Battpulse: An AI-Powered Electric Car Battery Health Analyzer

Electric cars are rapidly gaining adoption as an eco-friendly alternative to traditional cars, making battery health a key factor for performance and reliability. Over time, battery efficiency decreases due to repeated charging cycles, continuous usage, and varying environmental conditions. Monitoring battery health is essential to maintain optimal performance and extend battery life. This project presents a web-based system for predicting battery health using machine learning techniques. Users can provide simple inputs such as battery age, charge cycles, daily usage, and charging type through an interactive interface. The backend, developed using Flask, processes the data and communicates with a trained Random Forest model. The model analyzes the inputs and predicts battery health as a percentage. The results are displayed using visual elements like charts and battery indicators for better understanding. The system also provides insights into future battery degradation trends. This helps users make informed decisions about battery maintenance and usage. Overall, the solution offers a practical, efficient, and user-friendly approach for battery health monitoring in electric cars.