Grey Model-based Improved Gompertz Method for End-of-life Analysis of Chinese New Energy Vehicles

Abstract

Traditional forecasts of end-of-life vehicle (ELV) flows treat all cars as a single homogeneous stock, ignoring the emerging split between fuel vehicles and new-energy vehicles (NEVs). This omission becomes critical as NEV sales soar and their batteries enter the recycling stream. To address this gap, this research establishes a high-precision forecasting framework for the ELV volume of NEVs in China, tackling critical data scarcity and the lack of classified prediction between fuel and electric vehicles. By integrating sales-driven lifetime modeling with macroeconomic scenario sensitivity, the study develops and validates a NEV-oriented scrappage framework to improve prediction accuracy and provide a robust foundation for recycling research and national resource supply planning. Fuel-vehicle stock was benchmarked against NEV stock, historical NEV sales were fitted (exponential trend; R2=0.9876), and a Weibull failure-rate model was integrated with sales to estimate NEV retirements; additionally, quarterly ownership data were expanded using SMOTE, growth stabilization was characterized via a Grey Model, and a Gompertz curve was fitted to link per-capita vehicle ownership with GDP per capita to enable scenario-based dynamic scrappage forecasting. Compared with the traditional Gompertz model, which often overlooks the distinct scrapping characteristics of NEVs, the proposed method reduces the forecasting bias. The integration of Weibull-based survival analysis enables the model to achieve a significant improvement in precision, with a low relative error of 8.999%. Incorporating SMOTE-enhanced ownership data, Grey- Model-identified stabilization periods, and a Gompertz ownership-GDP relationship improved data completeness and enabled dynamic scrappage predictions under different economic growth scenarios, offering stronger scenario adaptability than traditional approaches. The findings provide a critical foundation for future recycling research. By enabling precise forecasting, the model significantly impacts the supply chain by stabilizing the supply of raw materials for the NEV industry and boosting metal resource efficiency. This approach directly catalyzes the transition from linear consumption models to a closed-loop circular economy. Ultimately, these contributions align with the broader strategic objectives of carbon neutrality and sustainable development, providing policymakers and industrial stakeholders with the quantitative support required to advance greener practices within the automotive sector. The proposed framework provides a validated and flexible methodology for forecasting NEV retirement volumes in China, supporting recycling research, stabilizing raw-material supply, and improving metal resource efficiency across the NEV supply chain; future work should refine classification by distinguishing between battery electric vehicles and plug-in hybrid electric vehicles to further enhance accuracy and policy relevance. For future research, vehicle categories should be refined by distinguishing between BEVs and PHEVs. Additionally, separate failure parameters should be developed for private and commercial vehicles to account for different usage intensities.