Optimal New Energy Vehicle Production Strategies Using Rich-Poor Optimisation Algorithms and Growth Models

With the increasing global emphasis on sustainable development, new energy electric vehicles have attracted widespread attention for their environmentally friendly characteristics and reliance on renewable energy sources, making them an important alternative to traditional fuel vehicles. Using data published by the National Bureau of Statistics (NBS), this study explores the advantages of electric vehicles and their development trends through mathematical modelling. The article first adopts a template function fitting method based on the Poor-Rich Optimisation Algorithm (PRO) to predict the development of China's electric vehicle market in the next ten years. On this basis, the paper further proposes linear and saturated growth models to describe and predict the future direction of electric vehicles in China. Through regression analyses of new energy and traditional energy vehicle production over the past five years, the study shows that new energy vehicles are gradually eroding the share of the traditional vehicle market, although in the long run, traditional energy vehicles still dominate the market. The analyses and models in this paper provide scientific data support and development strategies for decision makers in the electric vehicle industry.