Model-based design framework for clean-tech products

Anovion implemented a model-based design framework for novel clean energy products, which is particularly applicable to fuel and electrolyzer cell systems. The central idea of this framework is to model and numerically solve for the physical performance of the component or system under design. The degree of fidelity of the employed models varies in accordance with the given design objectives. The technical design tasks are then guided by these models until pre-defined performance targets are achieved.

  • 2022
  • Model-based design framework
  • Virtual engineering processes
  • Virtual engineering methods
  • Model eco-system
  • None

The key requirements for technical product development in the field of clean energy, and thus fuel cell and electrolyzer technologies, are – besides a pathway to zero CO2 emissions – as follows: (1) Fast-paced, i.e. short development cycles. Attaining leadership within the rapidly growing clean-tech markets requires to provide respective products at the right timing. (2) Flexible, i.e. designing products that are individualizable to meet the customers’ needs and provide solutions for application-specific problems. Achieving such flexibility features within the product offerings presents a lucrative unique selling point as by a “centralization of the customer” approach. (3) Cost-effective, i.e. newly developing or advancing an existing product needs to be economically viable. Employing a model-based design procedure for engineering and development of novel clean-tech products is a considerable factor towards meeting these above-described requirements.


Anovion’s goal was to create engineering development capabilities that enables fast-paced, flexible, and cost-efficient product and engineering design. Thus,  a model-based design framework was developed that is directly applicable to fuel cell and electrolyzer systems. Specifically, the framework offers five distinct modeling and analyses modules: (1) thermodynamic cycle design, (2) energy management strategy development, (3) numerical optimization procedures, and (4) model eco-system for “in-the-loop” engineering work.

Thermodynamic cycle design

Thermodynamic cycles describe the energy flows in energy systems in terms of physical mechanisms and quantitative values. Modeling and corresponding simulations of the cycle ensure that performance targets and optimal efficiencies are achieved. At the same time, the system architecture is derived along with the determination of the energetic quantities of the system components. The “Cycle Design” module contains appropriate modeling and simulation capabilities along with procedural guidelines for these latter tasks.

Energy management design

The static models associated with the thermodynamic cycle design and component sizing tasks present the input for building transient models. Respective simulations reproduce the dynamics of the system. Based on these transient models, an energy management strategy can be designed, i.e. define how the different components interact for given time-dependent performance scenarios. These tasks can be carried out using the module “EMS Design”.

Simulation and design optimization

Model-based and data-driven approaches are used to optimize the thermodynamic cycle, component sizes and the Energy Management Strategy with the help of module “optimization”. The main optimization constraints are maximizing the efficiency along with meeting short and long-term performance requirements. This leads to refined component sizes along with an optimal management strategy. Moreover, the foregoing procedures cause the virtual product design to be computationally optimized, ready for physical prototyping.

Model ecosystem

The static and transient models of the foregoing modules can be used as the basis for an “in the loop” product development approach. For this approach, prototypes of critical components or even the entire system are built for extensive experimental testing. All surrounding components, input or interface conditions are modeled and set as inputs for testing the physical system, which is “in-the-loop”. Thereby, realistic operating conditions for the testing and validation tasks are achieved. The Module “model eco-system” provides respective models for generating these realistic input conditions.


Utilizing this framework enables an end-to-end virtualized product development from part to system level without the need of building and testing expensive hardware prototypes. This leads to considerable competitive advantages in terms of (1) accelerating product development cycles, (2) enabling a customer-centered, individualized product development, (3) reducing overall R&D costs, and (4) implicitly generating digital twins of the respective products and systems. Of course, to finalize the product development, building, testing and validation of a hardware prototype is needed. However, since this prototype is based on a model-optimized counterpart, time, effort and costs for this hardware phase is greatly reduced.