Abstract
This paper presents the modelling and real time implementation of PEM (polymer electrolyte membrane) fuel cell flow control. Flow control presents a critical performance requirement to achieving dynamic power responses for electric vehicle motor demands. However a fuel cell’s complex structure and reactant requirements traditionally result in an unsatisfactory response to such dynamic loading instances. This in turn causes brief power losses associated with driving patterns such as acceleration and hill climbing. To improve the fuel cell’s dynamic response to such drive cycles, this paper presents new methodology for system identification and controller design. The fuel cell is modelled initially with established linear model and parameter estimation methods. The approach is then expanded to an on-line system identification LabVIEW programme to account for the non-linear and time varying characteristics. Based upon this identification process, a novel LabVIEW self-tuning PID controller is implemented in real time to control the response. The self-tuning controller continuously re-calculates the critical gain and period, and then adjusts the controller actions accordingly. Conclusions are then summarised from the results and future ongoing work is discussed briefly.
Highlights
The use of fuel cells as alternative power sources for mobile and stationary applications is steadily increasing as concerns over global oil reserves, escalating prices, and the environmental impacts of CO2 emissions increase
The identified system parameters are passed to Design and Implementation of On-Line Self-Tuning Control for PEM Fuel Cells further mathematical functions which execute the Ziegler–Nichols proportional integral derivative (PID) parameter determination process represented as Equations (26)-(28) referenced from “Advanced PID Control” Astrom, K et al [10]
System identification is performed on a single cell membrane electrode assembly (MEA)
Summary
The use of fuel cells as alternative power sources for mobile and stationary applications is steadily increasing as concerns over global oil reserves, escalating prices, and the environmental impacts of CO2 emissions increase. PEM’s use of solid polymers makes it favourable for ease of construction, and it exhibits good ability to respond to rapidly changing loads as experienced in automotive applications. This ability to respond to load changes depends on precise control of several subsystems. A fuel cell’s inherent nonlinearities and time varying characteristics inevitably pose difficult problems for system identification and control, making either linear or more complex nonlinear models, only valid over an operating range which exhibits linearity. Referenced Pukrushpan et al [1] implemented an observer based feedback controller to protect the stack from the previously described oxygen starvation effects during loading This uses a linear quadratic technique based on a linearised state-space model. Revenkar [7] highlights the requirement for further research into system models and controllers for multiple input, multiple output (MIMO) systems covering the whole operating range, a problem which is in part addressed within the proceeding sections
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
