###### Abstract
This approach is achieved by combining physical models with data-driven models or by integrating different predictive models, but the approach is complex and requires more time and resources to build and debug the model. Due to its strong ability to deal with complex and nonlinear problems, data-driven methods especially perform well in the face of high-dimensional data and large amounts of historical data. Compared to model-driven approaches, data-driven approaches do not rely on deep understanding and assumptions about the internal mechanisms of the system and are therefore more flexible and adaptable when dealing with unknown or complex systems. At the same time, compared with the hybrid method, the data-driven method is more concise, avoiding the complexity of model combination and high computation. It is more suitable for vehicle application scenarios with high dynamic conditions. Therefore, grey system theory is used for data-driven prediction with the advantages of no requirements of large amounts of data. As a new interdisciplinary research field, grey system theory has achieved remarkable developments [16]. The GM(1,1) model is an important model in grey system theory and is a specific form of grey prediction model. It is an Accumulated Generating Operation (AGO), which can build an approximate linear difference equation to predict data trends. The GM can provide an effective prediction with a small amount of data and can adapt to different operating conditions, so the model can be applied to the research of power prediction. An improved GM (1,1) model has been proposed, which uses cubic spline interpolation to optimize background values and has better prediction accuracy in power investment prediction [17]. Based on the improved failure mode recognition algorithm and GM (1,1) model, a fatigue life prediction method for hyperstatic truss structures was established in one previous study, which proved the effectiveness of the fatigue life prediction method [18]. Researchers also used GM (1,1) to establish a prediction model to predict the future PM2.5 content in Changchun and found that it can be used as an effective method to predict the fog and haze weather [19]. Additionally, the GM (1,1) model has been proven in others' research to effectively predict the number of regional tourists [20] and predict the freight volume and freight turnover of Luoyang City to provide references for the decision making of logistic industry schemes [21]. A novel grey prediction model, which predicted four typical interval grey sequences, respectively, has effectively extended function transformation technology to the field of interval grey numbers for grey prediction [22]. Based on the Grey Verhulst model, an algorithm was proposed for predicting Internet topic trends with high accuracy [23]. Studies in vehicles found a new and effective method to analyze and predict the sound quality of vehicle internal noise by using grey system theory, and the method can be a useful tool for analyzing and predicting vehicle internal noise quality [24]. An enhanced grey predictive fuzzy controller (EGPFC) was also developed, which has better control performance in enhancing vehicle grip [25]. Especially in the field of fuel cell research, the grey model has been applied. Researchers propose a Grey-Markov chain power prediction energy management strategy for the fuel cell power generation system, which reduces hydrogen consumption in fuel cell power generation systems [26]. Additionally, a GM (1, N) power prediction strategy is proposed and compared with other strategies as a switch control strategy and logical threshold strategy and found that the power prediction strategy based on GM (1, N) is superior to the other two strategies in terms of output efficiency and fuel economy [27]. Thus, the grey system model has the advantages of providing an effective prediction with only a small amount of data and adapting to different operating conditions and environmental changes to be suitable for the study of power prediction.