11:40
Session 16: Advance control strategies
Chair: Thomas Reiche
11:40
20 mins
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TOWARDS THE OPTIMAL OPERATION OF AN ORGANIC RANKINE CYCLE UNIT BY MEANS OF MODEL PREDICTIVE CONTROL
Andres Hernandez, Adriano Desideri, Clara Ionescu, Sylvain Quoilin, Vincent Lemort, Robin De Keyser
Abstract: In this paper the optimal operation of an Organic Rankine Cycle (ORC) unit is investigated both in terms of energy production and safety conditions. Simulations on a validated dynamic model of a real regenerative ORC unit, are used to illustrate the existence of an optimal evaporating temperature which maximizes energy production for some given heat source conditions. This idea is further extended using a perturbation based Extremum Seeking (ES) algorithm to find online the optimal evaporating temperature. Regarding safety conditions we propose the use of the Extended Prediction Self-Adaptive Control (EPSAC) approach to constrained Model Predictive Control (MPC). Since it uses input/output models for prediction, it avoids the need of state estimators, making of it a suitable tool for industrial applications. The performance of the proposed control strategy is compared to PID-like schemes. Results show that EPSAC-MPC is a more effective control strategy as it allows a safer and more efficient operation of the ORC unit, as it can handle constraints in a natural way, operating close to the boundary conditions where power generation is maximized.
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12:00
20 mins
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OPTIMAL CONTROL OF WASTE HEAT RECOVERY SYSTEMS APPLYING NONLINEAR MODEL PREDICTIVE CONTROL (NMPC)
Philipp Petr, Christian Schröder, Jürgen Köhler, Manuel Gräber
Abstract: This article describes an attempt for a real-time optimization of the net power output of an add-on Organic Rankine Cycle (ORC) system of a vehicle applying Nonlinear Model Predictive Control (NMPC). Therefore, a Modelica model library for satisfactorily accurate, fast vehicle and ORC models was developed. By means of the developed tool chain involving the optimizer MUSCOD II by the IWR Heidelberg, virtual simulation experiments of a waste heat recovery system for a long-distance bus could be realized. Results show an increase of the net power output of 7 % in part load engine operation in the European Transient Cycle compared to a conventional controller with optimum operation points optimized at steady-state conditions.
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12:20
20 mins
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WASTE HEAT RECOVERY RANKINE BASED SYSTEM MODELING FOR HEAVY DUTY TRUCKS FUEL ECONOMY ASSESSMENT
Vincent Grelet, Vincent Lemort, Madiha Nadri, Pascal Dufour, Thomas Reiche
Abstract: Even in nowadays heavy duty (HD) engines which can reach 45% of efficiency, a high amount of energy is released as heat to the ambient. The increase in oil prices compels manufacturers to focus on new solutions to improve fuel efficiency of truck powertrain such as Waste Heat Recovery Systems (WHRS) [1]. Over the last few years, a lot of studies have proven that there are a lot of hurdles (cooling margin, expansion machines, control, …) [2 , 3] for a perfect match of such a system with a vehicle. This paper intends to present realistic fuel economy figures over dynamic driving cycle representative of a real long haul truck usage. The system is optimized to minimize the total vehicle fuel consumption taking into account the different penalties induced by a waste heat recovery Rankine based system installation into a truck. This analysis present and quantify all these penalties thanks to simulation model representing the engine, the vehicle driveline, the cooling system and the WHRS. This new simulation tool allows to analyse the fuel consumption under various boundary conditions (e.g. ambient conditions, different driving cycles). It also shows the importance of the application when designing WHRS and yields to a better understanding when it comes to a vehicle integration of a Rankine cycle into a truck. Means of improvements are presented and discussed, since the base fuel saving figures presented could be lower to what is usually found in the literature [4] and the design and validation of WHRS components are based on prototypes and do not represent the optimum in terms of components sizing and transient performances.
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12:40
20 mins
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MODEL BASED CONTROL FOR WASTE HEAT RECOVERY HEAT EXCHANGERS RANKINE CYCLE BASE SYSTEM IN HEAVY DUTY TRUCKS
Vincent Grelet, Pascal Dufour, Madiha Nadri, Vincent Lemort, Thomas Reiche
Abstract: Driven by future emissions legislations and increase in fuel prices engine gas heat recovering has recently attracted a lot of interest. In the past few years a high number of studies have shown the interest of energy recovery Rankine based systems for heavy duty trucks engine compounding [1]. Recent studies have brought a significant potential for such a system in a Heavy Duty (HD) vehicle, which can lead to a decrease in fuel consumption of about 5% and reduce engine emissions but many challenges still need to be faced before the vehicle integration.
The system dynamics is mainly controlled by the heat exchangers behavior (i.e. evaporators and condenser) and dynamic models of these components are of two kinds: Moving Boundary (MV) and Finite Volume (FV). Both approaches have been widely used in large power recovery system and control system design [2] and results in a simplification of the heat recovery boiler/condenser geometry in a great extent (i.e. by representing the boiler by a straight pipe in pipe counterflow heat exchanger). The heat exchanger modeling methodology used in the following and its validation is approached in [3].
This paper presents a control oriented model development for waste heat recovery Rankine based control systems in heavy duty trucks. Due to the highly transient operating conditions, improving the control strategy of those systems is an important step to their integration into a vehicle. It is shown here that moving to more advanced control strategies than classical PIDs lead to some gains in terms of robustness and accuracy. Due to the limited availability and high operational cost of test bench the validation of such a controller is done on a representative and previously validated model [3].
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