Behaviorally informed joint optimization of charger placement and dynamic spatio-temporal pricing for electric vehicle networks

• Joint optimization of charger placement and dynamic spatio-temporal pricing. • Column generation with configuration values evaluated via reinforcement learning. • Behavioral demand model with spatial and temporal charging relocation. • Spatio-temporal pricing improves profitability under congestion and competition. The rapid growth of electric vehicles calls for efficient strategies to design charging networks that are both profitable and accessible. This paper develops an integrated framework that jointly optimizes charger placement and dynamic pricing by combining column generation with reinforcement learning. The column generation master problem governs the selection of charger configurations under budget and accessibility constraints, while pricing is modeled as a sequential decision-making problem and solved using reinforcement learning. To address the intractability of the column generation pricing problem, in which reduced costs depend on reinforcement-learning-based pricing outcomes and therefore admit no closed-form expression, we introduce a set of heuristics to generate promising charger configurations. These candidate configurations are subsequently evaluated using reinforcement learning to estimate their expected profitability. Computational experiments on a synthetic urban grid demonstrate that spatio-temporal pricing yields significantly higher expected profit than uniform pricing strategies, and that extending the behavioral model from spatial-only user charging relocation to joint spatial and temporal charging relocation yields statistically significant revenue gains. The results also highlight the sensitivity of network profitability to the presence of competitors, as well as the robustness of the proposed approach under imperfect demand forecasts. Finally, scalability tests show that heuristic-guided column generation enables efficient solutions for larger networks. These findings underscore the importance of integrating user choice modeling, dynamic pricing, and adaptive optimization in the planning of future EV charging infrastructures.