Evaluating system architectures for driving range estimation and charge planning for electric vehicles

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.

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“…In our previous work [35], we demonstrated the importance of system architecture and module placement for the performance and user experience of driving range prediction and charge planning software. By placing the prediction algorithm parts intelligently across the vehicle and cloud, the performance can be increased.…”

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“…Similarly, Zheng et al [86] develop a hybrid ML model to predict the energy consumption of EVs, considering high-dimensional multivariate data and extracting knowledge from historical travel characteristics for other applications. Moreover, Thorgeirsson et al [87] harness the connection to a back-end of modern vehicles to deploy ML-based driving range estimation software. The system allows accurate range estimation with low latencies, thus improving the experience of EVs users.…”

A review on dissimilar laser welding of steel-copper, steel-aluminum, aluminum-copper, and steel-nickel for electric vehicle battery manufacturing

“…Currently, most EVs can only go around 100-250 km on a single charge, much shorter than their ICE counterparts [15]. Using larger batteries is not a feasible solution owing to limited space in EVs, additional cost, higher weight, and the requirement of more rare-earth elements [16,17]. Hence, there is a need to enhance the energy density of the existing battery system as the key component that determines the vehicle's performance [18,19].…”