A Real-Time Model Predictive Controller for Power Control in Extended-Range Auxiliary Power Unit

In the power control process of the range extender, both the engine and the generator are at risk of reaching the torque output limit. The limitation not only comes from the restriction of their own torque output capacity, but also comes from the additional constraints that are established to avoid power reverse undershoot. In order to improve the system performance under time-varying torque constraints, a real-time model predictive controller (MPC) that manipulates the torque commands of the engine and the generator to track the reference power is designed. Different from the traditional penalty function method, the quadratic function is taken as the penalty function in this paper, which greatly improves the efficiency of solving the optimal solution. Meanwhile, the steady-state torque command in the feasible region is taken as the extreme point of the quadratic penalty function, which avoids the penalty coefficient tending to infinity. By adjusting the penalty coefficients of different constraints, the overall optimal solution approaches the boundary of the active inequality constraints. And according to the Karush-Kuhn-Tucker (KKT) condition, it is proved that the optimal solution of the original problem can be obtained through iteration. The effectiveness and practicability of the proposed strategy are evaluated through numerical simulations on Simulink and experiments on a range extender.