###### Abstract  
A frequently large variation in load conditions is of great impact on the service life of fuel cells during the operation of fuel cell vehicles and further increases the maintenance cost of the system. This study proposed a method of power demand prediction of the proton exchange membrane fuel cell (PEMFC) on vehicles in the actual traffic environment. The hybrid power system topology of fuel cells and power battery on commercial vehicles is selected to build a fuel cell model, and the accuracy of the fuel cell model is verified. An improved multiple grey prediction method is then proposed to predict the power demand during the sampling period of the fuel cell while considering a dynamic time window in the prediction period. Comparisons were made between this proposed model and the other two prediction models as a single-step prediction and multi-step prediction. Data of CHTC-HT and field testing working conditions were used to evaluate these three prediction models in fuel cell power demand. Results showed that the multiple grey method showed a better prediction performance than the other models, indicated by the lowest error value of 16.944% under the CHTC-HT condition, the lowest error value of 2.169% under stable conditions with less variable load and 1.930% under dynamic conditions with frequent load changes in field testing. This study of the demand power prediction can be devoted to pre-tuning the fuel cell system to avoid performance degradation caused by unanticipated power fluctuation. v 2020 2020 20 January 2020 20 January 2020 20 January 2020 20 January 2020 20 January 2020 20 January 2020 2  
Thus, it is necessary to enhance the durability of fuel cells through technological innovation to promote the commercialization of fuel cells. The degradation of fuel cell performance is a complex problem with multiple coupling, and the harsh working conditions will lead to changes in its electrochemical active area and catalyst structural layer, which will further cause the degradation of fuel cell performance [6]. Conditions that affect the durability of fuel cells mainly include variable load, idle speed, high load, start-stop and other conditions [7]. Among them, variable load conditions are the most serious conditions that affect the durability of fuel cells and account for the highest proportion of performance degradation factors [8]. Therefore, predicting the fuel cell demand power in the next period and then adjusting the fuel cell system in advance when the vehicle demand power changes rapidly can avoid the melting of the catalyst and the polymerization of the catalyst particles caused by an excessive power adjustment rate, thus decreasing the fuel cell performance attenuation. Accurate parameter prediction plays an important role in the lifetime of the PEMFC. Prediction methods used in the study of PEMFC performance can be divided into three categories: model-driven, data-driven and hybrid methods [9]. Model-driven methods usually use empirical or semi-empirical degradation models, combined with methods such as the extended Kalman filter (EKF) or particle filter to describe the behaviour of the system [10; 11]. These methods use a small amount of data, but no accurate model has been established because of an extremely complex system structure of the PEMFC with an indistinct degradation mechanism [12]. Data-driven approaches such as machine learning [13] and data packet processing [14] analyze the currently recorded data to explore the declining trend in PEMFC performance. Based on the short-term change trend of time series data, researchers [15] use the linear regression relationship to extract input and output variables and establish a regression prediction model based on a machine learning algorithm. The hybrid method combines the advantages of model-driven and data-driven methods and makes use of the theoretical basis provided by the model-driven method and the prediction ability of the data-driven method to improve the accuracy and reliability of prediction.