Data-Driven Real-Time Estimation Method for Demand Torque of New Energy Buses

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.