Summary
Deciding how electric vehicles should share public chargers is tricky because each charging station only sees some of the information. The authors studied if a special way of training multiple agents together helps stations make better decisions than if each learns separately. Using a city traffic simulator with different vehicle volumes, they found that training agents centrally and then letting them act separately led to higher acceptance of charging requests and better overall performance. This suggests that building coordination into the learning process matters more than just adding cooperative rules after the fact.
What this means in practice
- •For urban traffic planners: Improve electric vehicle charging allocation by integrating coordinated multi-agent learning into station management under uncertain traffic conditions.
- •For smart city infrastructure teams: Design charging station software that uses centralized training and decentralized decision making to boost acceptance rates and district-wide efficiency.
Abstract
The allocation of electric vehicles to public charging stations is increasingly constrained by the fact that several heterogeneous facilities often serve the same stream of requests while observing only partial local information. An accept/reject decision that is defensible for one station may nevertheless reduce the efficiency of the charging district as a whole. We formulate this operational problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and examine whether Centralized Training with Decentralized Execution (CTDE), instantiated through QMIX, improves assignment performance relative to a cooperative Independent Q-Learning baseline (IQL). The evaluation uses a six-station urban network simulated with SUMO, five independent random seeds, 200 training episodes, and four traffic flux scenarios ranging from 100 to 250 vehicles/hour. Under this protocol, QMIX reaches a mean reward per evaluated decision of +0.776, versus +0.578 for IQL (delta = +0.198, seed-paired Cohens dz = 2.98, exact two-sided Wilcoxon p = 0.0625, the minimum attainable value at this sample size), and raises the acceptance rate from 72.4% to 94.0% (delta = +21.6 pp, dz = 5.24, Wilcoxon p = 0.0625). A five-variant ablation suggests this gain is primarily attributable to the CTDE architecture when holding cooperative reward terms fixed, though not formally isolated with a single seed. By contrast, cooperative reward shaping and experience sharing show no clear descriptive benefit and may slightly reduce performance. These findings support the view that, for multi-station charging assignment, coordination should be embedded in the learning architecture rather than added only through heuristic cooperation terms.