This paper presents a fast algorithm for nonlinear model predictive control. In real-time implementation, a nonlinear optimal problem is often rewritten as a nonlinear programming(NLP) problem using the Euler method, which is based on dividing the prediction horizon into N steps in a given time interval. However,real-time optimization is usually limited to slow processes, since the sampling time must be sufficient to support the task’s computational needs. In this study, by combining the Gauss pseudospectral method and model predictive control, a fast algorithm is proposed using fewer discrete points to transcribe an optimal control problem into an NLP problem while ensuring the same computational accuracy as traditional discretization methods.The approach is applied to the torque split control for hybrid electric vehicles(HEV) with a predefined torque demand, and its computational time is at least half that of the Euler method with the same accuracy. This paper presents a fast algorithm for nonlinear model predictive control. In real-time implementation, a nonlinear optimal problem is often rewritten as a nonlinear programming (NLP) problem using the Euler method, which is based on dividing the prediction horizon into N steps in However, real-time optimization is usually limited to slow processes, since the sampling time must be sufficient to support the task’s computational needs. In this study, by combining the Gauss pseudospectral method and model predictive control, a fast algorithm is proposed using fewer discrete points to transcribe an optimal control problem into an NLP problem while ensuring the same computational accuracy as traditional discretization methods. The approach is applied to the torque split control for hybrid electric vehicles (HEV) with a predefined torque demand, and its computational time is at least half that of the Euler method with the same accuracy.