Fault detection of a Li-ion battery using SVM based machine learning and unscented Kalman filter

Chatterjee, Sayanti; Gatla, Ranjith Kumar; Sinha, Pampa; Jena, Chitralekha; Kundu, Shubhasri; Panda, Babita; Nanda, Lipika; Pradhan, Arjyadhara

doi:10.1016/j.matpr.2022.10.279

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…The Support Vector Machine (SVM) [70] is a method based on statistical theory, which offers improved generalization capabilities compared to neural network algorithms. It requires fewer sample data, reduces the computational costs, and exhibits superior performance in nonlinear and high-dimensional model training.…”

Section: Support Vector Machinementioning

confidence: 99%

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

Zhao,

Guo,

Chen

2024

WEVJ

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With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain.

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Section: Support Vector Machinementioning

confidence: 99%

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

Zhao,

Guo,

Chen

2024

WEVJ

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“…When solving classification problems with high feature dimensions and small sample sizes, support vector machines perform better than other classification methods. Chatterjee et al [15] presented an innovative technique for fault diagnosis in lithium-ion batteries utilizing SVM, and simulation studies have shown that this method can detect faults fast with a high coverage range. Huang et al [16] introduced an adaptive filter-bank approach for extracting spectral features and employed a retrained SVM to detect faults in automotive electric seats.…”

Section: Introductionmentioning

confidence: 99%

“…of the eight channels[12][13][14][15][16][17][18][19] Acoustic velocity in each of the eight channels 20Average acoustic velocity in all eight channels 21-36 Amplification at either ends of each of the eight channels at both ends of the four channels 36-43 Amplification at both ends of the four channels 44-51Time for transit at both ends of the four channels Velocity along each of the four channels 8-11Acoustic velocity in the four channels[12][13][14][15][16][17][18][19] Signal intensity at both ends of the four channels 20-27Quality of signal at both ends of the four channels 28-35 Amplification at both ends of the four channels 36-43Time for transit at both ends of the four channelsDue to the minor variation between flow meters C and D, with C having only one additional sample…”

mentioning

confidence: 99%

“…Velocity along each of the four channels 9Mean flow velocity across all four channels 10-13 Acoustic velocity in four channels 14Average acoustic velocity in all four channels[15][16][17][18][19][20][21][22] Signal intensity at each end of the four channels[23][24][25][26] Flow disturbance in each of the four channels 27Meter performance[28][29][30][31][32][33][34][35] Quality of signal at both ends of the four channels 36-43 Amplification at both ends of the four channels 44-51Time for transit at both ends of the four channels…”

mentioning

confidence: 99%

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A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

Chen,

Zhao,

Shen

et al. 2024

Processes

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To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%.

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“…The data-driven method only needs to input relevant features to train the model and establish the mapping between input and output to track the health status and remaining useful life of the battery. Traditional data-driven methods include regression methods, artificial neural networks (ANN), SVM, 14 gaussian process regression (GPR) 15 and Bayesian 16 and so on. For example, Phattara et al 17 used the deep neural networks (DNN) to predict the battery SOH and RUL, and their developed model was compared with some traditional machine learning algorithms, e.g., SVM, K-Means, ANN, linear regression (LR) and so on.…”

mentioning

confidence: 99%

Prediction of Battery SOH and RUL Based on Cooperative Characteristics in Voltage-Temperature-Time Dimensions

Tang,

Zhang,

Ning

et al. 2023

J. Electrochem. Soc.

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In the prognostics health management (PHM) of marine power lithium batteries, the estimation of the state of health (SOH) and the prediction of remaining useful life (RUL) are of great importance to ensure the battery operational safety and efficiency. In this study, an improved multivariate dimensionality-reduction for Bayesian optimized bi-directional long short-term memory (IMD-BiLSTM) algorithm is proposed and applied to realize SOH estimation and RUL prediction of lithium battery. Specifically, based on the discharging data of lithium battery under specific operating conditions, several health indicators are proposed for the work. On this basis, a collaborative dimensionality reduction algorithm based on Pearson correlation and principal component analysis is proposed to further retain feature information and reduce input dimensionality. Then, the prediction model based on BiLSTM is established, in which the hyperparameters are optimized by Bayesian algorithm. Finally, the effectiveness of the proposed IMD-BiLSTM method is verified by experiments based on the NASA PCoE dataset, and the prediction accuracies of SOH and RUL are emphatically analyzed. Numerical simulation results show that the proposed IMD-BiLSTM-method can effectively extract battery health characteristics and achieve dimensionality reduction. In addition, the proposed IMD-BiLSTM-method significantly outperforms the compared state-of-the-art algorithms in SOH/RUL prediction accuracy and robustness.

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Fault detection of a Li-ion battery using SVM based machine learning and unscented Kalman filter

Cited by 8 publications

References 15 publications

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

Prediction of Battery SOH and RUL Based on Cooperative Characteristics in Voltage-Temperature-Time Dimensions

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