Evaluating system architectures for driving range estimation and charge planning for electric vehicles. (16th October 2020)

• Record Type: Journal Article
• Authors: Thorgeirsson, Adam Thor; Vaillant, Moritz; Scheubner, Stefan; Gauterin, Frank
• Is Part Of: Software, practice & experience. Volume 51:Number 1(2021)
• Page Start: 72
• Page End: 90
• Publication Date: 2020-10-16
• Subjects: connected vehicles -- distributed computing -- electric vehicles -- machine learning -- modeling and simulation -- range anxiety -- range estimation -- system architecture; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2914
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: 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. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.