A multi-objective optimization framework for planning electric vehicle charging infrastructure incorporating traffic demand, cost, and equity considerations

This study develops a constrained multi-objective optimization framework for public Electric Vehicle (EV) charging infrastructure planning that simultaneously addresses spatial coverage, traffic-driven demand satisfaction, investment cost, and equity in capacity allocation. The framework integrates the 2025-2027 public charging station rollout plan for Jinyun County, China, with empirically observed 2024 traffic flow data from 13 continuous observation stations and applies a Differential Evolution (DE) strategy to search the feasible deployment space under budget and rollout constraints. The optimized deployment selects 15 stations from 43 planned candidates, yielding a total installed capacity of 6000 kW with an investment of ¥6.24 million, while achieving complete town-level coverage and near-complete demand satisfaction with high cost efficiency. Spatial analysis indicates an average effective service radius of 11.4 km and a marked reduction in coverage gaps along high-traffic corridors. Equity assessment based on the Gini coefficient indicates a more balanced distribution of charging capacity relative to traffic demand. Sensitivity and robustness analyses show stable performance under variations in budget availability, demand intensity, and key parameters, which confirms the value of demand-aware station selection and capacity allocation for data-driven and equitable EV charging infrastructure planning.