Nonlinear model predictive control of organic Rankine cycles for automotive waste heat recovery: Is it worth the effort?

Using organic Rankine cycles (ORC) for waste heat recovery in vehicles promises significant reductions fuel consumption. Controlling the cycle, however, is difficult due to highly transient exhaust gas conditions. To tackle this issue, nonlinear model predictive control (NMPC) has been proposed and approximate NMPC solutions have investigated reduce computational demand. Herein, we compare (i) an idealized economic (eNMPC) scheme as a benchmark (ii) enforcing minimal superheat (iii) PI controller with dynamic feed-forward term (PI-ff) case study disturbances. We show that, ORC system supersonic turbine, problem can be reduced single-input single-output tracking combined decoupled steady-state real-time optimization (RTO) of turbine operation, assuming condenser. Our results indicate that provides good performance negligible losses average power compared full solution even PI-ff only marginal model-based controllers.