Data-driven available capacity estimation of lithium-ion batteries based on fragmented charge capacity.

ABSTRACT: Efficient and accurate available capacity estimation of lithium-ion batteries is crucial for ensuring the safe and effective operation of electric vehicles. However, incomplete charging cycles in practical applications challenge conventional methods. Here we manipulate fragmented charge capacity data to estimate available capacity without complete charging information. Considering correlation, charging time, and initial state of charge, 36 feature combinations are available for estimation. The basic machine learning model is established on 11,500 cyclic samples, and a transfer learning model is fine-tuned and validated on multiple datasets. The validation results indicate that the best root-mean-square error for the basic model is 0.012. Furthermore, the RMSE demonstrates consistent stabil

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC11850593 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature