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Optimization of battery swapping station for electric vehicles by novel adaptive GWO algorithm

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  • Ali, Md Inayat
  • Mandal, Rajib Kumar
  • Kumar, Amitesh

Abstract

This paper presents a novel approach for optimising the scheduling of battery charging in battery swapping stations (BSS) to enhance their profitability. A multi-objective optimization model is developed to maximise the net profit of the BSS while maintaining a high quality of service for electric vehicle (EV) users. In the proposed framework, EVs can act both as battery consumers and providers. To solve this optimization problem, we employ a Novel Adaptive Grey Wolf Optimization (AGWO) algorithm that dynamically adjusts to system changes to iteratively determine optimal charging schedules. The model incorporates various realistic constraints, including battery state of charge (SOC), energy balance, customer satisfaction thresholds, battery inventory levels, and financial exchanges, while accounting for dynamic electricity pricing and stochastic EVs arrival rates. The proposed algorithm is implemented and validated in MATLAB Simulink. To evaluate the performance of the proposed Novel AGWO algorithm, a case study is conducted for an urban area in Patna district, Bihar, India (25.590° N, 85.110° E), where a centralized solar-powered Battery Storage System (BSS) is considered as a replacement for smaller distributed charging stations within a 10 km radius. The simulation results demonstrate that the AGWO algorithm outperforms existing methods, including Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), Cuckoo Search Optimization (CSO), and Chicken Swarm Algorithm (CSA). Specifically, AGWO achieves the highest average profit (94.556 %) and converges in approximately 60 iterations, faster than the competing algorithms. Additionally, AGWO exhibits superior battery stock management and scheduling efficiency under dynamic and uncertain system conditions. This research contributes to the advancement of sustainable transportation systems by providing a practical and efficient solution for optimising battery charging in Battery Swapping Stations (BSS), thereby promoting the widespread adoption of electric vehicles.

Suggested Citation

  • Ali, Md Inayat & Mandal, Rajib Kumar & Kumar, Amitesh, 2025. "Optimization of battery swapping station for electric vehicles by novel adaptive GWO algorithm," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225029901
    DOI: 10.1016/j.energy.2025.137348
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    References listed on IDEAS

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    1. Alberizzi, Jacopo C. & Callioni, Federico & Pérez Estévez, Manuel A. & Renzi, Massimiliano, 2026. "Optimized integration of charging and battery swapping stations for peak load and cost reduction," Energy, Elsevier, vol. 342(C).

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