC. The result show that, this artificial neural network can track the state of charge(SOC) of the batteries accurately, in the average tracking error less than 10%; the ANN is in low dependence on the initial SOC, and the output can be achieved target value only in 90 seconds.
黄智宇、朴昌浩、付文利、王进
自动化技术、自动化技术设备
混合动力汽车镍氢动力电池荷电状态人工神经网络
hybrid electric vehicleNI-MH power batteriesstate of charge(SOC)artificial neural network(ANN)
黄智宇,朴昌浩,付文利,王进.基于人工神经网络的镍氢动力电池荷电状态预估[EB/OL].(2009-03-11)[2025-08-21].http://www.paper.edu.cn/releasepaper/content/200903-326.点此复制
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