AI-Based Waste Battery and Plasma Convergence System for Adaptive Energy Reuse and Real-Time Process Optimization

The rapid growth of electric vehicles (EVs) and energy storage systems (ESSs) has accelerated the generation of waste lithium-ion batteries, posing both environmental and industrial challenges. This study proposes and experimentally validates an AI-based Waste Battery and Plasma Convergence System (AI-WBPCS) designed to integrate residual energy recovery from retired EV batteries with adaptive plasma control. The system aims to establish a self-optimizing energy reuse framework that enhances real-time energy utilization, improves plasma process stability, and supports sustainable circular energy ecosystems. The AI-WBPCS consists of three key sub-models: D1 for plasma output prediction, D2 for battery health evaluation, and D3 for adaptive energy-matching control. These models operate synergistically under a hybrid STM32–Jetson Nano platform, enabling predictive analysis and closed-loop optimization. Experimental validation using 2P6S retired EV modules demonstrated that the D2 model achieved a 93.7% SOH prediction accuracy and a 2.3% mean absolute error (MAE) in DCIR estimation. The AI-controlled plasma subsystem maintained output stability within ±2.1%, compared to fluctuations exceeding 6% under conventional rule-based methods. The overall energy-matching efficiency (η) reached 96.5%, representing a 13% improvement in power coordination performance. Interpretability analysis using SHAP (SHapley Additive exPlanations) identified SOH (46%) and DCIR (29%) as the dominant features influencing AI-driven decisions, confirming the physical relevance and transparency of the model. The AI-WBPCS provides a practical pathway toward circular-economy-oriented energy reuse, enabling intelligent, autonomous plasma systems for applications such as smart agriculture, biomedical sterilization, and decentralized wastewater treatment. Overall, this research establishes a new paradigm for AI-empowered electrochemical–plasma systems, where artificial intelligence not only enhances operational efficiency but also redefines end-of-life batteries as adaptive energy resources for next-generation green technologies.