Data-driven available capacity estimation of lithium-ion batteries based on fragmented charge capacity
Zhen Zhang, Xin Gu, Yuhao Zhu, Teng Wang, Yichang Gong, Yunlong Shang
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 stability across different datasets in the transfer learning model, with fluctuations within 0.5% when considering feature combinations across cycles with spacings of 5, 10, and 20. This work highlights the promise of available capacity estimation using actual, readily accessible fragmented charge capacity data. Zhen Zhang and colleagues use machine learning to extract lithium-ion battery available capacity from fragmented charge data. The work shows sufficient flexibility for applications in real-world scenarios, a step towards more accurate battery capacity estimation.