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

Peralez, Johan; Tona, Paolino; Lepreux, Olivier; Sciarretta, Antonio; Voise, Luce; Dufour, Pascal; Nadri, Madiha

doi:10.1109/cdc.2013.6760971

Cited by 41 publications

(33 citation statements)

References 14 publications

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“…Simulated results verified that the control scheme that follows the optimal evaporation temperature trajectory exhibited superior performance. Peralez et al [96] used a model based control scheme considering system inversion for the control of the superheating temperature which affects both cycle performance and system safety. The model inversion introduces a feedforward action in addition to the feedback controller to effectively compensate for disturbances in the evaporator.…”

Section: Control Approachesmentioning

confidence: 99%

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

2015

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Efficient power generation from low to medium grade heat is an important challenge to be addressed to ensure a sustainable energy future. Organic Rankine Cycles (ORCs) constitute an important enabling technology and their research and development has emerged as a very active research field over the past decade. Particular focus areas include working fluid selection and cycle design to achieve efficient heat to power conversions for diverse hot fluid streams associated with geothermal, solar or waste heat sources. Recently, a number of approaches have been developed that address the systematic selection of efficient working fluids as well as the design, integration and control of ORCs. This paper presents a review of emerging approaches with a particular emphasis on computer-aided design methods.

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Section: Control Approachesmentioning

confidence: 99%

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

2015

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“…The main problem when considering PI or PID control structure is that identification for heat exchangers is very sensitive to the inputs considered [Peralez et al (2013), Horst et al (2013)]. This is well described when considering several operating points.…”

Section: Pi Controllermentioning

confidence: 99%

“…5). Details of nonlinear inversion are approached and detailed in [Peralez et al (2013)]. To simplify the model inversion, the heat transfer coefficient for the WF is assumed constant in liquid and vapor phase.…”

Section: Nonlinear Model Inversionmentioning

confidence: 99%

“…To deal with that, an effective control strategy is really important to maximize power recuperation and ensure a safe operation of the system. Although many papers concerning Rankine components and system optimization for mobile application can be found in the literature [Seher et al (2012), Mavridou et al (2010)], only few of them deal with control development and operating strategy improvement [Peralez et al (2013), Willems et al (2012)]. One key variable to control is the working fluid temperature at the evaporator outlet / expander inlet since this temperature has a big impact on system performance [Quoilin et al (2011)].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and control of Rankine based waste heat recovery systems for heavy duty trucks

et al. 2015

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This paper presents a control oriented model development for waste heat recovery Rankine based control systems in heavy duty trucks. Waste heat recovery systems, such as Rankine cycle, are promising solutions to improve the fuel efficiency of heavy duty engines. Due to the highly transient operating conditions, improving the control strategy of those systems is an important step to their integration into a vehicle. The system considered here is recovering heat from both EGR and exhaust in a serial arrangement and use a mixture of water and ethanol as working fluid. The paper focuses on a comparison of a classical PID controller which is the state of the art in the automotive industry and a nonlinear model based controller in a simulation environment. The nonlinear model based controller shows better performance than the PID one and ensures safe operation of the system.

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“…This is due to highly dynamic engine conditions, to interaction between the engine and the WHR system and to constraints, namely actuator limitations and safe operation. The control strategies dedicated to automotive applications are mostly based on a proportional-integral-derivative (PID) control type of approach [7][8][9][10][11][12]. However, this approach is most well established for single-input single-output systems.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of a Parallel Waste Heat Recovery System for Euro-VI Heavy-Duty Diesel Engines

et al. 2014

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This paper presents the modeling and control of a waste heat recovery system for a Euro-VI heavy-duty truck engine. The considered waste heat recovery system consists of two parallel evaporators with expander and pumps mechanically coupled to the engine crankshaft. Compared to previous work, the waste heat recovery system modeling is improved by including evaporator models that combine the finite difference modeling approach with a moving boundary one. Over a specific cycle, the steady-state and dynamic temperature prediction accuracy improved on average by 2% and 7%. From a control design perspective, the objective is to maximize the waste heat recovery system output power. However, for safe system operation, the vapor state needs to be maintained before the expander under highly dynamic engine disturbances. To achieve this, a switching model predictive control strategy is developed. The proposed control strategy performance is demonstrated using the high-fidelity waste heat recovery system model subject to measured disturbances from an Euro-VI heavy-duty diesel engine. Simulations are performed using a cold-start World Harmonized Transient cycle that covers typical urban, rural and highway driving conditions. The model predictive control strategy provides 15% more time in vapor and recovered thermal energy than a classical proportional-integral (PI) control strategy. In the case that the model is accurately known, the proposed control strategy performance can be improved by 10% in terms of time in vapor and recovered thermal energy. This is demonstrated with an offline nonlinear model predictive control strategy.Energies 2014, 7 6572

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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

Cited by 41 publications

References 14 publications

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

Modeling and control of Rankine based waste heat recovery systems for heavy duty trucks

Modeling and Control of a Parallel Waste Heat Recovery System for Euro-VI Heavy-Duty Diesel Engines

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