Electric vehicle battery consumption estimation model based on simulated environments

被引：0

作者：

Cejudo I. ^{[1
]}

Arandia I. ^{[1
]}

Urbieta I. ^{[1
]}

Irigoyen E. ^{[1
]}

Arregui H. ^{[1
]}

Loyo E. ^{[1
]}

机构：

[1] Fundación Vicomtech, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia-San Sebastián

来源：

International Journal of Vehicle Information and Communication Systems | 2024年 / 9卷 / 03期

关键词：

battery estimation; data set; deep learning; electric vehicle; energy consumption; simulation;

D O I：

10.1504/IJVICS.2024.139759

中图分类号：

学科分类号：

摘要：

Governmental policies are promoting using Electric Vehicles (EVs) to reduce carbon emissions and make transportation more energy efficient. Car manufacturers are putting much effort into making reliable EVs. However, consumers still have to deal with the lack of enough infrastructure and an immature technology readiness level. In order to have an accurate battery range prediction and lessen these issues, this research proposes an energy consumption estimation model based on factors related to battery consumption during a trip. As part of the process, Simulation of Urban Mobility (SUMO), a well-known traffic simulation tool, has been used to run many simulations, produce a heterogeneous data set and train the model with a neural network. The results show an accurate battery range forecast, with a coefficient of determination of 0.91. This model can determine trip consumption considering conditions that vehicle manufacturers’ reference consumption values do not. Copyright © 2024 Inderscience Enterprises Ltd.

引用

页码：309 / 333

页数：24

共 39 条

[1]

A Better Route Planner, (2022)

[2]

Arandia I., Cejudo I.N., Irigoyen E., Urbieta I., Arregui H., Loyo E., Analyzing the influence of driver, route and vehicle-related factors in electric vehicle energy consumption, based on real life data, Proceedings of the 3rd International Conference on Robotics Systems and Vehicle Technology (RSVT), pp. 16-21, (2022)

[3]

Badin F., Le Berr F., Briki H., Dabadie J-C., Petit M., Magand S., Condemine E., Evaluation of EVs energy consumption influencing factors, driving conditions, auxiliaries use, driver’s aggressiveness, Proceedings of the World Electric Vehicle Symposium and Exhibition (EVS27), pp. 1-12, (2013)

[4]

Bi J., Wang Y., Sai Q., Ding C., Estimating remaining driving range of battery electric vehicles based on real-world data: a case study of Beijing, China, Energy, 169, pp. 833-843, (2019)

[5]

Bjarkvik E., Furer F., Pourabdollah M., Lindenberg B., Simulation and characterisation of traffic on drive me route around gothenburg using SUMO, SUMO User Conference, (2017)

[6]

Cejudo I., Dataset of electric vehicle simulated trips, (2022)

[7]

Chandran V., Patil C.K., Karthick A., Ganeshaperumal D., Rahim R., Ghosh A., State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms, World Electric Vehicle Journal, 12, 1, (2021)

[8]

Chen Z., Liu Y., Ye M., Zhang Y., Chen Z., Li G., A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles, Renewable and Sustainable Energy Reviews, 151, (2021)

[9]

De Cauwer C., Verbeke W., Coosemans T., Faid S., Van Mierlo J., A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions, Energies, 10, 5, (2017)

[10]

Dollinger M., Fischerauer G., Model-based range prediction for electric cars and trucks under real-world conditions, Energies, 14, 18, (2021)

← 1 2 3 4 →