Energy Consumption Modeling for a Heavy-duty Purely Electric-powered Vehicle

doi:10.12382/bgxb.2022.1079

Abstract

Abstract:

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.

Key words: heavy-duty purely electric-powered vehicle, combined energy consumption model, LSTM neural network, driving dynamics, complex operating condition

CLC Number:

U463.1

WANG Erlie, WANG Shuai, PI Dawei, WANG Hongliang, WANG Xianhui, XIE Boyuan. Energy Consumption Modeling for a Heavy-duty Purely Electric-powered Vehicle[J]. Acta Armamentarii, 2024, 45(4): 1229-1236.

Figures/Tables 13

Fig.1 Energy flow of the electric-powered vehicle

Fig.2 Motor efficiency fitting

Fig.3 Transmission structure diagram

Fig.4 First gear efficiency of transmission

Fig.5 Framework of combined energy consumption model

Fig.6 Schematic diagram of LSTM neural network

Fig.7 Vehicle speed of operating segment

Fig.8 Road slope of operating segment(unstructured road)

Fig.9 Loading mass of operating segment

Table 1 Test vehicle parameters

参数	数值
空载质量/t	35
迎风面积/m²	7.68
车轮半径/m	0.72
电机额定功率/kW	350
电机额定转矩/(N·m)	1800
电机额定转速/(r·min^-1)	1900

Fig.10 Comparison of the predicted power consumption results of the proposed combined model and the VT-Micro model

Fig.11 Comparison of the predicted power consumption results of the proposed combination model and the RBF neural network

Table 2 Comparison of power prediction errors

模型	e_ave/%	S/kW
组合能耗模型	6.67	5.44×10³
VT-Micro能耗模型	24.43	1.72×10⁴
RBF能耗模型	10.02	7.11×10³

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