Deep learning prediction of cathode thermal degradation kinetics for battery safety

Abstract

Thermal decomposition of lithium-ion battery cathode materials poses a critical safety challenge for the design and management of high-energy batteries. Accurate determination of reaction kinetic parameters is essential for understanding subsequent thermal runaway behavior. A deep learning framework was developed to automatically predict kinetic parameters from multi‑rate differential thermogravimetry (DTG) curves of pristine (non- delithiated) nickel–cobalt–manganese oxide (NCM) cathode materials under reductive gas attack. By jointly f itting the Johnson Mehl Avrami (JMA) model to experimental benchmarks, we established material-specific kinetic compensation effect (KCE) manifolds for four commercial NCM cathode materials (NCM811, 622, 523, and 111). These manifolds guided the construction of a synthetic dataset containing 20,000 multi-rate samples, designed to emulate complex peak-shift dynamics and instrumental noise. A specialized one-dimensional convolutional neural network (1D CNN) was engineered to process synchronized three-rate DTG sequences (10, 30, and 60 K⋅min⁻¹), allowing the model to implicitly learn the governing kinetic laws from raw signal topology. On the synthetic training set, the multi-rate CNN achieved an exceptional R 2 and pre-exponential factor (lgA), with validation set R 2 of 0.94 for both activation energy (E a ) of 0.92. Beyond numerical precision, the CNN faithfully reconstructed composition-specific KCE lines with slope deviations under 0.5%, proving its ability to capture subtle “kinetic fingerprints” without manual feature engineering. While delithiated cathodes exhibit more complex behaviors, this pristine-based foundation provides a scalable and reproducible trajectory for high- throughput screening and future transfer learning to diverse battery states of charge. By reducing diagnostic latency from hours to milliseconds, this “kinetics intelligence” approach provides a scalable and reproducible tool for high-throughput cathode screening and real-time safety assessment in industrial-scale battery management.