[HTML][HTML] Battery safety: Machine learning-based prognostics

J Zhao, X Feng, Q Pang, M Fowler, Y Lian… - Progress in Energy and …, 2024 - Elsevier
Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …

Critical review of intelligent battery systems: Challenges, implementation, and potential for electric vehicles

L Komsiyska, T Buchberger, S Diehl, M Ehrensberger… - Energies, 2021 - mdpi.com
This review provides an overview of new strategies to address the current challenges of
automotive battery systems: Intelligent Battery Systems. They have the potential to make …

[HTML][HTML] Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis

M Schmid, C Endisch - Journal of Energy Storage, 2022 - Elsevier
Safe operation of large battery storage systems requires advanced fault diagnosis that is
able to detect faults and provide an early warning in the event of a fault. Since Internal Short …

Battery diagnosis: a lifelong learning framework for electric vehicles

J Zhao, J Nan, J Wang, H Ling, Y Lian… - 2022 IEEE vehicle …, 2022 - ieeexplore.ieee.org
Expending manufacturing capacity and development of high-energy batteries greatly
stimulate the growth and applications of electric vehicles (EVs). However, battery …

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Z Huang, L Sugiarto, YC Lu - EcoMat, 2023 - Wiley Online Library
Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid
energy storage. Accurate monitoring of battery health status has been one of the most critical …

[HTML][HTML] A reconstruction-based model with transformer and long short-term memory for internal short circuit detection in battery packs

H Wang, J Nie, Z He, M Gao, W Song, Z Dong - Energy Reports, 2023 - Elsevier
With the rapid growth of the electric vehicle industry, the demand for battery fault detection
methods is also growing. Effective battery defect detection methods help maintain the …

Advanced Data-Driven Fault Diagnosis in Lithium-Ion Battery Management Systems for Electric Vehicles: Progress, Challenges, and Future Perspectives

MGM Abdolrasol, A Ayob, MSH Lipu, S Ansari… - eTransportation, 2024 - Elsevier
Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during
operation, aging, or charging. Robust early fault diagnosis algorithms are essential for …

A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles

J Hong, F Liang, Y Chen, F Wang, X Zhang, K Li… - Energy, 2024 - Elsevier
Accurate and efficient diagnosis of battery voltage abnormality is crucial for the safe
operation of electric vehicles. This paper proposes an innovative battery voltage abnormality …

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

L Zhang, B Xia, F Zhang - Journal of Energy Storage, 2024 - Elsevier
Lithium-ion batteries possess one of the best energy-weight ratios, while safety and
reliability are critical issues for its continued operation. Due to extreme fast-charging, huge …

Short Circuit Estimation in Lithium-Ion Batteries Using Moving Horizon Estimation

J Moon, K Bhaskar, CD Rahn - ASME Letters in …, 2024 - asmedigitalcollection.asme.org
This paper proposes rapid and accurate short circuit estimation under resting condition
using joint moving horizon estimation (MHE). The use of lithium-ion batteries (LiBs) in …