We present QFedFormer, a federated transformer for dynamic electric vehicle (EV)-charging price prediction that combines quantization-aware training, SHAP-guided explainability, and blockchain-based incentives. The framework trains across distributed charging stations without centralizing user data, and programmable contracts set tariffs from forecasted demand and user-declared flexibility, while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs. On a real-world dataset, QFedFormer attains an energy-demand RMSE of $1.82\pm 0.02$ kWh and a tariff RMSE of $11.83\pm 0.10$ KRW/kWh (MAPE $2.7\pm 0.2\mathrm{\%}$) in the non-private baseline, outperforming FedAvg and Block-FeDL by $14.1\mathrm{\%}$ and $9.5\mathrm{\%}$, respectively. Under client-level differential privacy (DP) with $(\sigma =1.6,C=1,p=0.1,{\delta }_{\mathrm{D}\mathrm{P}}={10}^{-5})$, QFedFormer achieves $(\epsilon =2.0,{\delta }_{\mathrm{D}\mathrm{P}}={10}^{-5})$ after 50 rounds under a Rényi accountant, with forecast accuracy degrading modestly to $1.95$ kWh RMSE ( $\sim$7.1% relative increase vs. non-private baseline). Blockchain evaluation shows an average audit latency of $58$ ms per audit round, while a permissioned Ethereum-compatible deployment sustains more than $500$ client updates per minute with gas costs of $\sim$$0.039/client per audit round. These results indicate that QFedFormer enables accurate, privacy-preserving, and auditable coordination of EV–grid interactions, offering both regulators and service providers a practical deployment pathway.