Battery Modeling Using Real Driving Cycle and Big-Bang Big-Crunch Algorithm

The purpose of this study is to model battery packs integrated in a ten fuel cell hybrid electric vehicle fleet developed among the European project Mobypost dedicated to postal delivery applications. This project led to create and feed a big database as the vehicles were deeply monitored. Thanks to this database and Big-Bang Big-Crunch optimization algorithm this paper proposes a method to model battery using real driving cycle data in few minutes with a NRMSE less than 0.02.

Mots clés

HYBRID AND ELECTRIC VEHICLES BATTERY AGING

VIEILLISSEMENT BATTERIE LITHIUM VEHICULE HYBRIDE ENERGIE RECHARGE DE VEHICULE ELECTRIQUE

Domaines

Energie électrique Thermique [physics.class-ph] Automatique / Robotique Mécanique des fluides [physics.class-ph]

Fichier principal

aef5a8ce-28e4-4cde-8128-49886d4a0a83-author.pdf (442.24 Ko)

Origine	Fichiers produits par l'(les) auteur(s)

Energie Femto-st : Connectez-vous pour contacter le contributeur

https://hal.science/hal-02300488

Soumis le : dimanche 29 septembre 2019-14:13:10

Dernière modification le : lundi 7 août 2023-15:22:08

Archivage à long terme le : lundi 10 février 2020-13:42:24

Dates et versions

hal-02300488 , version 1 (29-09-2019)

Identifiants

HAL Id : hal-02300488 , version 1
DOI : 10.1109/ITEC.2018.8449951

Citer

Loïc Vichard, Simon Morando, Alexandre Ravey, Fabien Harel, Pascal Venet, et al.. Battery Modeling Using Real Driving Cycle and Big-Bang Big-Crunch Algorithm. Transportation Electrification Conference and Expo, Jun 2018, Long Beach, CA, United States. ⟨10.1109/ITEC.2018.8449951⟩. ⟨hal-02300488⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CNRS UNIV-FCOMTE UNIV-BM UNIV-LYON1 INSA-LYON EC-LYON FEMTO-ST AMPERE UNIV-BM-THESE IFSTTAR TDS-MACS INSA-GROUPE UDL INRAE UNIV-EIFFEL

256 Consultations

145 Téléchargements