IoEV-MARL: A Reproducible Offline Dataset and Benchmark for Electric Vehicle Charging Coordination Under Centralized and Decentralized Training

Reproducible offline benchmarks for electric vehicle charging require explicit transition definitions and auditable data processing. We introduce IoEV-MARL, comprising 157,071 decision-level transitions from SUMO simulations of the road network of Tangier, Morocco, organized in a 62-column table. The corpus covers 700 collection episodes, seven seeds, four traffic regimes, and five behavioral arms: NEAREST, WEIGHTED, tabular Independent Q-Learning, and two QMIX checkpoint/exploration configurations. Learned collection policies originate from 120-episode training campaigns. A three-layer contract distinguishes decision-time observations, inter-decision elapsed time, and outcome/evaluation fields. Elapsed time is missing when no subsequent decision is recorded. The ETL protocol documents horizon censoring, missing successors, dimensional grain, and file-level provenance. Seven-fold leave-one-seed-out evaluations benchmark Behavior Cloning and offline Implicit Q-Learning on accept/reject decisions conditional on the selected station. Mean Behavior Cloning balanced accuracy ranges from 0.758 to 0.928 across tiers and feature sets, measuring imitation rather than operational performance. In matched greedy evaluation, QMIX has lower station-load dispersion in 7/7 seeds under Normal traffic, versus 2/7, 4/7, and 4/7 under Light, Peak, and Surge. Discrete-state audits characterize coverage gaps and structural reachability constraints. IoEV-MARL supports offline decision-learning research without establishing closed-loop performance gains.