A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles
This article proposes a novel framework to develop computationally efficient energy consumption models of electric and internal combustion engine vehicles. The number of calculations in a conventional energy consumption model prevents the model’s usage in applications where time is limited. As many...
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Online Access: | https://www.mdpi.com/1996-1073/14/7/2031 |
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doaj-bf1661a272ff447f93921cf51c07e3072021-04-06T23:06:11ZengMDPI AGEnergies1996-10732021-04-01142031203110.3390/en14072031A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric VehiclesAnil K. Madhusudhanan0Xiaoxiang Na1David Cebon2Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKDepartment of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKDepartment of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKThis article proposes a novel framework to develop computationally efficient energy consumption models of electric and internal combustion engine vehicles. The number of calculations in a conventional energy consumption model prevents the model’s usage in applications where time is limited. As many fleet operators around the world are in the process of transitioning towards electric vehicles, a computationally efficient energy consumption model will be valuable to analyse the vehicles they trial. A vehicle’s energy consumption depends on the vehicle characteristics, drive cycles and vehicle mass. The proposed modelling framework considers these aspects, is computationally efficient, and can be run using open source software packages. The framework is validated through two use cases: an electric bus and a diesel truck. The model error’s standard deviation is less 5% and its mean is less than 2%. The proposed model’s mean computation time is less than 20 ms, which is two orders of magnitude lower than that of the baseline model. Finally, a case study was performed to illustrate the usefulness of the modelling framework for a fleet operator.https://www.mdpi.com/1996-1073/14/7/2031energy consumptiondrive cyclemodelling frameworkelectric vehiclediesel vehicle |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anil K. Madhusudhanan Xiaoxiang Na David Cebon |
spellingShingle |
Anil K. Madhusudhanan Xiaoxiang Na David Cebon A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles Energies energy consumption drive cycle modelling framework electric vehicle diesel vehicle |
author_facet |
Anil K. Madhusudhanan Xiaoxiang Na David Cebon |
author_sort |
Anil K. Madhusudhanan |
title |
A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles |
title_short |
A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles |
title_full |
A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles |
title_fullStr |
A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles |
title_full_unstemmed |
A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles |
title_sort |
computationally efficient framework for modelling energy consumption of ice and electric vehicles |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2021-04-01 |
description |
This article proposes a novel framework to develop computationally efficient energy consumption models of electric and internal combustion engine vehicles. The number of calculations in a conventional energy consumption model prevents the model’s usage in applications where time is limited. As many fleet operators around the world are in the process of transitioning towards electric vehicles, a computationally efficient energy consumption model will be valuable to analyse the vehicles they trial. A vehicle’s energy consumption depends on the vehicle characteristics, drive cycles and vehicle mass. The proposed modelling framework considers these aspects, is computationally efficient, and can be run using open source software packages. The framework is validated through two use cases: an electric bus and a diesel truck. The model error’s standard deviation is less 5% and its mean is less than 2%. The proposed model’s mean computation time is less than 20 ms, which is two orders of magnitude lower than that of the baseline model. Finally, a case study was performed to illustrate the usefulness of the modelling framework for a fleet operator. |
topic |
energy consumption drive cycle modelling framework electric vehicle diesel vehicle |
url |
https://www.mdpi.com/1996-1073/14/7/2031 |
work_keys_str_mv |
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1721537041642029056 |