Degradation and Failure Mechanisms of Lithium/LiNixCoyMn1–x–yO2 Batteries DOI Creative Commons
Jia Guo, Pengwei Li, F. Del Piccolo

et al.

ACS Energy Letters, Journal Year: 2025, Volume and Issue: unknown, P. 2318 - 2340

Published: April 15, 2025

State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature DOI
Yan Ma, Jiaqi Li, Jinwu Gao

et al.

Energy, Journal Year: 2024, Volume and Issue: 295, P. 131085 - 131085

Published: March 23, 2024

Language: Английский

Citations

27
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

26
Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems DOI Creative Commons
Tuhibur Rahman, Talal Alharbi

Batteries, Journal Year: 2024, Volume and Issue: 10(7), P. 220 - 220

Published: June 22, 2024

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling resilience, promoting renewable integration, driving advancement eco-friendly mobility. However, degradation batteries over time remains significant challenge. This paper presents comprehensive review aimed at investigating intricate phenomenon battery within realm sustainable (EVs). consolidates current knowledge on diverse array factors influencing mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, environmental conditions. The key lithium-ion such as electrolyte breakdown, cycling, temperature, calendar aging, depth discharge are thoroughly discussed. Along with factor, impacts these including capacity fade, reduction density, increase internal resistance, overall efficiency have also been highlighted throughout paper. Additionally, data-driven approaches estimation taken into consideration. Furthermore, this delves multifaceted performance, longevity, sustainability EVs. Finally, main drawbacks, issues challenges related to lifespan addressed. Recommendations, best practices, future directions provided overcome towards system.

Language: Английский

Citations

25
A novel method for remaining useful life of solid-state lithium-ion battery based on improved CNN and health indicators derivation DOI

Yan Ma,

Zhenxi Wang,

Jinwu Gao

et al.

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 220, P. 111646 - 111646

Published: July 1, 2024

Language: Английский

Citations

17
State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression DOI
Fang Li, Yongjun Min, Ying Zhang

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 242, P. 109787 - 109787

Published: Nov. 7, 2023

Language: Английский

Citations

33
Vehicle-cloud-collaborated prognosis and health management for lithium-ion batteries: framework, technics and perspective DOI
Sida Zhou,

Gao Zichao,

Honglei Dong

et al.

Energy storage materials, Journal Year: 2024, Volume and Issue: 70, P. 103531 - 103531

Published: June 1, 2024

Language: Английский

Citations

8
Physics-informed Machine Learning for Accurate SOH Estimation of Lithium-ion Batteries Considering Various Temperatures and Operating Conditions DOI

Chunsong Lin,

Xianguo Tuo, Longxing Wu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134937 - 134937

Published: Feb. 1, 2025

Language: Английский

Citations

1
An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries DOI
Sun Geu Chae, Suk Joo Bae

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110926 - 110926

Published: Feb. 1, 2025

Language: Английский

Citations

1
Determination of Optimal Dataset Characteristics for Improving YOLO Performance in Agricultural Object Detection DOI Creative Commons
Jisu Song,

Dong-Seok Kim,

Eunji Jeong

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(7), P. 731 - 731

Published: March 28, 2025

Recent advances in artificial intelligence and computer vision have led to significant progress the use of agricultural technologies for yield prediction, pest detection, real-time monitoring plant conditions. However, collecting large-scale, high-quality image datasets agriculture sector remains challenging, particularly specialized such as disease images. This study analyzed effects size (320–640+) number labels on performance a YOLO-based object detection model using diverse strawberries, tomatoes, chilies, peppers. Model was evaluated intersection over union average precision (AP), where AP curve smoothed Savitzky–Golay filter EEM. The results revealed that increasing improved certain degree, after which gradually diminished. Furthermore, while from 320 640 substantially enhanced performance, additional increases beyond yielded only marginal improvements. training time graphics processing unit usage scaled linearly with sizes, larger images require greater computational resources. These findings underscore importance an optimal strategy selecting label quantity under resource constraints real-world development.

Language: Английский

Citations

1
Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm DOI
Yongfang Guo, Xiangyuan Yu, Yashuang Wang

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 244, P. 109913 - 109913

Published: Dec. 30, 2023

Language: Английский

Citations

16