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某纯电驱动重载车辆能耗预测模型

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高精度能耗预测模型是准确预测车辆续驶里程的重要前提。针对载荷大幅度变化且非结构化道路运行的纯电驱动重载车辆，建立其组合能耗模型，该模型由能耗计算基本模型与长短时记忆(Long Short-Term Memory，LSTM)神经网络差值修正两部分组成。基于能量流动过程驱动电机和变速器效率建模，结合汽车行驶动力学建立能耗计算基本模型;采用LSTM神经网络来修正基本模型能耗预测结果与车辆典型工况功率测试值的差值，有效提高了大幅变载荷且低信噪比坡度环境下的车辆能耗预测精度，因此组合能耗模型具有参数简单和模型拟合不需解释能耗规律的优点。经试验测试分析，与VT-Micro能耗模型和径向基(Radial Basis Function，RBF)神经网络能耗模型相比，所提组合能耗模型的功率预测平均误差率分别降低了17。76％和3。35％，能够实现纯电驱动重载车辆复杂工况下能耗的准确实时预测。
Energy Consumption Modeling for a Heavy-duty Purely Electric-powered Vehicle
High-precision energy consumption prediction is an important prerequisite for accurately predicting the running range of vehicle. A combined energy consumption model is established for a heavy-duty purely electric-powered vehicle operating on unstructured roads with significant load changes. The proposed model consists of two parts:a basic model for energy consumption calculation and a long short-term memory ( LSTM) neural network for difference correction. Based on the efficiency modeling of drive motor and transmission,the basic model is established in combination with vehicle driving dynamics. Then the LSTM neural network is used to correct the difference between the energy consumption prediction result of the basic model and the power test value of vehicle under typical operating conditions,which effectively improves the prediction accuracy of vehicle energy consumption under significantly variable loads and low signal-to-noise ratio gradient environments. Therefore,the combined energy consumption model has the advantages of simple parameters and model fitting without explaining the energy consumption laws. The real vehicle tests are analyzed. Compared with the VT-Micro model and the Radial basis function (RBF) neural network model for energy consumption,the average error rate of power prediction of the proposed combined model is reduced by 17. 76％ and 3. 35％,respectively,enabling the accurate real-time prediction of energy consumption for the heavy-duty purely electric powered vehicle under complex operating conditions.

heavy-duty purely electric-powered vehiclecombined energy consumption modelLSTM neural networkdriving dynamicscomplex operating condition

王尔烈、王帅、皮大伟、王洪亮、王显会、谢伯元

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南京理工大学 机械工程学院,江苏 南京210094

江苏省商用车智能底盘工程研究中心,江苏 南京210094

先进越野系统技术全国重点实验室,北京100072

纯电驱动重载车辆 组合能耗模型 长短时记忆神经网络 行驶动力学 复杂工况

国家重点研发计划"新能源汽车"重点专项国家自然科学基金南京市科技重大专项(综合类)江苏省重点研发计划重点项目

2021YFB250180052272399202309001BE2023010

2024

10.12382/bgxb.2022.1079

兵工学报
中国兵工学会

兵工学报

CSTPCD北大核心
影响因子：0.735
ISSN：1000-1093
年,卷(期)：2024.45(4)
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