Abstract

This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4%<inline-formula></inline-formula> energy compared to MLC at same average velocity, and achieves a 2.6%<inline-formula> </inline-formula> higher average velocity than MLC at the same energy consumption.

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Original document

The different versions of the original document can be found in:

https://publikationen.bibliothek.kit.edu/1000098758/43042956,
https://doi.org/10.5445/IR/1000098758 under the license cc-by
https://doaj.org/toc/2032-6653
http://dx.doi.org/10.3390/wevj10030052
https://www.mdpi.com/2032-6653/10/3/52,
https://academic.microsoft.com/#/detail/2972289227 under the license https://creativecommons.org/licenses/by/4.0/
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DOIS: 10.3390/wevj10030052 10.5445/ir/1000098758

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Document information

Published on 04/09/19
Accepted on 04/09/19
Submitted on 04/09/19

Volume 2019, 2019
DOI: 10.3390/wevj10030052
Licence: CC BY-NC-SA license

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