Beyond Traditional Forecasting: Machine Learning and Adaptive Algorithms in EV Sales Predictions

The Electric Vehicle market is growing fast, creating new challenges for sales predictions and market dynamics. Traditional forecasting methods like linear regression and expert judgment struggle with the market's non-linear and complex nature. To improve accuracy, new models such as machine learning and Adaptive Optimized Grey Models are being applied to predict EV sales, which is discussed in this review paper. These models dynamically adjust parameters and account for external factors, offering better precision. AOGMs, in particular, utilize data preprocessing and buffering to minimize external disturbances and enhance prediction smoothness. Techniques like the Particle Swarm Optimization help these models more effectively capture the EV sales trends. Additionally, adaptive grey models are not only essential for forecasting sales but also for identifying market risks like overcapacity or supply shortages. These advanced methods provide a more robust framework for understanding and anticipating changes in the rapidly evolving the EV market.