Enhancing Battery Performance: Ai-Driven State Level Prediction for Electric Vehicles

Abstract

In the realm of improving battery performance for electric vehicles (EVs), artificial intelligence is crucial for refining battery management through state-level predictions.AI-based models, such as the  RandomForestRegressor implemented in this project, offer sophisticated methods for forecasting the lifespan of EV batteries. By learning from vast amounts of operational data, such as discharge times, voltage fluctuations, and charging cycles, AI can identify complex patterns and interactions that traditional models or optimization algorithms may overlook. The integration of AI enhances the accuracy and reliability of predictions, enabling proactive battery maintenance and reducing the likelihood of sudden failures. This not only extends the overall lifespan of the battery but also improves energy efficiency and operational effectiveness. AI’s capacity to manage and interpret real-time data in dynamic environments makes it indispensable for real-world EV applications. Furthermore, AI enables predictive maintenance strategies, allowing operators to anticipate battery health degradation and optimize charging schedules, thus preventing overcharging or deep discharge scenarios. This translates into lower operating costs and a more efficient charging infrastructure. Compared to earlier approaches, AI’s ability to handle non-linear, multidimensional data sets ensures superior prediction accuracy and lower error rates. Overall, this AI-driven state-level prediction system fosters a more sustainable, energy-efficient, and reliable EV ecosystem, directly addressing one of the key challenges in EV adoption: battery performance and longevity.