Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

Abstract

Proton Exchange Membrane Fuel Cell (PEMFC) fuel cells is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that allow deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a Nexa Ballard 1.2 kW fuel cell; therefore, it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them taken from literature and two proposed in this work. Finally, the model with the identified parameters was validated with real data.

Dynamic modeling of an open cathode PEM fuel cell for automotive energy management applications

“…By initializing the PSO with a random population and an iterative procedure based on movement and intelligence processes in a scalable system, the algorithm succeeds in finding an optimal global solution [19]. Several methods have been used for fuel cell parameter identification in the literature, such as the genetic algorithm [20]- [21], the teaching-learning based optimization algorithm [22] and quantum-based optimization algorithm [11], [19], [23]- [25]. In this paper, the particle swarm optimization (PSO) approach is used to identify the mathematical parameters of each part of the model since it has been proven to be an accurate technique for identifying the parameters of PEM fuel cell models, even in the presence of measurement noise [24].…”

Synchronous Machine Winding Modeling Method Based on Broadband Characteristics

“…When identifying the model's parameters, the theoretical model with the experimental data are usually combined to identify the parameter value [19][20][21][22][23][24][25][26]. As an essential method of parameter identification, the optimization algorithm has a strong search performance for a specific parameter range, and it has high adaptability for practical problems [19,21,23,24].…”

Optimization of PEMFC Model Parameters Using Meta-Heuristics

“…Recently, different types of approaches were presented for this purpose. The use of meta-heuristic algorithms, due to their ability for faster and simpler solving of nonlinear models, are exponentially increasing [13][14][15][16]. Some examples of these methods are the dragonfly algorithm [17], deer hunting optimizer [18_ENREF_20], multi-verse optimizer [19_ENREF_18], improved invasive weed optimization algorithm [20_ENREF_19], and chaotic grasshopper optimizer [21].…”