Online estimation of lithium-ion battery health status based on transfer learning and deep neural network DOI
Yan Qiu Chen,

Yuwei Tang,

Lin Jian

et al.

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 3, 2025

Language: Английский

A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges DOI
Yanxin Xie, Shunli Wang, Gexiang Zhang

et al.

Journal of Energy Chemistry, Journal Year: 2024, Volume and Issue: 97, P. 630 - 649

Published: June 19, 2024

Language: Английский

Citations

34
Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis: A comprehensive review DOI Creative Commons
Xinghao Du, Jinhao Meng, Yassine Amirat

et al.

Journal of Energy Chemistry, Journal Year: 2024, Volume and Issue: 95, P. 464 - 483

Published: April 12, 2024

Lithium-ion batteries have extensive usage in various energy storage needs, owing to their notable benefits of high density and long lifespan. The monitoring battery states failure identification are indispensable for guaranteeing the secure optimal functionality batteries. impedance spectrum has garnered growing interest due its ability provide a valuable understanding material characteristics electrochemical processes. To inspire further progress investigation application spectrum, this paper provides comprehensive review determination utilization spectrum. sources inaccuracies systematically analyzed terms frequency response characteristics. applicability utilizing diverse features diagnosis prognosis is elaborated. Finally, challenges prospects future research discussed.

Language: Английский

Citations

25
Dynamic conditions-oriented model-data fused framework enabling state of charge and capacity accurate co-estimation of lithium-ion battery DOI

C.H.E.N. Shouxuan,

Z.H.A.N.G. Shuting,

G.E.N.G. Yuanfei

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134668 - 134668

Published: Jan. 1, 2025

Language: Английский

Citations

3
Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering DOI Creative Commons
Xingjun Li,

Dan Yu,

Søren Byg Vilsen

et al.

Journal of Energy Chemistry, Journal Year: 2024, Volume and Issue: 92, P. 591 - 604

Published: Jan. 29, 2024

State of health (SOH) estimation e-mobilities operated in real and dynamic conditions is essential challenging. Most existing estimations are based on a fixed constant current charging discharging aging profiles, which overlooked the fact that profiles random not complete application. This paper investigates influence feature engineering accuracy different machine learning (ML)-based SOH acting recharging sub-profiles where realistic battery mission profile considered. Fifteen features were extracted from partial considering factors such as starting voltage values, charge amount, sliding windows. Then, selected selection pipeline consisting filtering supervised ML-based subset selection. Multiple linear regression (MLR), Gaussian process (GPR), support vector (SVR) applied to estimate SOH, root mean square error (RMSE) was used evaluate compare performance. The results showed can improve by 55.05%, 2.57%, 2.82% for MLR, GPR SVR respectively. It demonstrated with lower voltage, large charge, window size more likely achieve higher accuracy. work hopes give some insights into recharges performance tries fill gap effective between theoretical study

Language: Английский

Citations

15
Lithium-Ion Battery State of Health Estimation Using a Hybrid Model with Electrochemical Impedance Spectroscopy DOI
Wu Jian, Jinhao Meng, Mingqiang Lin

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 252, P. 110450 - 110450

Published: Aug. 22, 2024

Language: Английский

Citations

12
Refined lithium-ion battery state of health estimation with charging segment adjustment DOI
Kun Zheng, Jinhao Meng, Zhipeng Yang

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 375, P. 124077 - 124077

Published: Aug. 6, 2024

Language: Английский

Citations

9
State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding DOI Creative Commons

Muhammad Rifqi Fauzi,

Novanto Yudistira, Wayan Firdaus Mahmudy

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 14659 - 14670

Published: Jan. 1, 2024

In the world of modern energy, Lithium-Ion batteries reign supreme, offering rechargeability, sustainability, and long-term energy storage. However, their lifespan is not infinite, calling for accurate prediction remaining life under various conditions. Deep learning shines in this domain, with Transformer architecture blossoming as a powerful tool time series forecasting. This research dives into data collection, processing, model design, training, evaluation, making key methodological contributions to battery prediction. Notably, SGEformer model, enhanced growth seasonal embedding, emerges groundbreaking innovation. Comparing ETSformer, Informer, Reformer, Transformer, LSTM reveals its unique strengths. With an impressive MSE score 0.000117, establishes itself highly effective prediction, highlighting value embedding boosting accuracy. propels state-of-the-art state-of-health robust foundation precise reliable forecasts.

Language: Английский

Citations

8
Adversarial Training Defense Strategy for Lithium-ion Batteries State of Health Estimation with Deep Learning DOI
Kun Zheng, Yijing Li, Zhipeng Yang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134411 - 134411

Published: Jan. 1, 2025

Language: Английский

Citations

1
Battery State of Health Estimation Based on Energy Features and ResNet‐SVR Model DOI
Yueying Xiao,

LU Zhen-feng,

Chao-Chun Huang

et al.

Quality and Reliability Engineering International, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

ABSTRACT The accurate estimation of battery state health (SOH) is crucial for monitoring status and alerting users to replace the degraded batteries. Energy, as a comprehensive feature integrating current voltage, can effectively reflect amount stored energy, graphical energy features contain more information. In this study, hybrid ResNet‐SVR model based on proposed estimate battery's SOH. sequential are first constructed during constant charging process. Then they transformed into two‐dimensional structure through sliding window inputs, which shows significant differences between cycles. uses ResNet extract relevant information from data SVR regression prediction. It combines advantages convolutional neural network extraction prediction achieve better results. method validated two datasets achieves satisfactory results in estimation, with an average mean absolute error 0.0072 percentage 0.83%. Overall, our findings highlight potential SOH significantly enhances performance management system various applications.

Language: Английский

Citations

1
State Estimation of Lithium-Ion Batteries via Physics-Machine Learning Combined Methods: A Methodological Review and Future Perspectives DOI
Hanqing Yu, Hongcai Zhang, Zhengjie Zhang

et al.

eTransportation, Journal Year: 2025, Volume and Issue: unknown, P. 100420 - 100420

Published: April 1, 2025

Language: Английский

Citations

1