Battery Optimization Using Machine Learning

The ever-growing mountain of e-waste, fueled by short-lived batteries, threatens our planet's security. Discarded batteries leach toxic materials, jeopardizing environmental health and safety. This escalating crisis demands a paradigm shift. This research proposes a groundbreaking solution: leveraging Machine Learning to optimize battery lifecycles, dramatically reducing waste and its associated environmental security risks.Our work integrates advancements in solid-state electrolytes with data-driven ML techniques to enhance battery management, contributing to sustainable security solutions. We employ clustering algorithms to categorize batteries for targeted optimization and implement regression models to predict and mitigate battery degradation. Our approach goes beyond traditional capacity and voltage metrics, incorporating crucial factors like temperature and depth of discharge for a holistic E-waste security assessment.This research paves the way for a secure and sustainable future by establishing a critical link between AI and responsible e-waste management. By promoting collaboration between researchers, policymakers, and consumers, we can leverage technology to decouple our reliance on electronics from the environmental burden of battery waste. This work presents a powerful solution with immense potential to secure a cleaner and more sustainable future.