Partekatu
Gorde erreferentzia
Izenburua
Robustness Evaluation of Energy Management Strategies for Hydrogen-Based Railway Vehicles
Ikerketa taldea
Almacenamiento de energíaSistemas electrónicos de potencia aplicados al control de la energía eléctrica
Beste erakundeak
https://ror.org/02afbgp72https://ror.org/03hp1m080
Bertsioa
Bertsio argitaratuaDokumentu-mota
Kongresu-ekarpenaBahituraren amaiera data
2143-01-01Hizkuntza
IngelesaEskubideak
© 2023 IEEESarbidea
Sarbide irekiaArgitaratzailearen bertsioa
https://doi.org/10.1109/VPPC60535.2023.10403272Non argitaratua
IEEE Vehicle Power and Propulsion Conference 2023 (VPPC). Milan, ItaliaArgitaratzailea
IEEEGako-hitzak
ODS 7 Energía asequible y no contaminante
ODS 9 Industria, innovación e infraestructura
ODS 11 Ciudades y comunidades sostenibles
ODS 13 Acción por el clima ... [+]
ODS 9 Industria, innovación e infraestructura
ODS 11 Ciudades y comunidades sostenibles
ODS 13 Acción por el clima ... [+]
ODS 7 Energía asequible y no contaminante
ODS 9 Industria, innovación e infraestructura
ODS 11 Ciudades y comunidades sostenibles
ODS 13 Acción por el clima
http://vocabularies.unesco.org/thesaurus/concept9546
Energy management
fuel cell
hydrogen
lithium battery
optimization
railway engineering [-]
ODS 9 Industria, innovación e infraestructura
ODS 11 Ciudades y comunidades sostenibles
ODS 13 Acción por el clima
http://vocabularies.unesco.org/thesaurus/concept9546
Energy management
fuel cell
hydrogen
lithium battery
optimization
railway engineering [-]
Gaia (UNESCO Tesauroa)
Energia elektrikoaTeknologia elektronikoa
Erdieroalea
Laburpena
This paper presents a robustness analysis of different energy management strategies designed for hydrogen-based rail vehicles. From the analyzed 4 strategies, one is based on dynamic programming, two ... [+]
This paper presents a robustness analysis of different energy management strategies designed for hydrogen-based rail vehicles. From the analyzed 4 strategies, one is based on dynamic programming, two are rule-based strategies optimized by genetic algorithms, and the last one is a learning-based approach. The strategies are optimized for (or learn from) a nominal scenario. Then, the number of passengers and the consumption of the auxiliaries are varied to generate new scenarios. The results obtained in the new scenarios show that the trends obtained in the nominal scenario are generally respected. Additionally, some other conclusions are also obtained: 1) GA-based strategies get closer to the global optimization of DP as the average demand of the route is higher, and 2) ANFIS should improve the degradation that the FC suffers, as it is increased overmuch compared to the other strategies. [-]
