Improving the Control Performance of an Organic Rankine Cycle System for Waste Heat Recovery from a Heavy-Duty Diesel Engine using a Model-Based Approach

Johan Peralez 1 Paolino Tona 1, * Olivier Lepreux 1 Antonio Sciarretta 1 Luce Voise 2 Pascal Dufour 3 Madiha Nadri 3
* Auteur correspondant
1 IFPEN - IFP Energies nouvelles
2 D2T Powertrain Engineering
3 LAGEP - Laboratoire d'automatique et de génie des procédés
Abstract : In recent years, waste heat recovery (WHR) systems based on Rankine cycles have been the focus of intensive research for transport applications, as they seem to offer considerable potential for fuel consumption reduction. Because of the highly transient conditions they are subject to, control plays a fundamental role to enable viability and efficiency of those systems. The system considered here is an Organic Rankine Cycle (ORC) for recovering waste heat from a heavy-duty diesel engine. For this system, a hierarchical and modular control structure has been designed, implemented and validated experimentally on an engine testbed cell. The paper focuses more particularly on improving the baseline control strategy using a model-based approach. The improvements come from an extensive system identification campaign allowing model-based tuning of PID controllers and, more particularly, from a dynamic feedforward term computed from a nonlinear reduced model of the high-pressure part of the system. Experimental results illustrate the enhanced performance in terms of disturbance rejection.
Keywords : waste heat recovery organic rankine cycle automotive heavy-duty vehicles moving boundary modeling nonlinear control
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Communication dans un congrès
2013 IEEE Conference on Decision and Control (CDC), Dec 2013, Florence, Italy. 7 p., 2013
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Dernière modification le : jeudi 7 février 2019 - 16:42:40
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Johan Peralez, Paolino Tona, Olivier Lepreux, Antonio Sciarretta, Luce Voise, et al.. Improving the Control Performance of an Organic Rankine Cycle System for Waste Heat Recovery from a Heavy-Duty Diesel Engine using a Model-Based Approach. 2013 IEEE Conference on Decision and Control (CDC), Dec 2013, Florence, Italy. 7 p., 2013. 〈hal-00875469〉

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