Artificial Intelligence Integration in Blade Battery Systems for EV Performance Optimization

The advancement of electric vehicles (EVs) demands battery technologies that combine high performance, safety, and cost efficiency. Blade battery technology, characterized by its long, thin cell design, enhances structural stability, thermal safety, and energy density, making it a promising solution for next-generation EVs. However, challenges remain in optimizing performance, extending lifespan, and ensuring efficient thermal management under diverse driving conditions. This study introduces an AI-based optimization framework for blade battery technology, leveraging machine learning algorithms and real-time data analytics to
enhance performance and safety. The system predicts battery degradation patterns, monitors thermal distribution, and dynamically regulates charging/discharging processes to maximize efficiency. By integrating artificial intelligence with blade battery architecture, the proposed
approach improves cycle life, reduces the risk of thermal runaway, and ensures stable highpower output. Experimental validation and simulation results indicate significant
improvements in energy utilization, thermal stability, and overall battery longevity. This work demonstrates that AI-driven blade battery technology can accelerate the deployment of highperformance EVs, supporting sustainable and reliable electric mobility.