Constrained control and optimization of tubular solid oxide fuel cells for extending cell lifetime

Abstract

Extending fuel cell lifetime is a necessary objective for reducing fuel cell power generation cost of electricity. Capital costs comprise the most significant fraction of the cost of electricity. Reducing the frequency of fuel cell replacement can be achieved by implementing a control strategy that prevents excursions into operating regions causing failure. In this paper we implement a constrained MIMO model predictive controller (MPC) to avoid the failure modes relevant for a high-temperature tubular solid oxide fuel cell (SOFC) system while performing load-following. The primary causes of failure are catalyst poisoning, fuel or air starvation, carbon deposition, and microcracking. Prior steady-state thermomechanical stress analysis in literature has demonstrated that the minimum cell temperature and maximum negative radial thermal gradient are primary causes of microcracking in the SOFC. State-of-the-art SOFC control literature often seeks to track a mean or outlet cell temperature. The authors have presented the first approach to control the primary two causes of thermally-driven microcracking in tubular SOFCs using constrained control. Constraints are also incorporated into a steady-state optimization to ensure a feasible optimum.

Nonlinear modeling, estimation and predictive control in APMonitor

“…Specific examples are included in the appendices with commands to reproduce the examples in this paper. Some other examples include applications of computational biology (Abbott et al, 2012), unmanned aerial systems (Sun et al, 2014), chemical process control (Soderstrom et al, 2010), solid oxide fuel cells (Jacobsen et al, 2013;Spivey et al, 2012), industrial process fouling (Spivey et al, 2010), boiler load following (Jensen and Hedengren, 2012), energy storage (Powell et al, 2957;Edgar, 2011, 2012), subsea monitoring systems (Hedengren and Brower, 2012;Brower et al, 2012Brower et al, , 2013, and friction stir welding of spent nuclear fuel (Nielsen, 2012).…”

Advances and Perspectives on Solid Oxide Fuel Cells: From Nanotechnology to Power Electronics Devices

“…To optimize SOFC, various methods have been reported in the literature, which include Pareto set, the Lagrange method, Newton's iteration, and real‐time optimization. [ 152–158 ]…”

“…In ref. [153], the authors have optimized the number of cells and operating parameters of an SOFC stack for control applications by implementing constrained NLP. An SOFC-proton exchange membrane (PEM) fuel cell combination for power generation uses NLP considering the PEM pressure, fuel utilization of SOFC, and equivalence ratio for SOFC.…”

Comprehensive summary of solid oxide fuel cell control: a state-of-the-art review

“…From the perspective of dimension, models can also be divided into zero-, one-, two-, and three-dimensional [34,35]. Appropriate models need to be used for different control objectives, such as constant output voltage [36][37][38], improving dynamic response speed [39,40], and prolonging system service life [41].…”