Out of Gas and Driving on E (for electric, not empty)
In the spring of 2018, our team participated in the Interdisciplinary Contest in Modeling (ICM), an international competition that challenges students to address real-world problems through mathematical modeling. Our focus was on optimizing the placement of Tesla Supercharger stations to support the widespread adoption of electric vehicles (EVs).comap.com
The Challenge
With the global shift towards sustainable energy, the adoption of EVs is accelerating. A critical factor in this transition is the availability of charging infrastructure. Our task was to determine the optimal number and locations for Supercharger stations across the United States to facilitate a complete switch to all-electric personal passenger vehicles.
Our Approach: Java-Based Genetic Algorithm
To tackle this complex problem, we developed a genetic algorithm (GA) in Java. Genetic algorithms are search heuristics inspired by the process of natural selection, effectively solving optimization problems by iteratively improving candidate solutions.
Key Components of Our GA:
Representation of Solutions: Each potential solution, or "individual," was represented as a set of coordinates corresponding to possible Supercharger locations.
Fitness Function: We designed a fitness function to evaluate how well each individual met our objectives, considering factors such as coverage of high-demand areas, proximity to major highways, and minimization of installation costs.
Selection: Individuals with higher fitness scores were more likely to be selected for reproduction, ensuring that favorable traits were passed on to subsequent generations.
Crossover and Mutation: We implemented crossover and mutation operators to introduce variability, allowing the algorithm to explore a diverse set of solutions and avoid local optima.
Results and Insights
Our GA efficiently identified optimal locations for Supercharger stations, balancing urban, suburban, and rural needs. The algorithm's adaptability allowed us to simulate various adoption rates of EVs, providing a scalable solution for future infrastructure planning.