Skip navigation
  •  Inicio
  • UDC 
    • Cómo depositar
    • Políticas do RUC
    • FAQ
    • Dereitos de Autor
    • Máis información en INFOguías UDC
  • Percorrer 
    • Comunidades
    • Buscar por:
    • Data de publicación
    • Autor
    • Título
    • Materia
  • Axuda
    • español
    • Gallegan
    • English
  • Acceder
  •  Galego 
    • Español
    • Galego
    • English
  
Ver ítem 
  •   RUC
  • Escola Politécnica de Enxeñaría de Ferrol
  • Investigación (EPEF)
  • Ver ítem
  •   RUC
  • Escola Politécnica de Enxeñaría de Ferrol
  • Investigación (EPEF)
  • Ver ítem
JavaScript is disabled for your browser. Some features of this site may not work without it.

Intelligent Model for Power Cells State of Charge Forecasting in EV

Thumbnail
Ver/abrir
Lopez_Victor_2022_Intelligent_model_for_power_cells_state_of_charge_forecasting_in_EV.pdf (1.586Mb)
Use este enlace para citar
http://hdl.handle.net/2183/31763
Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
A non ser que se indique outra cousa, a licenza do ítem descríbese como Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Coleccións
  • Investigación (EPEF) [590]
Metadatos
Mostrar o rexistro completo do ítem
Título
Intelligent Model for Power Cells State of Charge Forecasting in EV
Autor(es)
López, Víctor
Jove, Esteban
Zayas-Gato, Francisco
Pinto-Santos, Francisco
Piñón-Pazos, A.
Casteleiro-Roca, José-Luis
Quintián, Héctor
Calvo-Rolle, José Luis
Data
2022-07-19
Cita bibliográfica
López, V.; Jove, E.; Zayas Gato, F.; Pinto-Santos, F.; Piñón-Pazos, A.J.; Casteleiro-Roca, J.-L.; Quintian, H.; Calvo-Rolle, J.L. Intelligent Model for Power Cells State of Charge Forecasting in EV. Processes 2022, 10, 1406. https://doi.org/10.3390/pr10071406
Resumo
[Abstract] In electric vehicles and mobile electronic devices, batteries are one of the most critical components. They work by using electrochemical reactions that have been thoroughly investigated to identify their behavior and characteristics at each operating point. One of the fascinating aspects of batteries is their complicated behavior. The type of power cell reviewed in this study is a Lithium Iron Phosphate LiFePO4 (LFP). The goal of this study is to develop an intelligent model that can forecast the power cell State of Charge (SOC). The dataset used to create the model comprises all the operating points measured from an actual system during a capacity confirmation test. Regression approaches based on Deep Learning (DL), such as Long Short-Term Memory networks (LSTM), were evaluated under different model configurations and forecasting horizons.
Palabras chave
LSTM
Forecasting
Battery
 
Versión do editor
https://doi.org/10.3390/pr10071406
Dereitos
Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
ISSN
2227-9717

Listar

Todo RUCComunidades e colecciónsPor data de publicaciónAutoresTítulosMateriasGrupo de InvestigaciónTitulaciónEsta colecciónPor data de publicaciónAutoresTítulosMateriasGrupo de InvestigaciónTitulación

A miña conta

AccederRexistro

Estatísticas

Ver Estatísticas de uso
Sherpa
OpenArchives
OAIster
Scholar Google
UNIVERSIDADE DA CORUÑA. Servizo de Biblioteca.    DSpace Software Copyright © 2002-2013 Duraspace - Suxestións