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Energy consumption estimation for electric vehicles using routing API data

Lookup NU author(s): Saad Alateef, Dr Nigel Thomas

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) published in its final definitive form in 2021. For re-use rights please refer to the publishers terms and conditions.


Abstract

Electric vehicle (EV) range anxiety is an influential factor in electric vehicle’s low penetration into the transportation system.There have been several developments on range estimation for electric vehicles, however, the studies which focus on determining theremaining range based on the real-time publicly available data remain low. The majority of the current methods being employedconsider limited data collection and do not consider the most substantial factors that directly impact energy consumption. Thispaper introduces a velocity model based on route information for the range estimation of electric vehicles. It uses publicly availabledata sets obtained from several map services APIs and incorporates this data in the range estimation algorithm. Three map servicesAPIs were used to collect the data for an extended period, and then we analysed this data to extract the most representative datato generate the velocity profiles. The paper uses MATLAB code and python libraries to process the representative data and applythe velocity model. Moreover, we have integrated it into an electric vehicle model, including the battery, to estimate the powerdemand for each trip and the remaining driving range. We observed that producing realistic driving cycles using public data ispossible; furthermore, it simulates the driving patterns and satisfies the constraints of the vehicle.


Publication metadata

Author(s): Alateef S, Thomas N

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 37th Annual UK Performance Engineering Workshop

Year of Conference: 2021

Online publication date: 15/12/2021

Acceptance date: 30/11/2002

Date deposited: 31/01/2022

URL: https://www.researchgate.net/publication/357028895_37th_Annual_UK_Performance_Engineering_Workshop


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