[Paper Review] Thermal Modelling and Controller Design of an Alkaline Electrolysis System under Dynamic Operating Conditions

Ruomei Qi, Jiarong Li+7 more|arXiv (Cornell University)|Feb 27, 2022

Fuel Cells and Related Materials4 citations

TL;DR

This paper proposes a control-oriented third-order time-delay thermal model for alkaline electrolysis systems under dynamic conditions, enabling design of two advanced controllers—current feed-forward PID (PID-I) and model predictive control (MPC)—to suppress temperature overshoot. Experimental results show a 2.2 °C reduction in overshoot with PID-I and near-zero overshoot with MPC, while simulation indicates a 1% efficiency gain is achievable at higher set points in MW-scale systems.

ABSTRACT

Thermal management is vital for the efficient and safe operation of alkaline electrolysis systems. Traditional alkaline electrolysis systems use simple proportional-integral-differentiation (PID) controllers to maintain the stack temperature near the rated value. However, in renewable-to-hydrogen scenarios, the stack temperature is disturbed by load fluctuations, and the temperature overshoot phenomenon occurs which can exceed the upper limit and harm the stack. This paper focuses on the thermal modelling and controller design of an alkaline electrolysis system under dynamic operating conditions. A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design. Based on this model, we propose two novel controllers to reduce temperature overshoot: one is a current feed-forward PID controller (PID-I), the other is a model predictive controller (MPC). Their performances are tested on a lab-scale system and the experimental results are satisfying: the temperature overshoot is reduced by 2.2 degree with the PID-I controller, and no obvious overshoot is observed with the MPC controller. Furthermore, the thermal dynamic performance of an MW-scale alkaline electrolysis system is analyzed by simulation, which shows that the temperature overshoot phenomenon is more general in large systems. The proposed method allows for higher temperature set points which can improve system efficiency by 1%.

Motivation & Objective

• Address the critical challenge of temperature overshoot in alkaline electrolysis stacks during dynamic operation due to load fluctuations.
• Develop a simplified yet accurate control-oriented thermal model suitable for controller design under transient conditions.
• Design and validate novel temperature controllers that improve thermal stability and system safety compared to conventional PID.
• Assess the scalability of thermal control performance to MW-scale alkaline electrolysis systems.
• Enable higher temperature set points to improve system efficiency without compromising safety.

Proposed method

• Developed a third-order time-delay process model to represent the thermal dynamics of the alkaline electrolysis stack, capturing key thermal inertia and delay effects.
• Formulated a current feed-forward PID controller (PID-I) that anticipates load-induced thermal disturbances using real-time current measurements.
• Designed a model predictive controller (MPC) based on the thermal model to optimize control actions over a prediction horizon, minimizing overshoot.
• Validated the controllers on a lab-scale electrolysis system using experimental data under varying load conditions.
• Extended the analysis to MW-scale systems via simulation to evaluate thermal performance and efficiency potential under dynamic operation.
• Used the thermal model to simulate temperature responses and quantify overshoot behavior across different system scales.

Experimental results

Research questions

• RQ1 How does load fluctuation during dynamic operation lead to temperature overshoot in alkaline electrolysis stacks?
• RQ2 Can a simplified third-order time-delay thermal model accurately represent the thermal dynamics for controller design?
• RQ3 To what extent can a current feed-forward PID controller reduce temperature overshoot compared to standard PID?
• RQ4 Can model predictive control (MPC) eliminate temperature overshoot more effectively than PID-based approaches?
• RQ5 What efficiency improvement is achievable by increasing the temperature set point in MW-scale alkaline electrolysis systems with advanced thermal control?

Key findings

• The proposed third-order time-delay thermal model accurately captures the thermal dynamics of the alkaline electrolysis stack under transient conditions.
• The current feed-forward PID (PID-I) controller reduced temperature overshoot by 2.2 °C compared to conventional PID control in lab-scale experiments.
• The model predictive controller (MPC) successfully eliminated observable temperature overshoot during dynamic load changes.
• Simulation results for an MW-scale system confirmed that temperature overshoot is more pronounced in large systems under dynamic operation.
• Enabling higher temperature set points through advanced control can improve system efficiency by approximately 1%.
• The MPC controller demonstrated superior robustness and performance in handling complex thermal dynamics compared to PID-I and standard PID.