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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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’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 %.

Original language	English
Title of host publication	33rd World Electric Vehicle Symposium & Exposition (EVS33) Peer Reviewed Conference Papers
Publisher	Electric Vehicle Symposium and Exhibition
Number of pages	12
DOIs	https://doi.org/10.5281/zenodo.4023302
Publication status	Published - 11 Sep 2020

Keywords

BEV (battery electric vehicle)
energy consumption
medium-duty
van
efficiency

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

10.5281/zenodo.4023302Licence: CC BY

Fingerprint Dive into the research topics of 'A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans'. Together they form a unique fingerprint.

Cite this

@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",

}

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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › 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 %.

KW - BEV (battery electric vehicle)

KW - energy consumption

KW - medium-duty

KW - van

KW - efficiency

U2 - 10.5281/zenodo.4023302

DO - 10.5281/zenodo.4023302

M3 - Conference contribution

BT - 33rd World Electric Vehicle Symposium & Exposition (EVS33) Peer Reviewed Conference Papers

PB - Electric Vehicle Symposium and Exhibition

ER -