Energy-Aware EV Charging Optimization: A SWOT-Informed Hybrid Decision Framework
Abdelghani EZZAHI, Brahim ZRAIBI, Mohamed MANSOURI, Adam AFADISS, Omar LAMMAMRI
Abstract. The increasing of electrical vehicles (EVs) becomes a big challenge for charging infrastructures, including efficiency, grid impact and service quality. To address this need for EV charging management we used methods such as Optimisation, metaheuristics and deep reinforcement learning (DRL), but each method is limited when used separately. This paper proposes a hybrid decision framework guided by a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, which provides a structured way to combine and organize these approaches under different operating conditions. To organize the decision-making of this framework we are based on three complementary layers : deterministic optimization for day-ahead planning and constraint satisfaction, metaheuristic tuning for multi-objective trade-offs, and DRL-based control for real-time adaptation under uncertainty. The proposed framework is validated using real-world charging session data from the Adaptive Charging Network (ACN-Data) collected at the Caltech site from March-April 2019, under a constrained site with a 50-kW total limit and 7.2 kW per-charger limit. Under nominal conditions, FCFS, MILP, and MILP+PSO all reach a 1.00 satisfaction rate with 0.0 kWh energy deficit. Under surprise-arrival demand shocks, DRL remains robust: at 50% extra arrivals it achieves 0.937 satisfaction with 2.366 kWh deficit, whereas FCFS drops to 0.835 satisfaction with 45.680 kWh deficit; across 20–50% shocks, DRL reduces unmet energy demand by up to ~95% versus FCFS.
Keywords
Energy-Aware Charging, EV Scheduling, SWOT Analysis, Hybrid Decision Framework, DRL
Published online 4/25/2026, 10 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Abdelghani EZZAHI, Brahim ZRAIBI, Mohamed MANSOURI, Adam AFADISS, Omar LAMMAMRI, Energy-Aware EV Charging Optimization: A SWOT-Informed Hybrid Decision Framework, Materials Research Proceedings, Vol. 64, pp 361-370, 2026
DOI: https://doi.org/10.21741/9781644904091-45
The article was published as article 45 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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