AIDriven Predictive Modeling for Optimizing Battery Performance in Electric Vehicles

The effectiveness and dependability of electric cars are significantly influenced by the performance and longevity of batteries. Intelligent systems that can monitor, forecast, and optimize battery behavior under various operating situations are becoming more and more necessary as the use of electric vehicles grows. Conventional battery management systems frequently use static models that can't adjust to changes in load, temperature, and consumption patterns in real time. An AI-driven predictive modeling strategy for improving battery performance in electric cars is presented in this research. The suggested system analyzes past battery data using machine learning approaches to forecast important factors including performance degradation, health status, and state of charge. The model makes precise predictions that facilitate effective battery use and maintenance by figuring out intricate correlations between input factors. Additionally, a web-based platform is created that lets users enter battery parameters and see real-time predictions. In order to reduce noise and enhance model accuracy, the dataset utilized in this work has undergone preprocessing. According to experimental findings, the suggested method improves decision-making in electric vehicle energy management, decreases degradation, and increases battery efficiency. This strategy can be expanded to include smart transit options and sophisticated battery management technologies.