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A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries

Om A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries

Given the limitations of state-of-the-art methods, this book presents a state of health (SOH) forecasting method that is suitable for lithium-ion battery (LIB) systems in real-world battery electric vehicle operation. Its histogram-based features can capture the higher operational variability compared to constant and controlled laboratory operation. Also, the transferability of a trained machine learning model to new LIB cell types and new operational domains is investigated. The presented SOH forecasting method can be provided as a cloud service via a web or smartphone app to fleet managers. Forecasting the SOH enables fleet managers of battery electric vehicle fleets to forecast and plan vehicle replacements.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783658431877
  • Bindende:
  • Paperback
  • Sider:
  • 260
  • Utgitt:
  • 2 februar 2024
  • Utgave:
  • 24001
  • Dimensjoner:
  • 148x15x210 mm.
  • Vekt:
  • 341 g.
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 11 juni 2024

Beskrivelse av A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries

Given the limitations of state-of-the-art methods, this book presents a state of health (SOH) forecasting method that is suitable for lithium-ion battery (LIB) systems in real-world battery electric vehicle operation. Its histogram-based features can capture the higher operational variability compared to constant and controlled laboratory operation. Also, the transferability of a trained machine learning model to new LIB cell types and new operational domains is investigated. The presented SOH forecasting method can be provided as a cloud service via a web or smartphone app to fleet managers. Forecasting the SOH enables fleet managers of battery electric vehicle fleets to forecast and plan vehicle replacements.

Brukervurderinger av A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries



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