Prediction and Impact Analysis of Energy Vehicle Development Based on Nonlinear Optimization APH Method

New energy electric vehicles have seen rapid growth in recent years due to their low pollution, energy efficiency, and peak load-shifting capabilities. However, evaluations of the developmental trends in the new energy vehicle industry remain relatively limited. In this paper, we first employed the nonlinear optimization APH method to construct an indicator system and its weight system, thus modeling the main factors influencing the development of new energy electric vehicles. The method's consistency parameter CR is in the order of 10
^-4
, indicating high feasibility. Then, we utilized ARIMA time series forecasting and multiple linear regression to model and predict the future ten years' development of new energy electric vehicle industry. Evaluated comprehensively by multiple assessment indicators like RMSE and F-values, this method demonstrates high precision and good fitting effects. Finally, our optimized population competition model innovatively analyzed the interactive effects of the global new energy and traditional energy vehicle industries, and the two may stabilize after a period of joint development in the future, and new energy electric vehicles may dominate.