☆ 4.6 Article

Isolation and Grading of Faults in Battery Packs Based on Machine Learning Methods

ELECTRONICS (2022)

Related references

Note: Only part of the references are listed.

Article Engineering, Electrical & Electronic

Data-Driven Diagnosis of Multiple Faults in Series Battery Packs Based on Cross-Cell Voltage Correlation and Feature Principal Components

Jiale Xie et al.

Summary: This article presents an efficient fault diagnostic scheme for battery packs, utilizing a novel sensor topology and signal processing procedure. Cross-cell voltages are measured to detect electrical abnormalities, and recursive correlation coefficients are calculated to represent system state. Discrete wavelet packet transform is applied to extract diverse characteristic indexes, and principal component analysis is used to refine the most representative fault features. Multiclass relevance vector machine is employed to construct sparse classification models for fault pattern recognition and evaluation of fault types and grades. Experimental verifications demonstrate that the proposed scheme achieves accurate and reliable assessments of different fault specifics, with an 84% success rate in fault isolation and a 90% success rate in fault severity grading.

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS (2023)

Add to Collection

Article Thermodynamics

An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine

Lei Yao et al.

Summary: This study proposes an intelligent fault diagnosis method based on support vector machine for Lithium-ion batteries. It includes denoising, modification of covariance matrix to reduce current fluctuation influence, and optimization of SVM parameters through grid search method to achieve high accuracy and timeliness.

ENERGY (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Data-Driven Fault Diagnosis in Battery Systems Through Cross-Cell Monitoring

Michael Schmid et al.

Summary: Fault diagnosis in Battery Management Systems is crucial to prevent catastrophic consequences such as thermal runaway of battery cells. This study presents a novel data-driven approach based on comparing single cell voltages to detect faults and localize them using Principal Component Analysis. The method shows sensitivity and robustness in detecting abnormalities even under dynamic load profiles and sensor noise, demonstrating fault detection and isolation capabilities in a large battery system.

IEEE SENSORS JOURNAL (2021)

Add to Collection

Article Energy & Fuels

Machine learning-based model for lithium-ion batteries in BMS of electric/hybrid electric aircraft

Seyed Reza Hashemi et al.

Summary: A proposed adaptive lithium-ion battery model, estimated through supervised machine learning, achieves accurate fault diagnosis. Experimental results demonstrate the model's high accuracy and robustness, enhancing the accuracy of battery fault diagnosis.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2021)

Add to Collection

Article Chemistry, Physical

Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution

Qiao Xue et al.

Summary: A novel fault diagnosis and abnormality detection method for battery packs of electric scooters is proposed in this study, utilizing statistical distribution and parameter variation to determine operation states, employing algorithms and screening methods to detect abnormal cells, and identifying fault types and locating faulty cells by calculating fault frequency.

JOURNAL OF POWER SOURCES (2021)

Add to Collection

Article Automation & Control Systems

A Neural Network Based Method for Thermal Fault Detection in Lithium-Ion Batteries

Olaoluwa Ojo et al.

Summary: This article introduces a neural network-based approach for detecting thermal faults in lithium-ion batteries. It relies on the long short-term memory neural network and a residual monitor to accurately estimate temperature and detect faults in real time. This data-driven method is easy to implement and can adapt automatically to changes in the battery.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

Add to Collection

Review Computer Science, Information Systems

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Akash Samanta et al.

Summary: This paper provides a comprehensive review of state-of-the-art ML-based data-driven fault detection/diagnosis techniques for LIB systems, serving as a valuable reference and guide for the research community. ML methods have shown promising advantages over conventional techniques, while current issues and future challenges in LIB fault diagnosis are also addressed for better understanding and guidance.

ELECTRONICS (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Quantified Assessment of Internal Short-Circuit State for 18 650 Batteries Using an Extreme Learning Machine-Based Pseudo-Distributed Model

Jiale Xie et al.

Summary: This research proposes a method for diagnosing battery internal short circuit using thermal behaviors. By integrating thermal effects and utilizing a multiclass relevance vector machine to assess short circuit intensity, ISC faults can be effectively recognized.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2021)

Add to Collection

Article Energy & Fuels

Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks

Ruixin Yang et al.

APPLIED ENERGY (2020)

Add to Collection

Review Engineering, Electrical & Electronic

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

Xiaosong Hu et al.

IEEE INDUSTRIAL ELECTRONICS MAGAZINE (2020)

Add to Collection

Article Automation & Control Systems

Model-Based Battery Thermal Fault Diagnostics: Algorithms, Analysis, and Experiments

Satadru Dey et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)

Add to Collection

Review Green & Sustainable Science & Technology

Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs

Fei Feng et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

Add to Collection

Article Energy & Fuels

Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks

Jichao Hong et al.

APPLIED ENERGY (2019)

Add to Collection

Article Engineering, Multidisciplinary

Voltage fault detection for lithium-ion battery pack using local outlier factor

Zonghai Chen et al.

MEASUREMENT (2019)

Add to Collection

Article Engineering, Electrical & Electronic

Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles

Lei Zhang et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2018)

Add to Collection

Review Chemistry, Physical

Thermal runaway mechanism of lithium ion battery for electric vehicles: A review

Xuning Feng et al.

ENERGY STORAGE MATERIALS (2018)

Add to Collection

Article Energy & Fuels

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Jichao Hong et al.

ENERGIES (2017)

Add to Collection

Article Energy & Fuels

Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

Minhwan Seo et al.

ENERGIES (2017)

Add to Collection

Review Energy & Fuels

Review: Characterization and Modeling of the Mechanical Properties of Lithium-Ion Batteries

Golriz Kermani et al.

ENERGIES (2017)

Add to Collection

Proceedings Paper Engineering, Industrial

Method for classification of battery separator defects using optical inspection

J. Huber et al.

FACTORIES OF THE FUTURE IN THE DIGITAL ENVIRONMENT (2016)

Add to Collection

Article Chemistry, Physical

Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles

Lei Yao et al.

JOURNAL OF POWER SOURCES (2015)

Add to Collection

Article Computer Science, Artificial Intelligence

Multiclass Relevance Vector Machines: Sparsity and Accuracy

Ioannis Psorakis et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2010)

Add to Collection

© Peeref 2019-2024. All rights reserved.