In a significant stride towards sustainable energy reuse, researchers have developed an AI-driven system that breathes new life into waste lithium-ion batteries, particularly those from electric vehicles (EVs). The innovation, detailed in a recent study published in *Applied Sciences*, introduces the AI-Based Waste Battery and Plasma Convergence System (AI-WBPCS). This system not only recovers residual energy from retired EV batteries but also optimizes plasma processes in real-time, paving the way for smarter, greener technologies.
At the heart of the AI-WBPCS are three interconnected AI models. The first, D1, predicts plasma output, while D2 evaluates battery health, and D3 manages adaptive energy-matching control. These models work together on a hybrid STM32–Jetson Nano platform, enabling predictive analysis and closed-loop optimization. The system’s effectiveness was demonstrated using 2P6S retired EV modules, where the battery health evaluation model (D2) achieved an impressive 93.7% accuracy in predicting the state of health (SOH) and a mere 2.3% mean absolute error (MAE) in estimating direct current internal resistance (DCIR).
“The AI-WBPCS represents a paradigm shift in how we view end-of-life batteries,” said lead author Seongsoo Cho from the Department of Applied Artificial Intelligence at Hansung University in Seoul, South Korea. “By integrating AI with plasma technology, we’re not just recycling materials; we’re creating a circular energy ecosystem that maximizes resource utilization and minimizes waste.”
The AI-controlled plasma subsystem maintained output stability within ±2.1%, a notable improvement over conventional rule-based methods that often experience fluctuations exceeding 6%. The overall energy-matching efficiency reached 96.5%, marking a 13% improvement in power coordination performance. This level of precision and efficiency is crucial for applications requiring stable and reliable energy sources.
For the agriculture sector, the implications are substantial. Smart agriculture relies heavily on energy-efficient technologies for tasks such as precision farming, irrigation, and waste management. The AI-WBPCS could power these systems more sustainably, reducing the environmental footprint of agricultural operations. “Imagine a future where waste batteries from EVs are repurposed to drive smart agriculture technologies,” Cho added. “This is not just about energy reuse; it’s about creating a sustainable loop that benefits multiple industries.”
The study also highlights the importance of interpretability in AI systems. Using SHAP (SHapley Additive exPlanations), the researchers identified SOH (46%) and DCIR (29%) as the dominant features influencing AI-driven decisions. This transparency ensures that the AI models are not just effective but also understandable and trustworthy.
The AI-WBPCS is a testament to the potential of AI in redefining end-of-life batteries as adaptive energy resources. As the world grapples with the challenges of waste management and energy sustainability, innovations like the AI-WBPCS offer a beacon of hope. By integrating AI with plasma technology, researchers are not only enhancing operational efficiency but also laying the groundwork for next-generation green technologies that could revolutionize sectors like agriculture, biomedical sterilization, and decentralized wastewater treatment.
This research, led by Seongsoo Cho from the Department of Applied Artificial Intelligence at Hansung University, published in *Applied Sciences*, sets a new standard for AI-empowered electrochemical–plasma systems. It underscores the transformative power of AI in creating sustainable, circular-economy-oriented solutions that could shape the future of energy reuse and environmental stewardship.