From equation-centric modeling to transferable representation learning in battery lifetime prediction
Authors:
- Jiahuan LuJLJiahuan Lu
- Kaixiang HuangKHKaixiang Huang
- Rui ChenRCRui Chen
- Quanqing YuQuanqing Yu
- You XuYXYou Xu
- Jiehao LiJLJiehao Li
- 4 June 2026
- journal article
- Published by Elsevier in Journal of Energy Chemistry
- Vol. 120, 422-449
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (52407246)
- Basic and Applied Basic Research Foundation of Guangdong Province (2025A1515010354)
- Guangdong Provincial Department of Science and Technology
- Guangzhou Municipal Science and Technology Project (2025A04J3660)
This publication has 198 references indexed in Scilit:
- Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approachInternational Journal of Energy Research, 2021
- Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networksInternational Journal of Energy Research, 2020
- Method for estimating capacity and predicting remaining useful life of lithium-ion batteryApplied Energy, 2014
- A naive Bayes model for robust remaining useful life prediction of lithium-ion batteryApplied Energy, 2014
- A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimationApplied Energy, 2012
- The use of mathematical modeling in the design of lithium/polymer battery systemsElectrochimica Acta, 1995
- Electro-thermal modelling of polymer lithium batteries for starting period and pulse powerJournal of Power Sources, 1995
- Accelerated life cycle testing and analysis for early failure prediction using two types of lead/acid batteriesJournal of Power Sources, 1992
- Battery lifetime prediction by pattern recognition. Application to lead-acid battery life-cycling test dataJournal of Power Sources, 1984
- A new algorithm for Monte Carlo simulation of Ising spin systemsJournal of Computational Physics, 1975