A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans

Camiel J.J. Beckers; Mário Paroha; Igo J.M. Besselink; Henk Nijmeijer

doi:10.5281/zenodo.4023302

A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans

Camiel J.J. Beckers, Mário Paroha, Igo J.M. Besselink, Henk Nijmeijer

Dynamics and Control

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

Samenvatting

For cost-optimal utilization of battery electric delivery vans, energy consumption prediction is important. This paper presents a microscopic energy consumption tool, which requires the intended route as input. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. Secondly, up-to-date environmental data on the weather, the road slope profile, and local speed legislation are obtained through API’s via the internet. The results show good correspondence with the measured energy consumption. Validation with several measured trips shows that the energy consumption is predicted with an error that rarely exceeds 10 %.

Originele taal-2	Engels
Titel	33rd World Electric Vehicle Symposium & Exposition (EVS33) Peer Reviewed Conference Papers
Uitgeverij	Electric Vehicle Symposium and Exhibition
Aantal pagina's	12
DOI's	https://doi.org/10.5281/zenodo.4023302
Status	Gepubliceerd - 11 sep 2020

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10.5281/zenodo.4023302Licentie: CC BY

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Citeer dit

@inproceedings{a3c22fd25619418c8f7119396958a6e1,

title = "A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans",

abstract = "For cost-optimal utilization of battery electric delivery vans, energy consumption prediction is important. This paper presents a microscopic energy consumption tool, which requires the intended route as input. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. Secondly, up-to-date environmental data on the weather, the road slope profile, and local speed legislation are obtained through API{\textquoteright}s via the internet. The results show good correspondence with the measured energy consumption. Validation with several measured trips shows that the energy consumption is predicted with an error that rarely exceeds 10 %.",

keywords = "BEV (battery electric vehicle), energy consumption, medium-duty, van, efficiency",

author = "Beckers, {Camiel J.J.} and M{\'a}rio Paroha and Besselink, {Igo J.M.} and Henk Nijmeijer",

year = "2020",

month = sep,

day = "11",

doi = "10.5281/zenodo.4023302",

language = "English",

booktitle = "33rd World Electric Vehicle Symposium & Exposition (EVS33) Peer Reviewed Conference Papers",

publisher = "Electric Vehicle Symposium and Exhibition",

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A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans. / Beckers, Camiel J.J.; Paroha, Mário; Besselink, Igo J.M.; Nijmeijer, Henk.

33rd World Electric Vehicle Symposium & Exposition (EVS33) Peer Reviewed Conference Papers. Electric Vehicle Symposium and Exhibition, 2020.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans

AU - Beckers, Camiel J.J.

AU - Paroha, Mário

AU - Besselink, Igo J.M.

AU - Nijmeijer, Henk

PY - 2020/9/11

Y1 - 2020/9/11

N2 - For cost-optimal utilization of battery electric delivery vans, energy consumption prediction is important. This paper presents a microscopic energy consumption tool, which requires the intended route as input. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. Secondly, up-to-date environmental data on the weather, the road slope profile, and local speed legislation are obtained through API’s via the internet. The results show good correspondence with the measured energy consumption. Validation with several measured trips shows that the energy consumption is predicted with an error that rarely exceeds 10 %.

AB - For cost-optimal utilization of battery electric delivery vans, energy consumption prediction is important. This paper presents a microscopic energy consumption tool, which requires the intended route as input. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. Secondly, up-to-date environmental data on the weather, the road slope profile, and local speed legislation are obtained through API’s via the internet. The results show good correspondence with the measured energy consumption. Validation with several measured trips shows that the energy consumption is predicted with an error that rarely exceeds 10 %.

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KW - van

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