Quantum Optimization of Electric Vehicle Charging Schedules Using QAOA
By Anmol
This study applies the Quantum Approximate Optimization Algorithm (QAOA) to optimize charging schedules for electric motor vehicles (EMVs) on a motorway network. Using a 20-qubit simulation in Qiskit, I address the challenge of assigning 2 vehicles to 5 chargers across 2 routes, minimizing travel time, energy consumption, and charging costs. The QAOA approach achieved a cost estimate of 10–20 units, comparable to a classical greedy algorithm’s 12.97 units, while satisfying constraints on route selection, charger capacity, and battery demands. Despite a longer runtime of approximately 30 minutes due to quantum circuit simulation, the results highlight QAOA’s potential to enhance the efficiency and scalability of EV charging infrastructure. This work bridges quantum computing and transportation energy management, offering insights for sustainable mobility solutions. Future research should focus on quantum hardware implementation and larger-scale networks.
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