article · Unconventional Resources
Managing electric vehicle fleet charging over a 24-hour period requires balancing grid-to-vehicle charging and vehicle-to-grid discharging against fluctuating prices, variable arrival times, and battery degradation. An optimisation method called the Quadratic Interpolation Supercell Thunderstorm Algorithm addresses these non-convex, high-dimensional challenges without requiring gradient information. It integrates an adaptive rate control mechanism based on battery state of health, which modifies charging and discharging rates to maintain electrochemical safety and curb battery wear. Evaluated across five case studies covering pricing, arrival, and thermal uncertainties, the method was benchmarked against five metaheuristic and two deterministic algorithms over thirty runs. The approach achieved up to 20 percent lower operational costs than the baseline and maintained the lowest cumulative costs in scalability tests. It also eliminated target state of charge violations and demonstrated strong scheduling robustness.
Large electric vehicle fleets risk incurring high electricity costs and rapid battery degradation if charging schedules ignore market fluctuations and battery health. By intelligently coordinating vehicle-to-grid power flows and protecting battery lifespan under real-world operational uncertainties, robust optimisation algorithms can significantly reduce operating expenses while ensuring vehicles are reliably charged on time.
This scheduling algorithm is applicable to commercial electric vehicle fleet operators, depot managers, and grid aggregators seeking to minimise energy costs and deliver vehicle-to-grid services. The work represents an applied and tested algorithmic stage, having demonstrated cost reductions and constraint satisfaction across computational benchmark scenarios and uncertainty models, though the abstract does not indicate testing in live physical charging infrastructure.
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— This paper applies the Quadratic Interpolation Supercell Thunderstorm Algorithm to electric vehicle fleet charging scheduling optimization under uncertainties over a 24-hour horizon, through smart coordination between grid-to-vehicle and vehicle-to-grid operations. Validated on the Congress on Evolutionary Computation 2022 benchmark, Quadratic Interpolation Supercell Thunderstorm balances global exploration and local exploitation through a supercell-inspired population structure combined with quadratic interpolation, navigating multi-modal solution spaces and accelerating convergence toward locally smooth cost regions without requiring gradient information. The addressed electric vehicle scheduling problem is inherently non-convex, high-dimensional, due to coupled fleet constraints, stochastic operating conditions, and battery degradation dynamics, which limits the applicability of classical deterministic optimization methods. In addition, an state of health based adaptive C-rate control mechanism is introduced, in which charging and discharging rates dynamically adjust according to battery health and state of charge conditions, keeping operation electrochemically safe and limiting accelerated wear. Five case studies are investigated, including a deterministic baseline, thermal excursion battery degradation uncertainty, day-ahead price uncertainty, arrival/departure uncertainty, and scalability testing. The Quadratic Interpolation Supercell Thunderstorm Algorithm is benchmarked against five competing metaheuristic and two deterministic approaches under equal computational budgets across 30 independent runs. Comparative results show that the improved approach achieves up to 20% lower operational cost compared with the baseline approach, while also obtaining the lowest cumulative cost across all scalability scenarios. Statistical validation further confirms its superiority, with significant differences in 50 out of 56 Wilcoxon pairwise comparisons and the lowest average Friedman rank (1.71). In addition, the Quadratic Interpolation Supercell Thunderstorm Algorithm consistently satisfies target state of charge constraints with zero violations and demonstrates strong scheduling robustness across all investigated scenarios.
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DOI: 10.1016/j.uncres.2026.100541
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