Energy Consumption Prediction Method for Electric Vehicles by Integrating Charging Behavior with Data-driven Method

doi:10.11985/2024.01.010

Abstract

Abstract:

Accurately predicting the energy consumption of electric vehicles is essential for efficient vehicle path planning and charging. A multi-model fusion method for energy consumption prediction that takes into account charging behavior is proposed. Firstly, based on the limited parameters and sparse real vehicle data, the energy consumption calculation model is constructed. Then, the charging behavior model is created to analyze and extract features closely related to energy consumption. Finally, long short-term memory neural network(LSTM) is used to construct the energy consumption prediction model. The method is validated with real vehicle data. Results indicate that the proposed method accurately predicts the energy consumption for the given car model with differing starting battery states of charge(SOC), temperatures, and periods. The root mean square error(RMSE) recorded is 1.27, which shows a reduction of no less than 4.5% compared to the existing methods.

Key words: Energy consumption prediction, electric vehicles, charging behavior, long short-term memory neural network

CLC Number:

TM911

MA Junwei, HUO Meiru, ZHAO Min, DU Feng, JING Feng, FENG Yu. Energy Consumption Prediction Method for Electric Vehicles by Integrating Charging Behavior with Data-driven Method[J]. Journal of Electrical Engineering, 2024, 19(1): 97-105.

Figures/Tables 11

References 20

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时间	车速	电量	状态	里程	电流
2022-4-10 18:15:16
2022-4-10 18:15:26
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2022-4-10 20:19:46
2022-4-10 20:19:56
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模型	RMSE
随机森林	1.35
支持向量回归	1.42
Adaboost	1.48
XGboost	1.33
BP神经网络	1.57
RBF神经网络	1.39
RNN	1.49
GRU	1.35
本文方法	1.27