<p>Accurate power demand estimation is essential for improving vehicle control precision, optimizing power distribution, and enhancing energy management, with torque estimation being a critical component. However, the ideal longitudinal dynamics model ignores the actual running state of the vehicle, resulting in a large error between the calculated and actual values. To address this issue, this study proposes a data-driven real-time torque estimation framework that balances accuracy and computational efficiency for new energy buses. Key features include an online wheel radius identification approach using the least squares method, a speed-slip compensation strategy based on the extreme learning machine, and a dynamic road slope correction technique. Validation using real-world driving data demonstrates that the proposed method reduces vehicle speed RMSE by 58.45% and demand torque RMSE by 34.77%, compared to models without parameter identification.</p>