Design of 2DOF control system fused with artificial intelligence for power enhancement and mitigation of degradation in fuel cell systems.

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model. • Improved net output power through the integration of AI in a 2DOF control system. • Reduction of degradation using a data-driven 2DOF control system. • Development of data-driven reference generators and feedforward controllers. • Comparative performance evaluations of fuzzy models and neural networks. [ABSTRACT FROM AUTHOR]