The organic Rankine cycle power system is an emerging technology, which is able to recover the waste heat from the diesel engine of heavy-duty trucks and thus increase the overall engine efficiency. One of the major technical challenges for the integration of the organic Rankine cycle unit on-board trucks are the broad and rapid fluctuations of the available waste heat, caused by the unsteady driving conditions of the truck. Model predictive control has shown to be a powerful tool to ensure safe operation and optimal performance of the organic Rankine cycle unit on-board trucks. This paper presents a novel systematic method for the tuning of model predictive controllers based on a multi-objective optimization routine using a fourth-order reduced linear model. The objectives of the optimization are the settling time due to a step change of the exhaust gas mass flow rate and the cumulative controller effort due to measurement noise. The results suggest that a trade-off exists between the two objectives. Among the controller design parameters, the input rate weight has the largest influence on the controller performance. Inter- estingly, the simplified optimization procedure based on the reduced-order linear model of the organic Rankine cycle unit can provide key information about the controller performance based on a more complex nonlinear model of the organic Rankine cycle unit when subjected to a realistic waste heat profile. The results indicate that the settling time due to a step change of the exhaust gas mass flow rate is a good indicator of the absolute mean square tracking error over the profile, and it should not exceed 15 s for an absolute mean square tracking error below 2 K. On the other hand, the cumulative controller effort due to measurement noise is strongly correlated to the cumulative controller effort over the profile, and it should stay below 0.5 %/s for a cumulative controller effort over the whole profile below 2 %/s. The presented method is a powerful tool to help the control designer to find the optimal design parameters of model predictive controllers in a systematic way, in contrast to the time-consuming, experience-based trial and error methods     «

The organic Rankine cycle power system is an emerging technology, which is able to recover the waste heat from the diesel engine of heavy-duty trucks and thus increase the overall engine efficiency. One of the major technical challenges for the integration of the organic Rankine cycle unit on-board trucks are the broad and rapid fluctuations of the available waste heat, caused by the unsteady driving conditions of the truck. Model predictive control has shown to be a powerful tool to ensure s...     »