433247 A Generic Framework for the Robust Control of Fuel Cell Energy System

Wednesday, November 11, 2015: 10:43 AM
Salon E (Salt Lake Marriott Downtown at City Creek)
Amit M. Manthanwar, Imperial College London, London, United Kingdom and Efstratios N. Pistikopoulos, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX

A Generic Framework for the Robust Control of Fuel Cell Energy System

Amit M. Manthanwar*† and Efstratios N. Pistikopoulos

Department of Chemical Engineering, Imperial College London, England, UK

Artie McFerrin Department of Chemical Engineering, Texas A&M University, USA


Targeted Session: 10B05 Modeling, Control and Optimization of Energy Systems

With hydrogen envisioned as an energy carrier, fuel cell is the sustainable energy enabling technology. Amongst all fuel cell types, a polymer electrolyte membrane fuel cell is the most widely researched fuel cell type. Operating at low temperature and having higher energy conversion efficiency compared to an internal combustion engine, it offers distinct benefits and has found widespread adoption in many areas including the transport and stationary energy sector. Regardless of application and the type of fuel cell used, balance of plant system integration brings additional challenges affecting durability and performance of the overall system, [1]. Although the fuel cell stack technology has reached a level of maturity by approaching a target cost of $30 per kW to be competitive against internal combustion engines, systemwide control automation for better electricity, water and thermal management still remains the main research priorities. The majority of fuel cell system failures and forced outages (~90%) are due to lack of system integration and are the result of balance of plant events, [2]. These statistics reinforce the need for better system integration and more efficient controller design. Therefore three important aspects for improving fuel cell systems are: (a) reducing manufacturing and operational costs; (b) improving safety, durability and reliability; (c) improving operational performance by efficient controller design. These

three aspects are intertwined and the effective modelling, optimisation and control automation strategy plays a key role in addressing operational challenges and significantly improving system performance, durability and operational costs.

The real time control of fuel cell system using a well established technology of model predictive

control requires an accurate mathematical model of the system. The dynamic phenomenon taking place inside fuel cell involves complex interaction of mass transport, energy transport and electrochemical kinetics. These uncertain dynamics determine the overall operational performance of the system. In addition, the real time control requires computing machinery to run the optimisation algorithms. This compounds the manufacturing and operational costs. After a decade long theoretical research carried out in the area of mathematical optimisation and control theory, [3], we are poised to make a significant contribution of the developed theory applied to the energy systems applications, in particular to the experimental investigation of fuel cells at multiple scales. This work presents a generic framework for the development of efficient fuel cell controllers by integrating high fidelity mathematical models of the fuel cell system and integrated subsystems with online and offline model-based predictive control algorithms. Additionally, research presents the development of state-of-the-art testing and validation facility for characterising various designs as well as benchmarking operational policies of the polymer electrolyte membrane fuel cell system operating under uncertainty. Research envisions to further adapt and deploy in real-time the advanced optimisation and control algorithms to the various energy systems applications.


[1] C. Ziogou, E. N. Pistikopoulos, M. C. Georgiadis, S. Voutetakis, and S. Papadopoulou, “Empowering the performance of advanced nmpc by multiparametric programming–an application to a pem fuel cell system,” Ind. Eng. Chem. Res., vol. 52, pp. 4863–4873, Mar. 2013.

[2] K. Wipke, S. Sprik, J. Kurtz, T. Ramsden, C. Ainscough, and G. Saur, “Final results from u.s. fcev learning demonstration,” in EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, (Los Angeles, California), May 6–9 2012.

[3] E. N. Pistikopoulos, “From multi-parametric programming theory to mpc-on-a-chip multi-scale systems applications,” Computers and Chemical Engineering, no. 0, 2012. 

*The financial support from EPSRC grants (EP/I014640/1 and EP/K503381/1) and sponsorships from ARM Incorporation is gratefully acknowledged. Corresponding author: amit@imperial.ac.uk

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