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Estimation of the state of charge of Ni-MH battery pack based on artificial neural network

机译:基于人工神经网络的Ni-MH电池组充电状态估计

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To track the state of charge (SOC) of Ni-MH battery pack at the hybrid electric vehicle, an artificial neural network (ANN) is designed. Current, voltage and the previous SOC are used to inputs of ANN, 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 5%; the ANN is in low dependence on the initial SOC, and the output can be achieved target value only in 90 seconds.
机译:为了跟踪混合动力电动车辆的Ni-MH电池组的充电状态(SOC),设计了一种人工神经网络(ANN)。电流,电压和先前的SOC用于输入ANN,输出是SOC。结果表明,这种人工神经网络可以准确地跟踪电池的充电状态(SOC),平均跟踪误差小于5%; ANN对初始SOC的依赖性低,并且输出只能在90秒内实现目标值。

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