Chemistry-aware battery degradation prediction under simulated real-world cyclic protocols
This work addresses the challenge of predicting battery degradation under stochastic real-world conditions for energy storage technologies, offering a scalable framework that integrates chemical engineering and data science, though it is incremental in combining existing methods for a specific domain.
The researchers tackled battery degradation prediction under real-world cyclic conditions by integrating hidden Markov processes, automated testing, and a chemistry database with machine learning, achieving accurate predictions of battery life and knee points while revealing six distinct failure mechanisms through electrical signal analysis.
Battery degradation is governed by complex and randomized cyclic conditions, yet existing modeling and prediction frameworks usually rely on rigid, unchanging protocols that fail to capture real-world dynamics. The stochastic electrical signals make such prediction extremely challenging, while, on the other hand, they provide abundant additional information, such as voltage fluctuations, which may probe the degradation mechanisms. Here, we present chemistry-aware battery degradation prediction under dynamic conditions with machine learning, which integrates hidden Markov processes for realistic power simulations, an automated batch-testing system that generates a large electrochemical dataset under randomized conditions, an interfacial chemistry database derived from high-throughput X-ray photoelectron spectroscopy for mechanistic probing, and a machine learning model for prediction. By automatically constructing a polynomial-scale feature space from irregular electrochemical curves, our model accurately predicts both battery life and critical knee points. This feature space also predicts the composition of the solid electrolyte interphase, revealing six distinct failure mechanisms-demonstrating a viable approach to use electrical signals to infer interfacial chemistry. This work establishes a scalable and adaptive framework for integrating chemical engineering and data science to advance noninvasive diagnostics and optimize processes for more durable and sustainable energy storage technologies.