Against the backdrop of the rapid advancement of new power systems and electricity markets, Vehicle-to-Grid (V2G) aggregators face dual uncertainties in day-ahead operations, namely fluctuations in settlement prices on the system side and stochastic response behaviors on the user side. To address this, this paper proposes a robust V2G day-ahead pricing strategy based on Info-Gap Decision Theory (IGDT), aiming to provide reliable pricing and bidding decision support for aggregators under incomplete information. The strategy adopts a benefit-guaranteeing IGDT formulation with the objective of maximizing the aggregator’s expected profit, and incorporates a nonlinear participation behavior model that couples user psychological perception with revenue expectations. By employing McCormick envelopes, the complex non-deterministic optimization problem is reformulated into a solvable Mixed-Integer Linear Programming model. Case studies demonstrate that there exists an optimal range for the profit-sharing ratio between aggregators and users. Compared with multiple heuristic pricing methods, the proposed strategy can significantly reduce the share of deviation penalties and improve net profits without increasing incentive expenditures. Moreover, the aggregator’s prof-itability is found to be most sensitive to peak-period settlement prices, while the impacts of valley-period dispatch volumes and station capacity expansion exhibit diminishing marginal effects.