Prediction of electric vehicle energy consumption in an intelligent and connected environment

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 law...

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Main Authors: Liu, Qingchao, Gao, Fenxia, Zhao, Jingya, Zhou, Weiqi
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/174037
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1740372024-03-16T16:48:14Z Prediction of electric vehicle energy consumption in an intelligent and connected environment Liu, Qingchao Gao, Fenxia Zhao, Jingya Zhou, Weiqi School of Mechanical and Aerospace Engineering Engineering Electric vehicle Energy consumption prediction 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. Published version This work was supported by the National Natural Science Foundation of China (52372413, U20A20333, U20A20331, 52225212, 52072160, 51905223); Key Research and Development Program of Jiangsu Province (BE2020083-3, BE2019010-2, BE2021011-3); Natural Science Foundation of Jiangsu Province (BK20180100); Six Talent Peaks Project of Jiangsu Province(2018-TD-GDZB-022); Research project of Jiangsu Provincial Public Security Department(2021KO002Z); Transportation Science and Technology Project of Jiangsu Province(2021G05, 2022Y03); Postgraduate Research & Practice Innovation Program of Jiangsu Province(SJCX23_2049) and the Young Talent Cultivation Project of Jiangsu University. 2024-03-12T06:42:22Z 2024-03-12T06:42:22Z 2023 Journal Article Liu, Q., Gao, F., Zhao, J. & Zhou, W. (2023). Prediction of electric vehicle energy consumption in an intelligent and connected environment. Promet - Traffic & Transportation, 35(5), 662-680. https://dx.doi.org/10.7307/ptt.v35i5.202 0353-5320 https://hdl.handle.net/10356/174037 10.7307/ptt.v35i5.202 2-s2.0-85176559231 5 35 662 680 en Promet - Traffic & Transportation © 2023 Qingchao Liu, Fenxia Gao, Jingya Zhao, Weiqi Zhou. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Electric vehicle
Energy consumption prediction
spellingShingle Engineering
Electric vehicle
Energy consumption prediction
Liu, Qingchao
Gao, Fenxia
Zhao, Jingya
Zhou, Weiqi
Prediction of electric vehicle energy consumption in an intelligent and connected environment
description 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Liu, Qingchao
Gao, Fenxia
Zhao, Jingya
Zhou, Weiqi
format Article
author Liu, Qingchao
Gao, Fenxia
Zhao, Jingya
Zhou, Weiqi
author_sort Liu, Qingchao
title Prediction of electric vehicle energy consumption in an intelligent and connected environment
title_short Prediction of electric vehicle energy consumption in an intelligent and connected environment
title_full Prediction of electric vehicle energy consumption in an intelligent and connected environment
title_fullStr Prediction of electric vehicle energy consumption in an intelligent and connected environment
title_full_unstemmed Prediction of electric vehicle energy consumption in an intelligent and connected environment
title_sort prediction of electric vehicle energy consumption in an intelligent and connected environment
publishDate 2024
url https://hdl.handle.net/10356/174037
_version_ 1794549340395536384