Model predictive control of a waste heat recovery system for automotive diesel engines

Feru, Emanuel; Willems, Frank; Jager, de Ag Bram; Steinbuch, M Maarten

doi:10.1109/icstcc.2014.6982492

Cited by 17 publications

(17 citation statements)

References 11 publications

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“…In this section, the switching MPC strategy from [18] is applied to the standalone WHR system with optimal component sizes. A comparison is made with the original WHR system.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Sizing of Waste Heat Recovery Systems for Dynamic Engine Conditions

Feru¹,

Goyal²,

Willems³

2018

Organic Rankine Cycle Technology for Heat Recovery

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In this study, a methodology for optimal sizing of waste heat recovery (WHR) systems is presented. It deals with dynamic engine conditions. This study focuses on Euro-VI truck applications with a mechanically coupled Organic Rankine Cycle-based WHR system. An alternating optimization architecture is developed for optimal system sizing and control of the WHR system. The sizing problem is formulated as a fuel consumption and system cost optimization problem using a newly developed, scalable WHR system model. Constraints related to safe WHR operation and system mass are included in this methodology. The components scaled in this study are the expander and the EGR and exhaust gas evaporators. The WHR system size is optimized over a hot World Harmonized Transient Cycle (WHTC), which consists of urban, rural and highway driving conditions. The optimal component sizes are found to vary for these different driving conditions. By implementing a switching model predictive control (MPC) strategy on the optimally sized WHR system, its performance is validated. The net fuel consumption is found to be reduced by 1.1% as compared to the originally sized WHR system over the total WHTC.

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“…In this section, the switching MPC strategy from [18] is applied to the standalone WHR system with optimal component sizes. A comparison is made with the original WHR system.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Sizing of Waste Heat Recovery Systems for Dynamic Engine Conditions

Feru¹,

Goyal²,

Willems³

2018

Organic Rankine Cycle Technology for Heat Recovery

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the literature, model-based control generally copes with the complexity of a reference model, such as the one described earlier, by linearization around an operating point [34] or a set of operating points [35]. However, [3] and [36] underline the need of considering the nonlinear behavior of the Rankine system for control design, as its static gains and response times strongly vary with the operating conditions.…”

Section: Nonlinear Control-oriented Modelmentioning

confidence: 99%

Organic Rankine Cycle for Vehicles: Control Design and Experimental Results

Peralez

Nadri

Dufour

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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The system considered here is an organic Rankine cycle for recovering waste heat from a heavy-duty diesel engine. Because of the highly transient conditions these systems are subject to, control plays a fundamental role to enable the viability and efficiency of those systems. In this context, this paper investigates the problem of control design for superheating (SH) and pressure at evaporator outlet. Based on a moving boundary heat exchanger model, a first controller, which consists of a dynamic feedforward combined to a gain-scheduled PID, is implemented on the pump speed to maintain the SH close to the set-point value. Experimental results illustrate the enhanced performance in terms of disturbance rejection. Then, a second controller based on nonlinear state estimation is proposed. This is a nonlinear feedback law, which allows to adjust the evaporating pressure to time-varying demand with a good accuracy.

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“…A constrained model predictive controller (MPC) was presented to control an ORC based WHR considering the system nonlinearities as well as inputs and outputs constraints [13]. In Reference [14], MPC was proposed for a WHR to track the optimal references obtained from a steady-state optimization, and an extended Kalman filter was employed for estimation of the model-plant mismatches. In light of the system nonlinearities, a switching model predictive control strategy was designed for the WHR [15].…”

Section: Introductionmentioning

confidence: 99%

Generalized Correntropy Predictive Control for Waste Heat Recovery Systems Based on Organic Rankine Cycle

et al. 2019

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Organic Rankine cycle (ORC) is one of the most promising technologies to recover energy from low temperature waste heat. The heat sources usually experience fluctuations in temperature and mass flow rate, which makes it difficult to obtain satisfactory control performances of ORC systems. In this paper, a single neuron adaptive multi-step predictive control scheme is developed for an ORC based waste heat recovery (WHR) system with non-Gaussian disturbances. Since the non-Gaussian disturbances existed in WHR system follow heavy-tailed distribution, generalized correntropy is adopted as the performance index to characterize the system uncertainties. The weights of the single neuron controller are updated by optimizing the performance function. The whole implementation procedures of the proposed control strategy for the WHR are presented. As a contrast, the performances of the minimum error entropy (MEE) based controller and the mean square error (MSE) based controller are also tested. The proposed control scheme is confirmed to be more effective through simulations. INDEX TERMS ORC, waste heat recovery, generalized correntropy, single neuron adaptive multi-step predictive control, non-Gaussian.

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Model predictive control of a waste heat recovery system for automotive diesel engines

Cited by 17 publications

References 11 publications

Optimal Sizing of Waste Heat Recovery Systems for Dynamic Engine Conditions

Optimal Sizing of Waste Heat Recovery Systems for Dynamic Engine Conditions

Organic Rankine Cycle for Vehicles: Control Design and Experimental Results

Generalized Correntropy Predictive Control for Waste Heat Recovery Systems Based on Organic Rankine Cycle

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