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基于GGAP-RBF神经网络的多参数纯电动客车蓄电池荷电状态预测

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Keywords: 汽车工程,荷电状态,广义生长剪枝径向基函数,蓄电池,纯电动客车

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Abstract:

?为了准确预测纯电动客车蓄电池的荷电状态(SOC),提出了基于广义生长剪枝径向基函数(GGAP-RBF)神经网络的多参数纯电动客车蓄电池SOC预测模型。首先以蓄电池端电压、放电电流、环境温度和循环次数作为神经网络输入参数建立GGAP-RBF神经网络蓄电池SOC预测模型,然后以不同放电倍率、环境温度和循环次数的蓄电池放电试验数据作为样本对模型进行训练,并建立了蓄电池仿真模型和纯电动客车整车仿真模型,最后进行了城市道路循环行驶工况(UDDS工况)下单体蓄电池放电试验和纯电动客车40km·h-1等速行驶续驶里程试验研究。结果表明:UDDS工况下,SOC预测值与试验值的均方根误差为0.0264,平均绝对误差为0.0206;纯电动客车40km·h-1等速行驶工况下,SOC预测值与试验值的均方根误差为0.0399,平均绝对误差为0.0313;表明所建立的蓄电池SOC预测模型在各种工况下均能精确预测蓄电池SOC。

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