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.
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence Techniques for a Scalable Energy Transition |
| Subtitle of host publication | Advanced Methods, Digital Technologies, Decision Support Tools, and Applications |
| Publisher | Springer International Publishing AG |
| Pages | 279-310 |
| Number of pages | 32 |
| ISBN (Electronic) | 9783030427269 |
| ISBN (Print) | 9783030427252 |
| DOIs | |
| Publication status | Published - 1 Jan 2020 |
Keywords
- Artificial intelligence for battery control
- Configuration
- Diagnosis
- Model-based reasoning
- Smart adaptation
ASJC Scopus subject areas
- Engineering(all)
- Computer Science(all)
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)