Fuel cell modelling, simulation and optimisation

Why first-principles modelling is essential

The challenge: many complex effects need to be considered simultaneously

Effects that need to be considered simultaeously – click to enlarge

First principles modelling – creating the framework

First-principles modelling – click to enlarge

Model validation – adjusting parameters to match reality

A major challenge of fuel cell design – and the reason that experimental techniques alone are inadequate – is that so many complex phenomena and interacting components need to be considered simultaneously.

It is no use designing fuel or air flow channels without understanding the how they interact with the chemical and electrochemical phenomena occurring in the electrolyte. Similarly it is pointless designing a fuel cell stack, with all its complex interactions, without considering the system within which it operates and the likely dynamic load requirements.

Advanced process modelling was specifically designed to handle such complex interacting systems. This is the reason that so many leading fuel cell companies use gPROMS and PSE's models and methodologies.

The power of first-principles models validated with experimental data

Advanced process models have two attributes that make them capable of a very high degree of predictive accuracy, within a flexible framework.

The first is a detailed first-principles representation of all the fundamental physical and chemical phenomena that characterise the process.

gPROMS fuel cell models typically include all chemical and electrochemical reactions and interactions, the micro-scale diffusion of molecules across and within the layers, and the heat transfer across the layers and to the surroundings. Because they involve first-principles modelling they are equally applicable to SOFC, PEM and other fuel cell types.

The second ingredient is accurate model parameter information – for example, reaction kinetic constants, heat transfer coefficients and other material properties – derived from "real-world" laboratory, pilot or operating data.

These are determined using gPROMS's powerful parameter estimation techniques, which use the model in conjunction with experimental data to fit the most accurate possible model parameters from sets of experimental data.

It is the combination of these two that provides advanced process models with a highly-accurate predictive capability over a wide range of design and operating conditions.

Hybrid modelling and co-simulation

Hybrid modelling – Fluent and gPROMS – click for more details

In some cases it may be advantageous to use gPROMS models in combination with other modelling software, in order to take advantages of unique software features and strengths. For example,

it is possible to use gPROMS to handle the complex chemistry and electrochemistry of the anode-electrolyte-cathode structure, and Fluent Inc.'s Fluent® for detailed modelling of flow channel hydrodynamics (see gO:CFD for more information).
gPROMS models can be used with Mathworks MATLAB® and Simulink® environments for control analysis and design (see gO:Simulink for more information).
gPROMS models can be used within CAPE-OPEN compliant steady-state flowsheeting packages such as Aspen Technology's Aspen Plus® or Hysys® (see gO:CAPE-OPEN for more information).

Such "hybrid simulation" or "co-simulation" is well-established. Much of the pioneering work in this field was done on fuel cell applications because of their need to take into account complex interacting phenomena simultaneously.

Process optimisation vs. simulation

Once a fully-validated fuel cell model is available it can be used in conjunction with formal mathematical optimisation techniques to optimise many different aspects of design and operation – not just simulate behaviour.

Examples of a complex optimisation are "choose between one material from a selection of three that will minimise the temperature differential across the anode for a range of typical operating scenarios".

With a well-formulated predictive model, this question can be answered in a single run rather than many trial-and-error simulations coupled with physical testing.

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