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The present work deals with the evaluation of model based control strategies for a PEM fuel cell to control voltage. PEM fuel cell is an electrochemical device that converts the chemical energy to electrical energy. Stack voltage is affected by many factors like stack temperature, moisture content of the membrane, partial pressure of hydrogen and air, inlet rate of hydrogen and air and also fuel starvation affects the rate of reaction and hence the voltage produced. In this work, two single input single output models are taken with stack voltage as controlled variable and hydrogen and air flow rate as manipulated variables respectively. The simulation study on two different control structures i.e., feedback and feedback plus feed forward control structures evaluates the effectiveness of proposed controllers concerning set-point tracking and disturbance rejection. Comparative study is carried out by simulations by implementing various model based control strategies, PI, IMC-PID and MPC. The results shows that MPC gives best results in terms of Integral Square error (ISE), Integral Absolute error (IAE) and controller effort (TV). In addition, robust stability analysis is carried out for uncertainty in the process parameters. Also, the controller fragility is studied for uncertainty in the controller parameters.

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Nowadays, the application of the proton exchange membrane fuel cells (PEMFCs) is advancing as a popular renewable energy source. PEMFCs must operate at low temperatures, have high power density, and be easily implemented. These features contorted them into the most compelling type of fuel cell. However, PEMFCs need a strategy for maintaining the voltage at a desired operating point, specifically during the current variation. The present study proposes a novel Multiple-Input-Single-Output (MISO) control structure for a PEMFC system to improve its performance. This study focuses on airflow optimization and hydrogen consumption optimization, as the literature focuses more on airflow optimization for compressor or pump performance. To give an outlook, a two-input-two-output PEMFC system, with air and hydrogen flow rates as inputs and cell voltage and load current as outputs, is considered a two-input-single-output system by fixing the output resistance. The fractional order model is realized from the existing integer order MISO PEMFC system using a genetic algorithm as the optimization technique. The proposed control structure aims to control the output cell voltage by regulating the air and hydrogen inlet rates by designing various model-based controllers like PI, PID, MPC, and Predictive PID. The control performance is evaluated for set point tracking, disturbance rejection, inverse response rejection, and time delay compensation for the best combination of controllers based on ISE, IAE, and TV values. From the simulation results, the fractional-order system is observed to give better results than the integer-order system, with MPC showing the best results for controlling the stack voltage.

Concerning global warming, an energy-efficient power source must produce low or no pollutant emissions and provide an unlimited fuel supply. Proton Exchange Membrane fuel cell (PEMFC) is an electrochemical device that transforms chemical energy into electrical energy. The performance and durability of PEM fuel cells are affected by voltage reversals and fuel starvation. Oxygen Excess Ratio (OER) is a crucial factor in controlling the fuel starvation of the PEMFC system. First, this work identified the PEMFC as an integer order and fractional-order first order plus time delay models using the predictor error method and Grunwald–Letnikov simulation method based on a trust-region-reflect algorithm, respectively. Fractional order models more accurately represented the PEM fuel cell system dynamics. Then, robust fractional filters cascaded with PID controllers based on the Internal Model Control scheme (IMC) are designed for identified integer and fractional order models to regulate the OER by compressor voltage manipulation. The genetic Algorithm (GA) optimization technique is used to find the optimal fractional filter tuning parameters. The proposed controller’s performance regarding Integral Absolute Error (IAE) and Total Variance (TV) is analyzed. Furthermore, the robustness of a perturbed plant and fragility with perturbed controllers are elucidated. The results show that a fractional filter cascaded with fractional order PID controller improves the performance compared to a fractional filter cascaded with integer order PID controllers.