Under the deep integration of novel power systems and electricity markets, vehicle-to-grid (V2G) aggregators face dual uncertainties, including fluctuating system-side settlement prices and stochastic user-side response behaviors in day-ahead operations. However, the existing studies often rely on single-source uncertainty assumptions, struggling to balance economic efficiency and robustness under incomplete information. To address this, we propose an info-gap decision theory (IGDT)-based robust pricing strategy for day-ahead V2G operations, offering profit-guaranteed pricing and bidding decisions for aggregators in information-scarce environments. The model maximizes expected profit while capturing a nonlinear participation mechanism that couples psychological perception of users with profit expectations, and employs McCormick envelopes to convert the non-deterministic optimization into a mixed-integer linear program for tractability. Case results reveal an optimal profit-sharing interval between aggregators and users without raising incentive costs, the method markedly reduces imbalance penalties and increases net profit. The strategy effectively balances return and risk, providing a novel decision framework for V2G aggregators operating amid information deficiency.