article · Energy Reports
Widespread electric vehicle adoption faces operational obstacles such as limited driving ranges, battery sizing, and the uneven distribution of charging stations. To tackle issues of excessive waiting and charging durations, a charging scheduling strategy has been formulated for designated regions. The method organises charging stations into distinct queues for different service levels, lowering waiting times and expenses during peak hours. The model balances trade-offs between fairness and overall waiting periods while integrating factors such as reachability, battery state of charge, depth of discharge limits, and charging rate constraints. Driven by a bi-objective online formulation, the system assigns vehicles to stations using the Chaotic Harris Hawks Optimization algorithm, taking account of travel demands, dynamic schedulable time, energy price shifts, and user priorities. Testing via vehicular network simulations reveals marked reductions in travel, queue, and recharging times, alongside lower overall energy costs.
As more drivers switch to electric vehicles, clustered demand and scarce charging infrastructure can create long queues and high grid costs. Smart scheduling algorithms help drivers find available chargers quickly and recharge at lower expenses, ensuring smoother daily transport and preventing local power grids from being overwhelmed during peak travel times.
The method is designed for intelligent transport systems, charging network operators, and fleet management platforms seeking to automate vehicle routing and charging queues. Because performance was validated entirely through Vehicular Ad-hoc Network simulations rather than physical trial deployments, the technology currently represents early-stage applied research that requires field testing on real-world networks before market adoption.
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Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.
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DOI: 10.1016/j.egyr.2024.04.006
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