Estimación del estado de carga de una batería de litio con redes neuronales y validación con FPGA-en-lazo

  1. Martínez-Vera, Erik 1
  2. Rosado-Muñoz, Alfredo 2
  3. Bañuelos-Sánchez, Pedro 1
  1. 1 Universidad de las Américas Puebla
    info

    Universidad de las Américas Puebla

    San Andrés Cholula, México

    ROR https://ror.org/01s1km724

  2. 2 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Ano de publicación: 2024

Volume: 21

Número: 3

Páxinas: 243-251

Tipo: Artigo

DOI: 10.4995/RIAI.2024.20718 DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumo

Los vehículos eléctricos presentan una alternativa viable para reducir las emisiones de gases tóxicos en las concentraciones urbanas y para disminuir los efectos de los gases de invernadero. La batería de los vehículos eléctricos debe ser monitoreada con precisión para asegurar su funcionamiento adecuado y seguro. Para esto, es necesario desarrollar algoritmos eficientes que permitan estimar de forma precisa el estado de carga mediante dispositivos embarcados en el vehículo. En este trabajo, se utiliza un conjunto de datos de ciclado de una batería de Litio para entrenar una red neuronal para la estimación del estado de carga. Se realiza una optimización bayesiana para establecer la mejor arquitectura de red neuronal y se valida el comportamiento frente a las mediciones reales que ofrece el conjunto de datos. Para su utilización en un dispositivo embarcado, la red neuronal se valida con un modelo de hardware-en-lazo (HIL) en un FPGA con aritmética de punto fijo. Después del entrenamiento se observa un error promedio cuadrático menor al 2% y una precisión promedio del 97.5%.

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Fonte de datos: Dialnet