Using model-based reasoning for self-adaptive control of smart battery systems

Franz Wotawa*

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtBegutachtung

Abstract

Keeping the power supply of autonomous and electrical vehicles working even in case of faults is of uttermost importance in order to maintain the desired behavior during operation. Especially in case of increased autonomy faults occurring in the power supply when driving should not require the vehicle to stop operation immediately. Instead the autonomous vehicle should still be able to reach a safe state like an emergency lane or a parking space. In this chapter, we introduce a method that enables the development of battery systems that react on internal or external faults in a smart way. In particular, we discuss model-based reasoning for this purpose and show its application for configuring and diagnosing systems. Besides discussing the foundations behind model-based reasoning, we make use of a smart battery system as a case study. In addition, we describe how to use the corresponding physical model for fault detection and a logical model for computing the root cause of the observed failure. The intention behind the chapter is to provide all necessary details of the methods allowing to adapt the methods to implement similar smart adaptive systems.

Originalspracheenglisch
TitelArtificial Intelligence Techniques for a Scalable Energy Transition
UntertitelAdvanced Methods, Digital Technologies, Decision Support Tools, and Applications
Herausgeber (Verlag)Springer International Publishing AG
Seiten279-310
Seitenumfang32
ISBN (elektronisch)9783030427269
ISBN (Print)9783030427252
DOIs
PublikationsstatusVeröffentlicht - 1 Jan. 2020

ASJC Scopus subject areas

  • Ingenieurwesen (insg.)
  • Informatik (insg.)
  • Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
  • Betriebswirtschaft, Management und Rechnungswesen (insg.)

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