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Model-Based Predictive Control of Integrated Fuel Cell Systems—From Design to Implementation

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Advances in Energy Systems Engineering

Abstract

Fuel cell systems are a promising alternative to traditional power sources for a wide range of portable, automotive and stationary applications and have an increasing potential for wider use as the demand for clean energy is increasing and the focus is shifting towards renewable energy generation. This chapter has a multidisciplinary scope, the design of a computer-aided framework for monitoring and operation of integrated fuel cell systems and the development of advanced model-based control schemes. The behavior of the framework is experimentally verified through the online deployment to an automated small-scale fuel cell unit, demonstrating excellent response in terms of computational effort and accuracy with respect to the control objectives.

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References

  • A European strategic energy technology plan (SET Plan). (2007). Towards a low carbon future COM(2007) 723.

    Google Scholar 

  • Ahn, J., & Choe, S. (2008). Coolant controls of a PEM fuel cell system. Journal of Power Sources, 179, 252–264.

    Article  Google Scholar 

  • Bauer, M., & Craig, I. K. (2008). Economic assessment of advanced process control—A survey and framework. Journal of Process Control, 18(1), 2–18.

    Google Scholar 

  • Bavarian, M., Soroush, M., Kevrekidis, I., & Benziger, J. (2010). Mathematical modeling, steady-state and dynamic behavior, and control of fuel cells: A review. Industrial and Engineering Chemistry Research, 49, 7922–7950.

    Article  Google Scholar 

  • Bemporad, A., Morari, M., Dua, V., & Pistikopoulos, E. N. (2002). The explicit linear quadratic regulator for constrained systems. Automatica, 38, 3–20.

    Article  MathSciNet  MATH  Google Scholar 

  • Benson, H. Y., & Shanno, D. F. (2008). Interior-point methods for nonconvex nonlinear programming: regularization and warmstarts. Computational Optimization and Applications, 40, 143–189.

    Article  MathSciNet  MATH  Google Scholar 

  • Betts, J. T. (2001). Practical methods for optimal control and estimation using nonlinear programming. Advances in design and control (Vol. 19). Philadelphia: SIAM.

    Google Scholar 

  • Biegler, L. T., & Grossmann, I. E. (2004). Part I: Retrospective on optimization. Computers & Chemical Engineering, 28(8), 1169–1192.

    Article  Google Scholar 

  • Biegler, L. T., Cervantes, A. M., & Wachter, A. (2002). Advances in simultaneous strategies for dynamic process optimization. Chemical Engineering Science, 57, 575–593.

    Article  Google Scholar 

  • CSS. (2011). The impact of control technology. In T. Samad., & A.M. Annaswamy (eds.) IEEE control systems society. Retrieved December 2015 from www.ieeecss.org.

  • Diehl, M., Bock, H., Schloder, J., Findeisen, R., Nagy, Z., & Allgöwer, F. (2002). Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. Journal of Process Control, 12(4), 577–585.

    Article  Google Scholar 

  • Diehl, M., Ferreau, H. J., & Haverbeke, N. (2009). Efficient numerical methods for nonlinear MPC and moving horizon estimation. In Nonlinear model predictive control (pp. 391–417). Berlin Heidelberg: Springer.

    Google Scholar 

  • Domahidi, A., Zgraggen, A. U., Zeilinger, M. N., Morari, M., Jones, C. N. (2012). Efficient interior point methods for multistage problems arising in receding horizon control. In IEEE 51st Annual Conference on Decision and Control (CDC) 10–13 December 2012 (pp. 668, 674).

    Google Scholar 

  • Dua, V., Bozinis, N. A., & Pistikopoulos, E. N. (2002). A multiparametric programming approach for mixed-integer quadratic engineering problems. Computers and Chemical Engineering, 26, 715–733.

    Google Scholar 

  • Engell, S. (2007). Feedback control for optimal process operation. Journal of Process Control, 17, 203–219.

    Article  Google Scholar 

  • Eqtami, A., Dimarogonas, D. V., & Kyriakopoulos, K. J. (2011). Novel event-triggered strategies for model predictive controllers. In 50th IEEE conference on decision and control and european control conference (CDC-ECC), 12−15 December 2011 (pp. 3392–3397).

    Google Scholar 

  • European Commission (2011). A roadmap for moving to a competitive low carbon economy in 2050. In COM 112, Brussels.

    Google Scholar 

  • Ferreau, H. J., Bock, H. G., & Diehl, M. (2008). An online active set strategy to overcome the limitations of explicit MPC. International Journal of Robust and Nonlinear Control, 18(8), 816–830.

    Article  MathSciNet  MATH  Google Scholar 

  • Findeisen, R., Allgöwer, F., & Biegler, L. T. (2007). Assessment and future directions of nonlinear model predictive control. In Lecture Notes in Control and Information Sciences (Vol 358). Verlag, Berlin: Springer.

    Google Scholar 

  • Finlayson, B. (1992). Numerical methods for problems with moving fronts. Ravenna Park Publishing.

    Google Scholar 

  • Fuel Cell Today. (2012). The Fuel Cell Industry Review. Retrieved December 2015 from http://www.fuelcelltoday.com.

  • Kameswaran, S., & Biegler, L. T. (2008). Convergence rates for direct transcription of optimal control problems using collocation at Radau points. Computational Optimization and Applications, 41(1), 81–126.

    Article  MathSciNet  MATH  Google Scholar 

  • Kvasnica, M., Grieder, P., Baotic, M., & Morari, M. (2004). Multi-Parametric Toolbox (MPT) (pp. 448–462).

    Google Scholar 

  • Magni, L., Raimondo, D., & Allgöwer, F. (2009). Nonlinear model predictive control: Towards new challenging applications. In Lecture Notes in Control and Information Sciences (Vol. 384). Berlin: Springer Verlag.

    Google Scholar 

  • Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M. (2000). Constrained model predictive control: Stability and optimality. Automatica, 36, 789–814.

    Article  MathSciNet  MATH  Google Scholar 

  • Murtagh, B. A., Saunders, M. A. (1998). MINOS 5.5 User’s Guide. Technical report SOL 83–20R, Stanford University.

    Google Scholar 

  • Pannocchia, G., Rawlings, J. B., & Wright, S. J. (2007). Fast, large-scale model predictive control by partial enumeration. Automatica, 43, 852–860.

    Article  MathSciNet  MATH  Google Scholar 

  • Patrinos, P., & Bemporad, A. (2012). An accelerated dual gradient-projection algorithm for linear model predictive control. In IEEE 51st Annual Conference on Decision and Control (CDC) 10–13 December 2012 (pp. 662, 667).

    Google Scholar 

  • Pistikopoulos, E. N. (2012). From multi-parametric programming theory to MPC-on-a-chip multi-scale systems applications. Computers & Chemical Engineering, 47, 57–66.

    Article  Google Scholar 

  • Pistikopoulos, E. N., Dua, V., Bozinis, N. A., Bemporad, A., & Morari, M. (2002). On-line optimization via off-line parametric optimization tools. Computers & Chemical Engineering, 26(2), 175–185.

    Article  Google Scholar 

  • Pistikopoulos, E. N., Georgiadis, M., & Dua, V. (2007). Multi-parametric model-based control: theory and applications. Weinheim: Wiley-VCH.

    Book  Google Scholar 

  • POP. (2007). Parametric optimization solutions (ParOS) Ltd.

    Google Scholar 

  • Pukrushpan, J. T., Peng, H., & Stefanopoulou, A. G. (2004). Control-oriented modeling and analysis for automotive fuel cell systems. Journal of Dynamic Systems Measurement and Control-Transactions of the ASME, 126(1), 14–25.

    Article  Google Scholar 

  • Qin, S., & Badgwell, T. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7), 733–764.

    Google Scholar 

  • Raimondo, D. M., Riverso, S., Summers, S., Jones, C., Lygeros, J., & Morari, M. (2012). A set-theoretic method for verifying feasibility of a fast explicit nonlinear model predictive controller. Distributed Decision Making and Control, LNCIS, 417, 289–311.

    Article  MathSciNet  MATH  Google Scholar 

  • Rawlings, J. B., & Mayne, D. Q. (2009). Model predictive control: Theory and design. Madison: Nob Hill Publishing.

    Google Scholar 

  • Reble, M., & Allgöwer, F. (2012). Unconstrained model predictive control and suboptimality estimates for nonlinear continuous-time systems. Automatica, 48(8), 1812–1817.

    Article  MathSciNet  MATH  Google Scholar 

  • Schafer, A., Kuehl, P. Baschuk, N., Diehl, M., Schloder, J., & Bock, H. (2007). Fast reduced multiple shooting methods for nonlinear model predictive control. Chemical Engineering and Processing, 46(11), 1200–1214.

    Article  Google Scholar 

  • Schmittinger, W., & Vahidi, A. (2008). A review of the main parameters influencing long-term performance and durability of PEM fuel cells. Journal of Power Sources, 180, 1–14.

    Article  Google Scholar 

  • Stefanopoulou, A. G., & Suh, K. W. (2007). Mechatronics in fuel cell systems. Control Engineering Practice, 15, 277–289.

    Article  Google Scholar 

  • Tamimi, J., & Li, P. (2010). A combined approach to nonlinear model predictive control of fast systems. Journal of Process Control, 20(9), 1092–1102.

    Article  Google Scholar 

  • Wang, Y., & Boyd, S. (2010). Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology, 18(2), 267–278.

    Article  Google Scholar 

  • Yang, X., & Biegler, L. T. (2012). Advanced-multi-step Nonlinear Model Predictive Control. In 8th IFAC Symposium on Advanced Control of Chemical Processes, Singapore, July 10−13, (pp. 426–431).

    Google Scholar 

  • Zavala, V. M., & Biegler, L. T. (2009). The advanced step NMPC controller: Optimality. Stability and Robustness. Automatica, 45(1), 86–93.

    MathSciNet  MATH  Google Scholar 

  • Zeilinger, M. N., Jones, C. N., & Morari, M. (2011). Real-Time suboptimal model predictive control using a combination of explicit MPC and online optimization. IEEE Transactions on Control Systems Technology, 56(7), 1524–1534.

    MathSciNet  Google Scholar 

  • Ziogou, C., Pistikopoulos, E. N., Georgiadis, M. C., Voutetakis, S., & Papadopoulou, S. (2013a). Empowering the performance of advanced NMPC by multi-parametric programming—An application to a PEM fuel cell system. Industrial Engineering and Chemistry Research, 52(13), 4863–4873.

    Article  Google Scholar 

  • Ziogou, C., Papadopoulou, S., Georgiadis, M. C., & Voutetakis, S. (2013b). On-line nonlinear model predictive control of a PEM fuel cell system. Journal of Process Control, 23(4), 483–492.

    Article  Google Scholar 

  • Ziogou C., Panos C., Kouramas K., Papadopoulou S., Georgiadis M. C., Voutetakis S., et al. (2011a). Multi-parametric model predictive control of an automated integrated fuel cell testing unit. Computer Aided Chemical Engineering, 29, 744–747.

    Google Scholar 

  • Ziogou, C., Voutetakis, S., Papadopoulou, S., & Georgiadis, M. C. (2011b). Modeling simulation and validation of a PEM fuel cell system. Computers and Chemical Engineering, 35, 1886–1900.

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Research and Technology Hellas (CERTH), Chemical Process and Energy Resources Institute (CPERI), Thessaloniki, Greece

    Chrysovalantou Ziogou & Spyros Voutetakis

  2. Department of Automation Engineering, Alexander Technological Educational Institute of Thessaloniki, Thessaloniki, Greece

    Simira Papadopoulou

  3. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA

    Efstratios Pistikopoulos

  4. Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Michael Georgiadis

Authors
  1. Chrysovalantou Ziogou
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  2. Simira Papadopoulou
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  3. Efstratios Pistikopoulos
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  4. Michael Georgiadis
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  5. Spyros Voutetakis
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Corresponding author

Correspondence to Chrysovalantou Ziogou .

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Editors and Affiliations

  1. Cranfield University, Bedfordshire, United Kingdom

    Georgios M. Kopanos

  2. Tsinghua University, Beijing, China

    Pei Liu

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Michael C. Georgiadis

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Ziogou, C., Papadopoulou, S., Pistikopoulos, E., Georgiadis, M., Voutetakis, S. (2017). Model-Based Predictive Control of Integrated Fuel Cell Systems—From Design to Implementation. In: Kopanos, G., Liu, P., Georgiadis, M. (eds) Advances in Energy Systems Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-42803-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-42803-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42802-4

  • Online ISBN: 978-3-319-42803-1

  • eBook Packages: EnergyEnergy (R0)

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