This study introduces a lightweight yet systematic simulation-based verification framework for evaluating electric vehicle routing (EVR) solutions under realistic traffic and charging conditions. Rather than proposing new EVR optimization algorithm like heuristics, meta-heuristics or dynamics, our contribution is a framework that ingests routing outputs produced by any EVR algorithm, exposes these outputs to microscopic traffic and energy models, and conducts distortion testing (e.g., traffic jams, customer removal, charging-station relocation) to assess robustness. The framework reports comparable performance metrics—distance, travel time, energy consumption, state of charge (SoC) trajectories, charging waits—and quantifies plan–execution gaps via statistical measures. We demonstrate the framework on a campus-scale case study. Results show how distance-optimal plans may become energy-suboptimal once regenerative braking limits, auxiliary loads, and charging constraints are enforced, and how seemingly favorable changes (e.g., moving a charger closer) can paradoxically reduce final SoC due to capacity and timing effects. The framework is algorithm-agnostic, transparent, and reproducible; it complements, rather than replaces, classical EVR models by providing an execution-level lens. We discuss threats to validity arising from scale and parameterization and outline how the framework generalizes to larger settings. Overall, the work provides a verification analysis for stress-testing EVR outputs before deployment.
Kahraman et al. (2026) studied this question.