A novel fractional-order accumulation-based discrete grey model and its application in forecasting BEV sales in Europe

Purpose Accurate forecasting of Europe’s battery electric vehicle (BEV) market is crucial for trend analysis and policy formulation. This study aims to propose an innovative discrete grey model specifically designed for predicting BEV sales in Europe. Design/methodology/approach This study innovatively integrates fractional-order accumulated (FOA) with an enhanced driving term that incorporates integer-order polynomials and time-dependent power terms, thereby developing an optimized discrete grey prediction model. The particle swarm optimization algorithm (PSO algorithm) was employed for parameter optimization, and solution stability was thoroughly investigated. Through comparative analysis with three benchmark models using real-case studies, the effectiveness and forecasting accuracy of the proposed model were validated, followed by its application to predict future BEV sales in Europe. Findings The proposed model demonstrates effective applicability for BEV sales forecasting. In the coming years, European BEV sales are projected to exhibit a stable growth trend. Practical implications The proposed model can be effectively applied to forecast and analyze both BEV sales volume and market prospects in Europe. Based on the forecasting and analytical results, corresponding policy recommendations are provided. Originality/value This study pioneers a FOA discrete grey polynomial model that incorporates time-power terms, representing a novel grey prediction model with superior performance characteristics.