A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles

Anil K. Madhusudhanan, Xiaoxiang Na and David Cebon
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Anil K. Madhusudhanan: Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
Xiaoxiang Na: Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
David Cebon: Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK

Energies, 2021, vol. 14, issue 7, 1-15

Abstract: 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.

Keywords: energy consumption; drive cycle; modelling framework; electric vehicle; diesel vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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