Air starvation is a serious hazard during the operation of a solid oxide fuel cell (SOFC) system. So, an accurate performance model of the air supply system is needed for the control of air flow rate and the reduction of air starvation. The performance modelling of a regenerative blower, which is used for the air supply of a 5 kW SOFC system during power generation, is proposed based on the methods of random forest (RF) and Bayesian Optimization (BO). The performance model is first built

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... the RF method. The BO algorithm is then used to adjust the hyper-parameters of the model. The developed model can predict the outlet air flow rate through the operation data of regenerative blower, i.e. the rotation speed of impeller, the inlet and outlet pressure, humidity and temperature, without specific geometrical parameters. A series of operation data obtained from an experimental test rig are adopted to train and validate the presented method. Finally, the prediction performance of the developed algorithm is compared with different machine learning methods, such as decision tree regression, deep neural network. Results show that the proposed method can predict the outlet air flow rate under different working conditions with minimum prediction errors within 3.5% and acceptable computational cost. Therefore, the developed algorithm is promising for performance modelling of the blower, which can be used to predict the outlet flow rate and control the air supply system at SOFC in our next research.