Integrated Motor Optimization and Route Planning for Electric Vehicle using Embedded GPU System

For the route planning of electric vehicles (EV) a greater emphasize is placed on minimizing the energy consumption. The model used to calculate the energy along the path is typically based on the mechanical model of the vehicle. However, to be more accurate, one should also consider the motor losses. In this paper, we propose an integrated motor optimization and route planning for EV based on the Particle Swarm Optimization (PSO) and the Bellman-Ford (BF) routing algorithm. The PSO is used to calculate optimized magnetic flux settings for an induction motor for various operating points. The calculated settings maintain the high efficiency of the motor throughout the trip, but are also used to accurately calculate the motor losses prior to planning the route. The BF algorithm is used to calculate optimized routes. A Pareto front of optimized routes that minimized energy and distance is produced and allows the user to select the preferred route. Both the PSO and the BF are implemented in CUDA on an embedded NVIDIA Jetson TX2 graphics processing unit (GPU) for maximum performance. The system is tested on road maps with up to 4.6 million edges and provides a speedup of 19.3x for the PSO and 10.1x for the BF.