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New opportunities for large-scale design optimization of electric vehicles using GPU technology

Large-scale system design optimizations of electric vehicles have been limited in the past due to the lack of computational power. In order to fully utilize the potential of EV technology, novel optimization methods need to be developed. This paper investigates new opportunities for EV system optimization evolving from recent advancements in parallel processing hardware. The power train and control system of a fuel cell hybrid electric vehicle are optimized on the parallel-architecture of a graphical processing unit. The significant gain in computation speed enables incorporation of expensive lifetime effects, such as battery degradation, as well as parameter uncertainties, such as variations of driving patterns. Significant speedup factors over single-threaded CPU implementations in the Matlab/Simulink and C/C++ environment are achieved and analyzed.