Analysis of Machine Learning Techniques for the Intelligent Diagnosis of Ni-MH Battery Cells

Ortiz, Juan P.; Valladolid, Juan D.; Garcia, Cristian L.; Novillo, Gina P.; Berrezueta, Felipe

doi:10.1109/ropec.2018.8661446

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…We first look at unsupervised and supervised learning approaches. In a study (Ortiz et al, 2019) In another work (Haider et al, 2020), a K-shape-based time series hierarchical clustering algorithm is applied to perform defect detection for lead-acid batteries.…”

Section: Battery Fault Diagnosis Degradation Analysis and Property Classificationsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

108

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Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

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Section: Battery Fault Diagnosis Degradation Analysis and Property Classificationsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

108

View full text Add to dashboard Cite

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“…This fact makes it more difficult to achieve a universal model that could accurately predict the state of multiple battery parameters regardless of battery aging, variations in road conditions, changes in driver's driving behavior, and other uncertainties in environmental and operating conditions. Ortiz et al [51] conducted a comparative study to demonstrate the effectiveness of five commonly used algorithms, namely, k-nearest neighbors (k-NN), LR, Gaussian naive Bayes (GNB), KSVM and NN, to classify the unbalanced and damaged Ni-MH battery cells. While this test was not conducted for LIB cells, this study showed that the NN-based diagnostic tool provides a high evaluation score with correctly classified data.…”

Section: Ann-based Fault Diagnosis Methodsmentioning

confidence: 99%

“…There are two other variants in the support vector domain, namely, KSVM and relevance vector machine (RVM), that are also reported in the focused domain. Ortiz et al [51] employed KSVM for classifying faulty and non-faulty battery cells and revealed that the KSVM method has a greater classification efficiency compared to conventional SVM. This is especially because the characteristics and the operations of the battery cell can be better adapted by the function of the radial base kernel.…”

Section: Svm-based Fault Diagnosis Methodsmentioning

confidence: 99%

“…In reference [64] Tagade et al used GPR for degradation mode diagnosis in LIB. In reference [51] Ortiz et al evaluated the classification efficiency of five different supervised ML methods where Gaussian naive Bayes (GNB) is one of the methods employed for the LIB fault detection. The study showed that each method has its advantages over others where GNB demonstrated high efficiency in classifying battery cells that are unbalanced and damaged.…”

Section: Gpr Based Fault Diagnosis Methodsmentioning

confidence: 99%

“…This method is the simplest method for two-class classification and has shown very good performance in linear and nonlinear regression. However, so far very few researchers have employed LR for fault detection/diagnosis in the LIB system [51].…”

Section: Logistic Regressionmentioning

confidence: 99%

See 2 more Smart Citations

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

2021

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Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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