Data-Driven Cycle Life Prediction of Lithium Metal-Based Rechargeable Battery Based on Discharge/Charge Capacity and Relaxation Features.

ABSTRACT: Achieving precise estimates of battery cycle life is a formidable challenge due to the nonlinear nature of battery degradation. This study explores an approach using machine learning (ML) methods to predict the cycle life of lithium-metal-based rechargeable batteries with high mass loading LiNi_0.8Mn_0.1Co_0.1O₂ electrode, which exhibits more complicated and electrochemical profile during battery operating conditions than typically studied LiFePO₄/graphite based rechargeable batteries. Extracting diverse features from discharge, charge, and relaxation processes, the intricacies of cell behavior without relying on specific degradation mechanisms are navigated. The best-performing ML model, after feature selection, achieves an R² of 0.89, s

SUBMITTER: Si Q 

PROVIDER: S-EPMC11633329 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature