Control Strategies for Proton Exchange Membrane Fuel Cell Systems

Proton exchange membrane fuel cell systems convert hydrogen and oxygen into electricity via electrochemical reactions at low to moderate temperatures. These systems comprise the cell stack and several auxiliary subsystems for reactant supply, thermal management, water regulation and power conversion. Effective control strategies are required to regulate reactant stoichiometry, stack temperature, humidity and pressure to ensure optimal performance, durability and safety under transient loads. Traditional feedback schemes—PID controllers and sliding mode control—offer simplicity and robust setpoint tracking but can struggle with complex nonlinearities and time-varying dynamics. Model predictive control (MPC) has been adopted to handle constraints and anticipate future behaviour, while adaptive observers and Kalman filters provide real-time state estimation for closed-loop regulation. Recent advances integrate fuzzy logic, fractional-order controllers and machine-learning techniques—particularly deep reinforcement learning—to enhance robustness, accelerate response and enable online tuning. Hybrid frameworks combine physics-based models with data-driven algorithms to manage disturbances, avoid oxygen starvation, mitigate flooding and extend membrane life. Applications span automotive propulsion, stationary power and renewable energy storage, where rapid load changes and strict efficiency requirements demand sophisticated control. These developments underscore the global significance of PEMFC control strategies in advancing decarbonisation and energy security from research laboratories to commercial deployment.

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Control Strategies for Proton Exchange Membrane Fuel Cell Systems publication trend

The graph below shows the total number of articles in control strategies for proton exchange membrane fuel cell systems across all publications each year (not limited to Nature Index journals).

Technical terms

Proton Exchange Membrane Fuel Cell (PEMFC): Electrochemical device that produces electricity by transferring protons through a polymer electrolyte membrane between hydrogen and oxygen electrodes.

Oxygen Excess Ratio (OER): Dimensionless measure of the actual to stoichiometric oxygen supplied, critical for avoiding oxygen starvation and minimising parasitic losses.

Deep Reinforcement Learning: Machine learning paradigm where agents learn optimal control policies through trial-and-error interactions within a simulated or real environment.

Fractional-Order Fuzzy PID Controller: Control algorithm combining fractional calculus, fuzzy logic and PID principles to achieve flexible and robust regulation of nonlinear systems.

References

1. A review of control strategies for proton exchange membrane (PEM) fuel cells and water electrolysers: From automation to autonomy. Energy and AI (2024).
2. Optimization of the air loop system in a hydrogen fuel cell for vehicle application. Energy Conversion and Management (2023).
3. A new adaptive controller based on distributed deep reinforcement learning for PEMFC air supply system. Energy Reports (2021).
4. Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm. Energies (2019).
5. Constrained extended Kalman filter design and application for on-line state estimation of high-order polymer electrolyte membrane fuel cell systems. International Journal of Hydrogen Energy (2021).
6. Operational Efficiency Improvement of PEM Fuel Cell—A Sliding Mode Based Modern Control Approach. IEEE Access (2020).