Advanced Automatic Calibration of Fuel Cells and Electrolyzers in AVL FIRE™ M

  • Blog

Peter Urthaler
Senior Development Engineer Software

As the world seeks sustainable energy alternatives, fuel cells and electrolyzers emerge as a promising solution. These electrochemical devices play critical roles in the clean energy landscape. Fuel cells convert hydrogen and oxygen into electricity, emitting only water vapor, whereas electrolysis is the process of using electricity to split water into hydrogen and oxygen. Computational Fluid Dynamics (CFD) modeling in AVL FIRE™ M plays a crucial role in understanding and optimizing both fuel cells and electrolyzers. 

However, modeling fuel cells is no straightforward task. The intricate interplay of fluid dynamics, material interactions, and electrochemical reactions demands accurate representation. By calibrating the model using empirical data, we ensure that the simulated fuel cell aligns with real-world behavior. Calibration typically is necessary, due to uncertainties in material parameters and kinetic parameters, e.g. activation energies and exchange current densities. Calibration thus bridges the gap between theoretical models and real-world behavior, ensuring reliable predictions. 

gl_ast_image_blog-header_calibration-of-fuel-cells-and-electrolyzers

The polarization curve represents the relationship between cell voltage and current density. Achieving an accurate match between the model’s predictions and the experimental polarization curve is essential for reliable simulations. Calibration ensures that the model captures the real-world behavior of the fuel cell or electrolyzer, accounting for various losses and phenomena. Unlike simplified analytical models, CFD models operate on real-life geometries. Simplifications that do not significantly alter the polarization curve are challenging to achieve. Therefore, calibration must be performed directly on the actual geometry to account for intricate details. Even a single-cell model requires a medium to large mesh size to accurately capture flow patterns, mass transport, and electrochemical reactions. Consequently, calculating a single point on the polarization curve can take several hours due to the computational complexity. Using an optimizer on top of the fuel cell or electrolyzer model is impractical due to the lengthy calculation times. Traditional optimization techniques would significantly extend the simulation duration. An alternative approach involves calibrating parameters during the simulation run itself. As the simulation progresses, the model adjusts its parameters to match the experimental polarization curve. This dynamic calibration minimizes the need for separate optimization steps.

IMG-1
Figure 1: Polarization curve of a fuel cell

To calibrate a fuel cell model, we must analyze the typical polarization curve, see Figure 1. Performance losses observed in the curve can be attributed to specific physical phenomena:

  • Fuel crossover and internal currents
  • Kinetic (activation) losses: Associated with the kinetics of electrochemical reactions at the electrodes. They reflect the energy barrier that must be overcome for reactants (such as hydrogen and oxygen) to participate in the electrochemical processes.
  • Ohmic losses: Arise mainly from ionic conductivity in the ionomer and electrical conductivity in the gas diffusion layers.
  • Mass transport losses: Result from limitations in reactant diffusion and product removal.

In the model calibration setup of FIRE M, users have the flexibility to select the specific parameters they wish to calibrate, see Figure 2. For each performance loss, users can select a corresponding parameter to fine-tune. 

gl-ast_image-web-blog-fuel-cells-electrolyzers-calibration-02_05-24.png
Figure 2: Calibration settings in the fuel cell module

These regions cannot be adequately captured by a single operating point. Instead, for each parameter, the user must define an operating point to account for the diverse performance characteristics across the curve. These operating points are calibrated simultaneously, exchanging information about the various parameters. Prioritizing user-friendliness, the process is designed to be straightforward. Users only need to select which parameters they want to calibrate and define the same number of operating points. The critical requirement is that all operating points are calculated concurrently.  The internal workings of FIRE M handle the remaining tasks. This includes making sure the right amount of parallel calculations are started, making sure different operating points are defined, coordinating parallel calculations, managing communication among different operating points, and ensuring seamless execution.  Currently, interaction among the operating points occurs via the filesystem. As long as the different operating points have access to the same file system tree, they can independently perform their calculations. This approach even allows for running different operating points on separate workstations, as long as they execute simultaneously.

gl-ast_image-web-blog-fuel-cells-electrolyzers-calibration-03_05-24.png
Figure 3: Calibration of three parameters and three operating points on the polarization curve In Figure 3, the results of such a calibration are evident. For this specific low-temperature PEM fuel cell example, three operating points corresponding to th

In Figure 3, the results of such a calibration are evident. For this specific low-temperature PEM fuel cell example, three operating points corresponding to the parameters,  ionic conductivity, reference exchange current density on the cathode and through-plane tortuosity, were defined. The parameters are successfully adapted simultaneously to reach all three operating points.

A novel calibration algorithm has been integrated into FIRE M, specifically for fuel cells and electrolyzer cells. This innovative approach enables the calibration of a complete polarization curve by concurrently calculating various operating points. The approach is designed for real-life examples with large mesh sizes and limited calculation time while emphasizing ease of use. In summary, this calibration algorithm combines accuracy, efficiency, and user-friendliness—a valuable addition to the FIRE M toolkit.

Stay tuned

Don't miss the Simulation blog series. Sign up today and stay informed!

Like this? Maybe you’ll also enjoy these…

gl_ast_image_blog-header_template_04_23.jpg
Particle-Based Simulation to Optimize Dishwasher Design

Dishwashers are one of those common household appliances that can be found in almost every modern kitchen. Over the decades, not only have dishwasher designs and capabilities been adapted, but the methods and technologies used to analyze and enhance various aspects of dishwasher efficiency have also changed significantly.

gl-ast_blog-battery-aging-header-07-2024
Gaining Insights Into Battery Aging With the Virtual Twin

The battery is undoubtedly the most complex component of modern electric cars and is largely responsible for the driving experience and range. However, over the course of its service life, it is subject to a continuous loss of performance due to degradation mechanisms that impair its storage capacity and thus the range and power output of the vehicle.

gl-ast_blog-fast-charging-header-01-07-2024
Optimizing Fast-Charging Strategies for Electric Vehicles

Electromobility is facing a key challenge: battery charging times must be minimized in order to increase the acceptance of electric vehicles. This is of key importance as, alongside range, charging time is one of the most important factors for user satisfaction.

Skip to main content Toolbar items Administration menu Home Current page Content Structure Translation Reports Configuration Help Close Breadcrumb Back to site  Edit gl_iodp_imag_optimizing_hybrid_powertrain_system_interactions_on_all_testbed_types_07.22.png  Edit Media Toolbar items Prod Go to  Global Nusa.Viher@avl.com Edit Image gl_ast_image_header-blog_vtms-kolaric_04_23.jpg Primary tabs Edit(active tab) Delete Usage Translate Name gl_ast_image_header-blog_vtms-kolaric_04_23.jpg Category  - None - Statu
Leveraging Simulation to Achieve Highly Efficient Vehicle Thermal Management

Driving range is one of the key sales drivers of battery electric vehicles and to the end customers, every kilometer counts. There are several ways the total efficiency of the vehicle can be increased, such as improving aerodynamics or decreasing vehicle weight, but one of the major contributors is an efficient thermal management system (VTMS).

Simulation Blog - Analyzing Critical ADAS/AD Scenarios With AVL Scenario Simulator™
Analyzing Critical ADAS/AD Scenarios With AVL Scenario Simulator™

To determine if an automated driving function is safe, billions of test kilometers would be required. Physical testing and real-world prototypes simply cannot efficiently handle such a massive test volume. Virtualization offers a more sustainable option that can manage the enormous test volume required at a much lower cost.

gl-ast_image-web-blog-soiling-header_00_06-24.jpg.
5 Reasons Why PreonLab Is the Ideal Simulation Software for Vehicle Soiling Simulations

Understanding and managing the influence of soiling on a vehicle is a critical aspect of driving safety that needs to be considered while designing vehicles. Soiling or contamination can be caused by the deposition of fluid and solid contaminants on the vehicle surface due to splashing, harsh weather conditions while driving, ...

gl_ast_image_blog-header_template-01_04_23
Develop and Evaluate Solid Oxide Electrolyzer Systems Through Simulation

Global initiatives and actions to reach long-term climate goals are evidently resulting in the development and industrialization of new electrolyzer systems. Significant growth of announced electrolyzer projects is forecasted each year and solid oxide electrolyzers (SOEC) are one of the most promising technologies.

gl_ast_image_blog-header_thermal-runaway
Preventing Thermal Runaway: Simulation as a Tool for Improved Battery Cell Safety

Driving range is one of the key sales drivers of battery electric vehicles and to the end customers, every kilometer counts. There are several ways the total efficiency of the vehicle can be increased, such as improving aerodynamics or decreasing vehicle weight, but one of the major contributors is an efficient thermal management system (VTMS).

gl_ast_image_slideshow-release2024r1_keyvisual_16x9.jpg
AVL Simulation Software Release 2024 R1

Discover new features and updates to our simulation solutions.

Stay tuned for the Simulation Blog

Don't miss the Simulation blog series. Sign up today and stay informed!

CAPTCHA
By clicking on submit, you give consent to the use of the data you provided to process your request and to receiving communication in connection with your request/registration.
Please click here to view the AVL Privacy Policy.

Senior Development Engineer Software