Journal Article

FZJ-2021-00509

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Nonlinear model predictive control of organic Rankine cycles for automotive waste heat recovery: Is it worth the effort?

Vaupel, Y.RWTH* ; Schulze, J. C.RWTH* ; Mhamdi, A.RWTH* ; Mitsos, A. (Corresponding author)FZJ*RWTH*

2021
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/26849  doi:10.1016/j.jprocont.2021.01.003

Abstract: Using organic Rankine cycles (ORC) for waste heat recovery in vehicles promises significant reductions in fuel consumption. Controlling the organic Rankine cycle, however, is difficult due to the highly transient exhaust gas conditions. To tackle this issue, nonlinear model predictive control (NMPC) has been proposed and approximate NMPC solutions have been investigated to reduce computational demand. Herein, we compare (i) an idealized economic NMPC (eNMPC) scheme as a benchmark to (ii) a NMPC enforcing minimal superheat and (iii) a PI controller with dynamic feed-forward term (PI-ff) in a control case study with highly transient disturbances. We show that, for an ORC system with supersonic turbine, the economic control problem can be reduced to a single-input single-output superheat tracking problem combined with a decoupled steady-state real-time optimization (RTO) of turbine operation, assuming an idealized condenser. Our results indicate that the NMPC enforcing minimal superheat provides good control performance with negligible losses in average power compared to the full solution of the economic NMPC problem and that even PI-ff only results in marginal losses in average power compared to the model-based controllers.

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Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112) (POF4-112)

Appears in the scientific report 2021

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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