Genetic Algorithm-Based Optimization of EV Charging Station Placement on Long-Distance Routes

The rapid adoption of electric vehicles (EVs) has introduced significant challenges in planning and optimizing charging infrastructure, especially along extensive road networks. This paper presents a comprehensive optimization model using genetic algorithms (GA) to determine the optimal placement of EV charging stations along long-distance roads, aiming to mini-mize the total cost while ensuring service reliability. The model integrates various parameters, including road characteristics, traffic dynamics, multiple EV types, driver behavior, charging station capacities with renewable energy integration, and grid infrastructure constraints. Detailed mathematical formulations of the EV energy consumption, state-of-charge dynamics, charging station operations, objective function, and constraints are provided. The simulation results demonstrate the effectiveness of the proposed GA-based optimization in minimizing the total cost while ensuring all EVs are adequately served without violating grid capacity constraints. This work contributes to the efficient planning of EV charging infrastructure, facilitating the transition towards sustainable transportation.