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Electric vehicle (EV) design offers additional degrees of freedom in powertrain layout. A tremendous number of topologies have to be compared to find the best one for a given set of requirements. An integrated approach is needed to increase powertrain efficiency not only on component level. In this paper a design space exploration (DSE) method is presented to study interdependencies of all involved components. The research is done for topologies with multiple motors (AWD) combined with multiple-speed gearboxes. The objective is a holistic topology optimization to increase driving range. The DSE's key element which defines the energy management strategy (EMS) and computes consumption is discussed in detail. Dynamic programming (DP) is used to determine the most energy efficient operation strategy for the respective topology. Different methods for EMS definition are compared in terms of predicted load collectives, energy efficiency and computing time. Measures to reduce the prediction error for a fast backwards simulation are presented.

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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