Route-based charging infrastructure planning under uncertainty: a heuristic approach for improving network demand and electrification impact

Abstract

The successful decarbonization of road transportation via electrification hinges on the efficient rollout of a well-designed charging infrastructure. This paper proposes a set of methods to support data-driven decision-making in charging infrastructure planning under evolving and uncertain transport electrification conditions. The methods aim to increase the network revenues and the electrification impacts of the charging network placements by avoiding demand losses due to competition between the stations and ensuring that much of the transportation can be carried out according to a realistic data-driven transport electrification model. The proposed methods determine the placement through a greedy network expansion that is guided by static or dynamically recomputed, network-dependent charging demand estimates, together with spatial and temporal constraints, where at each step the candidate location with the highest estimated contribution to the objective is selected to reduce demand losses and improve network coverage. Empirical evaluations show that the proposed Route-Based Network Demand (RBND) method, which dynamically recomputes route-based network demand through transport electrification simulations, achieves the highest total network demand across the evaluated scenarios and, under the most challenging initial state-of-charge (SoC) scenario (10% SoC), enables approximately 82% of transport routes and 83% of transport work, compared with approximately 54% and 64%, respectively, achieved by the best-performing baseline methods for the corresponding objectives. Candidate pruning based on the monotonic behavior of candidate utility eliminates approximately 95% of costly simulation evaluations, enabling the generation of a national-scale 200-station charging network from over one million candidate network expansions in under 15 minutes. These results support efficient and practical charging infrastructure planning under uncertainty.