Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning

Reliable safety assurance for high-energy lithium-ion batteries (LIBs) necessitates real-time internal diagnostics. Herein, an implantable resistive sensor is presented, which is based on CsPbBr₃ nanocrystals (NCs) passivated by zinc acetylacetonate and embedded in a flexible polymer matrix. By integrating the sensor with a random forest (RF) model, the system not only tracks internal electrochemical changes in situ but also accurately predicts future charge/discharge curves and capacity up to 4500 s in advance (R 2  = 0.99). Density functional theory (DFT) confirms that Li + adsorption narrows the bandgap and enhances conductivity, leading to reliable resistance modulation. This study presents a pioneering implantable perovskite-based sensor integrated with machine learning (ML) for predictive battery diagnostics. The proposed platform enables early detection of degradation and provides a low-cost, scalable, and intelligent route toward enhanced battery safety and advanced energy storage management.