Nonlinear model predictive control of an Organic Rankine Cycle for exhaust waste heat recovery in automotive engines

Energy recovery from exhaust gas waste heat can be regarded as an effective way to improve the energy efficiency of automotive powertrains, thus reducing CO2 emissions. The application of Organic Rankine Cycles (ORCs) to waste heat recovery is a solution that couples effectiveness and reasonably low technological risks. On the other hand, ORC plants are rather complex to design, integrate and control, due to the presence of heat exchangers operating with phase changing fluid, and several control devices to regulate the thermodynamic states of the systems. Furthermore, the power output and efficiency of ORC systems are extremely sensitive to the operating conditions, requiring precise control of the evaporator pressure and superheat temperature. This paper presents an optimization and control design study for an Organic Rankine Cycle plant for automotive engine waste heat recovery. The analysis has been developed using a detailed Moving Boundary Model that predicts mass and energy flows through the heat exchangers, valves, pumps and expander, as well as the system performance. Starting from the model results, a nonlinear model predictive controller is designed to optimize the transient response of the ORC system. Simulation results for an acceleration-deceleration test illustrate the benefits of the proposed control strategy.