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Advancing Solar Car Strategy with Evolutionary Algorithm

Explore the use of a Strength Pareto evolutionary algorithm for solar cars, optimizing multiple criteria like distance and battery usage. Results, challenges, and future considerations are discussed with a focus on efficiency and performance enhancement.

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Advancing Solar Car Strategy with Evolutionary Algorithm

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  1. Solar Car Strategy An evolutionary algorithm using the Strength Pareto approach Srinivas ‘jack’ Jakkidi

  2. Overview • Solar car basics • Problem statement • Strength Pareto • Algorithm • Results • The road ahead

  3. Solar Car basics • Solar array • Batteries • Aerodynamics • Rolling resistance • Efficiency losses • Resource starvation

  4. Problem Statement • How fast to go through the day? • Maximize distance • Minimize battery usage • Use X% of the battery

  5. Strengths Pareto • Multiple Criterion • Distance, battery usage • Pareto element: no individual travels farther AND uses less energy. • Pareto front: set of all Pareto elements. • Fitness: depends on the number of elements that the element ‘dominates’.

  6. EA • Basic EA structure • Fitness using Pareto technique • Targeting: Scaling the fitness based on its distance from a target Target = end battery desired

  7. Results: w/o targeting

  8. Results: w/o targeting

  9. Results: w/o targeting

  10. Results: w/o targeting

  11. Results: w/ targeting

  12. Results: w/ targeting

  13. Summary • Strength Pareto algorithm works well • Optimizes to less than 1% error • Problems • Targeting Vs. Lowering Battery usage • Computationally intensive

  14. The road ahead • Weather, terrain, etc. • Solar car model • American Solar Challenge

  15. got sun?

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