Supervisor of Master's Candidates
Name (Simplified Chinese): 吴慕遥
Name (Pinyin): wumuyao
Date of Birth: 1995-12-08
E-Mail:
Date of Employment: 2022-12-27
School/Department: 车辆工程系
Education Level: With Certificate of Graduation for Doctorate Study
Business Address: 安徽省合肥市屯溪路193号合肥工业大学格物楼515
Gender: Male
Degree: Doctoral Degree in Engineering
Professional Title: Lecturer
Status: Employed
Alma Mater: 中国科学技术大学
Supervisor of Master's Candidates
Discipline: Automobile Engineering
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Probabilistic state of health estimation and environmentally optimized retirement threshold for lithium-ion batteries based on deep learning and Gaussian process regression
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Impact Factor:10.7
DOI number:10.1016/j.est.2026.124251
Journal:Journal of Energy Storage
Key Words:Lithium-ion batteries; State of health estimation; Temporal; convolutional network; Gaussian process regression; Uncertainty quantification; Decommissioning thresholds
Abstract:The intensifying reliability requirements for contemporary energy storage systems necessitate advanced State of Health (SOH) monitoring strategies that simultaneously address battery degradation patterns and environmental sustainability. This research introduces a hybrid Temporal Convolutional Network (TCN)-Bidirectional Long Short-Term Memory (BiLSTM) architecture designed to capture multi-scale temporal features, utilizing Gaussian Process Regression (GPR) to deliver probabilistic SOH estimations with robust uncertainty quantification. Based on the SOH estimation, a circular carbon emission intensity (CEI) metric is introduced to characterize the evolution of battery carbon burden by quantifying carbon emissions per unit of delivered energy. This integrated framework establishes a novel methodology coupling real-time health monitoring with lifecycle carbon assessment, facilitating dynamic retirement decisions that overcome the limitations of traditional, fixed SOH thresholds. Validation using NASA and CALCE battery datasets demonstrates superior precision, with RMSE and MAE values maintained below 0.66% and 0.54%, respectively. Furthermore, the observed CEI evolution effectively identifies the transition from stable aging to accelerated carbon burden growth, providing a more sustainable criterion for battery retirement and overall lifecycle management.
Note:中科院2Top
Co-author:Shiyi Yang,Yiqing Yang,Muyao Wu,Zhongwen Zhu,Yan Ma,Jifang Hu
First Author:Shuhua Li
Indexed by:Essay collection
Document Code:124251
Discipline:Engineering
Document Type:J
Volume:180
Translation or Not:no
Date of Publication:2026-08-16
Included Journals:SCI、EI
Links to published journals:https://www.sciencedirect.com/science/article/pii/S2352152X26039150
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