Enhanced battery life prediction with reduced data demand via semi-supervised representation learning  

作　　者：Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung 

机构地区：[1]School of Mechanical,Materials,Mechatronic and Biomedical Engineering,University of Wollongong,NSW 2522,Australia [2]Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation,The Hong Kong Polytechnic University,Kowloon,Hong Kong 999077,China [3]Photonics Research Institute,The Hong Kong Polytechnic University,Kowloon,Hong Kong 999077,China [4]School of Electrical,Computer and Telecommunications Engineering,University of Wollongong,Wollongong,NSW 2522,Australia

出　　处：《Journal of Energy Chemistry》2025年第2期524-534,I0011,共12页能源化学(英文版)

基　　金：supported by the National Natural Science Foundation of China(No.52207229);the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003);the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254);the financial support from the China Scholarship Council(No.202207550010).

摘　　要：Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关 键 词：Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM912[自动化与计算机技术—控制科学与工程]