Abstract
Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of finishing the race in the shortest possible time using the energy available. Two heuristic optimization methods are implemented to solve this problem, a convergence and performance comparison of both methods is presented. A computational model of the race is developed, including energy input, consumption and storage systems. Based on this model, the different optimization methods are tested on the optimization of the World Solar Challenge 2015 race strategy under two different environmental conditions. A suitable method for solar car racing strategy is developed with the vehicle specifications taken as an independent input to permit the simulation of different solar or electric vehicles.
Highlights
Solar car races are well-known as universities and college competitions with the aim of of promoting alternative energies and energy efficiency
Defining a solar car racing strategy can be treated as a control optimization problem where, in the most general case, a velocity pattern for the vehicle must be found in order to minimize the time to complete a defined distance considering race, energy and environmental conditions
Five main approaches are analyzed for the two environmental cases:
Summary
Solar car races are well-known as universities and college competitions with the aim of of promoting alternative energies and energy efficiency. The main objective is to cross Australia from Darwin to Adelaide (3022 km) using only solar energy. The success on this challenge demands both an efficient vehicle and an adequate control strategy during the entire race [3]. Several differences are remarkable, the energy efficiency operation is the main objective of both applications and different approaches can be applied on solar cars. Three different optimization methods are tested and results are exposed on Section 5
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