Artificial intelligence-driven monitoring and sustainable management of lithium-ion battery waste: integrating advanced sensing, environmental remediation, and circular resource recovery.

The fast growth of electric cars and portable devices has caused a huge rise in used lithium‑ion batteries. This creates big environmental problems but also chances to recover valuable resources. Our research looks at combining artificial intelligence, modern sensors, and clean‑up methods to improve how battery waste is handled and reused. Machine‑learning algorithms will be used to: – Track how batteries degrade – Predict the chance of thermal runaway (dangerous overheating) – Identify the battery chemistry so they can be sorted automatically We also add environmental impact models that compare two recovery routes: hydrometallurgical and pyrometallurgical processes. By merging predictions with sensor data, the system aims to cut toxic emissions

and prevent soil contamination that can happen with improper disposal. Applying circular‑economy ideas, the approach focuses on reclaiming critical materials such as cobalt, lithium, and nickel efficiently. Overall, this framework offers a clear plan for building smart waste‑management tools and provides decision‑support for environmental engineers and policy makers who want to lower the ecological impact of energy‑storage technologies.

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