A novel grey Bass extended model considering price factors for the demand forecasting of European new energy vehicles

The vigorous development of new energy vehicles (NEVs) has become an effective approach for achieving carbon emission reduction and carbon neutrality goals. The prediction of the demand for NEVs can provide quantitative decision-making basis for governments and enterprises. In consideration of the condition under which the demand for NEVs is affected by subsidy policy and limited samples, a novel demand forecasting model for NEVs is constructed based on the improved Bass model and grey theory in this study. First, the price function is introduced into the improved Bass model to establish a differential equation of demand for NEVs. Considering the limited samples, the differential equation model is transformed into a grey Bass extended model (GBEM). Second, the parameters of GBEM are estimated using the least squares method, the time response sequence is obtained through the Gaussian hypergeometric function, and the background value is optimized using the particle swarm optimization algorithm. Third, three data sets from Norway, France, and Europe are studied to confirm the validity. The calculation results show that the proposed model achieves higher accuracy than six existing models, and the MAPE of the model are all below 10% in three cases. Lastly, GBEM is applied to predict the demand for NEVs in the three aforementioned regions from 2020 to 2023. The results show that the demand for NEVs in Norway, France, and Europe in 2023 are 343,860, 280,685 and 2,157,908, with an average annual growth rate of 46.3133%, 47.1837% and 40.0457%, respectively, which provide a certain reference value for the formulation of national government policies and the production activities of NEV enterprises.