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Computing And Optimization For Dc Power Systems Of Electric Transport

Om Computing And Optimization For Dc Power Systems Of Electric Transport

In the modern world, further development of society is impossible without environmentally friendly modes of transport and their effective interaction within the framework of Smart Energy and ECO-driving concepts. Written at a time of rapid development of information technologies in various fields, this book serves as a kind of bridge for the introduction of artificial intelligence into the electric transport power supply systems. The calculation models and methods proposed make it possible to choose the most efficient modern equipment for electric power supply of electric vehicles, which, as a result, saves investment costs and reduces time to make decisions for designing complex objects. Insight is also given into building and adjusting Smart Energy class equipment to achieve optimal and economical modes.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9781786347718
  • Bindende:
  • Hardback
  • Sider:
  • 220
  • Utgitt:
  • 9. mars 2020
  • Dimensjoner:
  • 177x233x18 mm.
  • Vekt:
  • 434 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 20. desember 2024

Beskrivelse av Computing And Optimization For Dc Power Systems Of Electric Transport

In the modern world, further development of society is impossible without environmentally friendly modes of transport and their effective interaction within the framework of Smart Energy and ECO-driving concepts. Written at a time of rapid development of information technologies in various fields, this book serves as a kind of bridge for the introduction of artificial intelligence into the electric transport power supply systems.
The calculation models and methods proposed make it possible to choose the most efficient modern equipment for electric power supply of electric vehicles, which, as a result, saves investment costs and reduces time to make decisions for designing complex objects. Insight is also given into building and adjusting Smart Energy class equipment to achieve optimal and economical modes.

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