| Issue |
MATEC Web Conf.
Volume 133, 2017
BulTrans-2017 – 9th International Scientific Conference on Aeronautics, Automotive and Railway Engineering and Technologies
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 5 | |
| Section | Automotive Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201713302002 | |
| Published online | 07 November 2017 | |
Predictive and holistic energy distribution for hybrid electric vehicles
1 PSA Groupe, Research and Innovation Department, 78943 Vélizy- Villacoublay, France
2 Ecole Polytechnique Fédérale de Lausanne, IPESE, 1100 Lausanne, Switzerland
* Corresponding author: zlatina.dimitrova@mpsa.com
The improvement of the efficiency of vehicle energy systems stimulate active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named “multi-objective optimization” is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical and economic point of view .This paper presents a systematic optimization methodology for vehicle energy systems that delivers the designs of optimal vehicle powertrain solutions and their optimal operating strategies in a holistic way. The methodology is applied on D class hybrid electric vehicles, in order to define the powertrain configurations, to estimate the cost of the powertrain equipment and the optimal operating parameters. The optimal designs and operating strategies are researched for the normalized European driving cycle. The optimization can be done on-line and consider the next step of the driving cycle, which allow for predictive energy distribution strategies.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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