Thermal-aware multi-objective optimization of battery swapping station with operational cost and battery degradation considerations

The swift growth in electric vehicle usage and the need for a high-performance energy supply chain have become a research hotspot. The battery swapping station (BSS) approach is considered a promising alternative to conventional charge-only scheme. However, the problem of effective control of operations in BSS poses several important challenges, as it requires joint minimization of expenses and battery degradation. In current study, a multi-objective MILP formulation framework is proposed for BSS operation to concentrate on optimizing the operational cost, battery degradation, and thermal stress. The proposed model considers battery State-of-charge (SoC) dynamics, time of use electricity pricing, demand uncertainties, battery aging phenomenon, grid interaction constraints, and vehicle to grid (V2G) functionality. The enhanced formulation of epsilon-constraint approach helps to generate Pareto-efficient solutions, which enable decision-makers to assess the trade-off between financial effectiveness and operational sustainability. Evaluation of the proposed model on a public dataset including, Tsinghua taxi fleet, NEPRA electricity tariff, and NASA battery aging data, achieves a considerable improvement in cost reduction (11.1%) and in battery life prolongation (38.0%). The sensitivity analysis in terms of the peak-to-off-peak price ratio, fleet size, ambient temperature, and battery chemistry confirms the robustness of the proposed model. The framework provides a fair thermal-aware solution to maintain a balance between energy consumption and cost sustainability.