Charging policies for battery swapping station for hybrid motorcycles

The detrimental impact of fossil fuel dependence on the environment and human health needs a shift towards sustainable transportation solutions. Electric mobility, exemplified by electric vehicles (EVs), presents a promising solution to combat air pollution and address climate change concerns. However, the widespread adoption of EVs faces obstacles such as charging time and range anxiety. Battery Swapping (BS) stations have emerged as a potential remedy, facilitating quick battery exchanges to address these issues. This research proposes a method to determine charging policies for hybrid motorcycle fleets using mixed integer linear programming (MILP) and a simple greedy heuristic algorithm to minimize battery degradation and reduce the operating costs of a BS station designed for hybrid motorcycles. By employing these two solution techniques, we focus on evaluating the practicality of these stations and the impact of smart charging decisions on pricing. 10 unique instances were generated using six distinct values of time deltas, resulting in a total of 60 individual instances to evaluate various simulation scenarios, highlighting distinct operational dynamics for motorcycle and station management. Notably, objective function values were lower in the first 20 instances, with the heuristic outperforming the exact method by approximately 175,000 COP in the initial 10 instances. The models demonstrated greater cost efficiency at three and five-minute deltas, effectively capturing real dynamics and minimizing unexpected fluctuations. Simulation times varied significantly, with the heuristic method running between 0.001 and 0.005 s compared to the 50 to 200 s for the more complex exact method, which exhibited a broader range of results but also higher variability, indicating less consistency than the more stable heuristic approach. • Proposes battery-swap stations to cut fossil reliance in hybrid motorcycles. • Uses MILP + heuristic to optimize charging, lowering cost and battery wear. • In 60 cases, heuristic is faster; exact model trims costs a bit more. • Heuristic cuts user wait and balances energy, solar, grid use. • Heuristic solves < 0 . 005 s; exact cheaper but heavier for real-time decision.