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 | |
| Publication status | Published - 11 Sept 2020 |
| Event | 33rd International Electric Vehicle Symposium and Exposition (EVS33) - Portland, United States Duration: 14 Jun 2020 → 17 Jun 2020 Conference number: 33 https://evs33portland.org/ |
Conference
| Conference | 33rd International Electric Vehicle Symposium and Exposition (EVS33) |
|---|---|
| Abbreviated title | EVS33 |
| Country/Territory | United States |
| City | Portland |
| Period | 14/06/20 → 17/06/20 |
| Internet address |
Keywords
- BEV (battery electric vehicle)
- energy consumption
- medium-duty
- van
- efficiency
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TU/e Microscopic Energy Consumption PRediction tOol 0.1 (TU/e MECPRO 0.1)
Beckers, C. J. J. (Creator), Geraedts, T. (Contributor), Besselink, I. J. M. (Creator) & Nijmeijer, H. (Creator), 4TU.Centre for Research Data, 24 Aug 2021
DOI: 10.4121/12764732
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