Industrial Battery State-of-Health Estimation with Incomplete Limited Data Towards Second-Life Applications

Shaojie Yang; Long Ling; Xiang Li; Jintong Han; Shijie Tong

doi:10.37965/jdmd.2024.562

Authors

Shaojie Yang Department of Mechanical and Aerospace Engineering at the University of California San Diego, La Jolla, CA 92093 USA
Long Ling Department of Mechanical and Aerospace Engineering at the University of California San Diego, La Jolla, CA 92093 USA
Xiang Li Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an, 710049, China https://orcid.org/0000-0003-0569-2176
Jintong Han Department of Mechanical and Aerospace Engineering at the University of California San Diego, La Jolla, CA 92093 USA
Shijie Tong Smartville Inc., Carlsbad, CA 92011 USA

DOI:

https://doi.org/10.37965/jdmd.2024.562

Keywords:

Lithium-ion battery (LIB), second life, SOH estimation, deep learning, battery degradation.

Abstract

Battery State of Health (SOH) estimation is vital across applications ranging from portable electronics to electric vehicles, particularly in second-life applications where accurate prediction becomes complex due to varying degradation levels. This paper introduces a novel SOH estimation model to address the lack of labeled data, employing Domain Adversarial Neural Networks (DANN) combined with 1-dimensional Convolutional Neural Networks (CNN). The proposed method allows for effective transfer of knowledge between diverse battery conditions, enhancing adaptability and efficiency by utilizing both source and target datasets. Experimental results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 1.68% and a Root Mean Squared Error (RMSE) of 2.50%, with minimal data. Specifically, the model requires only one cell of unlabeled data from the second-life target domain, utilizing only the dQ/dV curve for estimation. Proposed model sets a new standard in second-life battery health monitoring and management by effectively leveraging a minimal amount of data for training, this approach offers a robust solution for accurate SOH estimation, particularly in scenarios with limited access to labeled data.

Conflict of Interest Statement

The authors declare no conflicts of interest.