## 1 Introduction  
In recent years, there has been an increasing demand for alternative energy due to fossil fuel shortages and environmental issues [1; 2; 3; 4; 5; 6]. The hydrogen fuel cell technology is an ideal solution, benefiting from low carbon emissions and high energy-conversion efficiency [7; 8; 9; 10; 11; 12]. As one of the types of fuel cells, the proton exchange membrane fuel cell (PEMFC) has a promising future as a clean power source [13; 14; 15; 16; 17; 18; 19] with its high power density, outstanding energy efficiency, low operating temperature, and zero emissions. The PEMFC's commercialization still faces challenges in improving system endurance and performance [20; 21], although fundamental research and technology development has been advanced in the past decades. Cell voltage uniformity is one of the significant parameters indicating the performance and endurance of a practical PEMFC stack system [22; 23]. Keeping the cell voltage consistent for a stack with hundreds of cells is vital because high cell voltage uniformity ensures a long cell life and high performance [22; 24; 25]. The cell voltage uniformity is affected by temperature, reactant flow rates, humidity, load current, and gas pressure [24; 26; 27; 28; 29]. Two models are used in performance prediction: the physics-based and the data-driven models. The physics-based model demands massive computation resources due to the fact that the designed geometry and multi-physical processes of the fuel cell stack are complicated [24; 30]. It makes the model unpractical for performing real-time control and dynamic online diagnostics. Unlike the physics-based model, the data-driven model can reveal the relation between the inputs and the outputs using artificial intelligence algorithms, avoiding the computation of the complex governing equations. As one of the most widely used data-driven PEMFC models, the artificial neural network (ANN) can process the data efficiently due to the fact that it circumvents the multi-physical mechanism. Several ANN models have been presented for the PEMFC system's performance forecast and operation optimization. [PERSON] et al. suggested an ANN model coupled with a genetic algorithm to forecast PEMFC performance and determine its maximum power for operational management [31]. [PERSON] et al. developed an ANN model to predict the maximum output power of PEMFCs, taking into account the operational factors' impact on the polarization curves [32]. [PERSON] and [PERSON] investigated an ANN model with the group data management approach to forecast fuel cell performance [33]. [PERSON] et al. devised optimum control techniques to maximize PEMFC efficiency with an ANN model of nonlinear autoregressive configuration. [PERSON] et al. proposed an ANN structure to forecast the single cell voltage considering the performance impacts of operating circumstances [34]. [PERSON] et al. developed an ANN structure and semi-empirical models to find the best working conditions for improving the PEMFC's efficiency [35]. [PERSON] et al. studied an ANN and multivariate polynomial regression (MPR) model for predicting the short-term degradation behaviors of PEMFCs [36]. [PERSON] et al. developed an ANN model to predict the remaining useful life of the PEMFC in comparison with other data-driven models [37]. [PERSON] et al. proposed an ANN model to predict the dynamic electrical and thermal performance of the PEMFC stack under various operating conditions [38; 39]. [PERSON] et al. investigated a bio-inspired ANN model to find the optimal design and control variables for fuel cell dynamic operations [40]. Most of the literatures focused on the average cell voltage, and did not consider the cell voltage's uniformity, which is critical to evaluating a fuel cell stack's performance, health, and reliability. In our previous work [41], an artificial neural network fuel cell model was used to predict each individual cell voltage of a 60 kW PEMFC stack.