Optimizing Battery Electric Bus Charging Schedules Using Simulated Annealing

This research paper presents a novel application of the Simulated Annealing (SA) algorithm to optimize charging schedules for Battery Electric Buses (BEBs), aiming to balance battery health, peak energy consumption, and total energy consumption. The study introduces two SA-based techniques, termed “quick” and “heuristic,” and compares their performance against a benchmark Position Allocation Problem (BPAP) solution and a threshold-based strategy known as the Qin-Modified technique. The SA algorithms employ innovative perturbation methods and charge scheduling heuristics to generate and refine candidate solutions, ensuring they remain within the feasible solution space. Results from a 24-hour scenario involving 35 BEBs and 30 chargers demonstrate that the SA approaches, particularly the quick SA, achieve significant reductions in peak demand and overall energy consumption compared to the BPAP and Qin-Modified strategies. Although the SA methods allowed minor deviations below the minimum State of Charge (SOC) threshold, they prioritized slow charging, highlighting their potential to enhance BEB charging operations while mitigating peak demand costs. This study underscores the effectiveness of SA in solving complex BEB charging problems and offers insights into improving public transport electrification strategies.