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Evaluating system architectures for driving range estimation and charge planning for electric vehicles [in press]

Thorgeirsson, A. T.; Vaillant, M.; Scheubner, S.; Gauterin, F.

Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000125676
Veröffentlicht am 05.11.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 0038-0644, 1097-024X
KITopen-ID: 1000125676
Erschienen in Software <Chichester>
Vorab online veröffentlicht am 16.10.2020
Schlagwörter connected vehicles, distributed computing, electric vehicles, machine learning, modeling and simulation, range anxiety, range estimation, system architecture
Nachgewiesen in Scopus
Web of Science
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