Battery aging mode identification across NMC compositions and designs using machine learning

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing durable batteries and optimizing use protocols. Although battery lifetime prediction methods are flourishing, diagnosis of the root causes of aging and degradation have not yet been well developed nor studied for a broad mixture of designs and use cases. Here, we create a machine-learning (ML)-based framework that distinguishes aging modes using multiple electrochemical signatures recorded cycle-by-cycle. The predominant aging behaviors include a combination of loss of active materials in cathode (LAMPE) and a loss of Li inventory (LLI) in Li plating or solid electrolyte interphase (SEI) formation, manifested from 44 batteries representing two cathode chemistries, two electrode loadings, and five charging rates. The aging mode classification accuracy is 86% using features within the first 50 cycles and increases to 88% beyond 225 cycles. The same features can quantify the percentage of end-of-life LAMPE with only 4.3% of error. [Display omitted] •A machine-learning framework for aging mode identification in Li-ion batteries•Aging modes are classified using cycle-by-cycle electrochemical features•Battery design, chemistry, and use condition determine predominant aging modes•Understanding aging phenomena creates opportunities for building more robust batteries Like human beings, batteries age with time and usage. To design more durable lithium (Li)-ion batteries for advanced electric vehicles and stationary storage devices, methods for rapid lifetime prediction and knowledge about the root causes of degradation are necessary. We present a machine-learning-based battery aging mode detection framework using multiple electrochemical signatures recorded during battery charge-discharge cycles. Through this framework, predominant aging modes, such as loss of Li and loss of active materials in the cathode, can be distinguished at an early stage of life. We demonstrate that battery design and use scenario primarily impacts battery aging behavior. Overall, our work provides pathways to accelerate battery development cycles and insights into strategies for minimizing degradations, ultimately improving the lifetime and safety of the next generation of batteries. Electrode design, cathode composition, and use scenario dictate the aging behaviors of a battery and are reflected on the evolving trend of electrothermal signatures collected during cycling. These signatures are the cor