Optimizing Bus Fleet Operations for Prolonged Battery Life: A Genetic Algorithm Approach

The rising popularity of Electric Vehicles (EVs) can be attributed to their potential for substantial fuel savings, reduced emissions, and easier maintenance. However, for widespread adoption of EVs, it's imperative to tackle challenges such as limitations in charging infrastructure and battery degradation. In our study, we employed a proprietary battery degradation model capable of estimating battery health. Our research focuses on using charging-related variables such as start time, duration, and rate of charging to minimize the battery degradation of the entire bus. We compared the degradation performance of the optimized charge scheduling with the first come first charge strategy for five EV buses and noted the overall degradation of the fleet is reduced from 12% to 8%. This suggests that the potential extension of the useful battery life could reach up to 50%. This research activity can lead to the development of a novel depot charging management solution, lowering the EV buses' degradation rate, thereby lowering the total cost of the Battery.