Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Qingchao Liu; Fenxia Gao; Jingya Zhao; Weiqi Zhou

doi:10.7307/ptt.v35i5.202

Authors

Qingchao Liu Automotive Engineering Research Institute, Jiangsu University; School of Mechanical and Aerospace Engineering, Nanyang Technological University
Fenxia Gao Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology
Jingya Zhao Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology
Weiqi Zhou Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology

DOI:

https://doi.org/10.7307/ptt.v35i5.202

Keywords:

electric vehicle, energy consumption prediction, green travel, signalised intersection

Abstract

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm.

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