The Italian SME is dedicated to advanced power generation and storage technologies with a focus on innovative solutions for net-zero energy systems. Model-Based Systems Engineering (MBSE) is combined with V-model methodologies to design advanced controls strategies and deliver comprehensive engineering services that drive cost-effect power solutions.

The company’s expertise includes optimizing stack design and balance-of-plant subsystems, along with electrical design and testing of power management systems. High-fidelity 3D and lumped-element models are utilized to simulate fuel cell and electrolyzer behaviour, generating large datasets for machine learning-based surrogate models. These are integrated into Model Predictive Control (MPC) frameworks, enabling real-time system optimization.
In addition, the company prototypes hybrid power sources, hydrogen generation and storage systems, and predictive battery management systems, addressing both short- and long-term energy needs.
The services include developing real-time diagnostic and prognostic tools for fuel cells, batteries, and electrolyzers to enhance system reliability and lifespan, as well as the implementation of engineering automation for fuel cell, battery and electrolyzer operations to increase precision and efficiency.

The company conducts techno-economic assessments of distributed energy systems using AI tools to optimize energy sources, locations, and operations, balancing technical performance with economic viability.

With over two decades of specialized expertise, the SME offers consultancy on materials and components for fuel cell and electrolyzer stacks, ensuring that clients will benefit from cutting-edge, tailored solutions.
Advantages and innovation
Employing a combination of Model-Based Systems Engineering and V-model methodologies, the SME provides a comprehensive range of engineering services tailored to support companies aiming for progress in net-zero power generation, such as:

- Optimization of stack design, including balance-of-plant subsystems.

- Electrical design and testing of power management systems.

- Utilization of high-fidelity 3D models and lumped-element models to accurately simulate fuel cells and electrolyzers behavior, enabling the generation of large datasets for machine learning surrogate models.

- Integration of surrogate models into data-driven Model Predictive Control (MPC) frameworks for real-time operation optimization.

- Prototyping of hybrid power sources, and hydrogen generation and storage systems.

- Design and prototyping of model-based predictive battery management systems.

- Development of real-time diagnostic and prognostic tools for fuel cells, batteries, and electrolyzers.

- Implementation of engineering automation for fuel cell, battery, hydrogen, and electrolyzers.

- Techno-economic assessment of distributed energy sources, locations, and operations using artificial intelligence tools.

- Providing consultancy based on highly specialized expertise developed over two decades on materials and components for fuel cell and electrolyzers stacks.