Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

Lee, Pyeong-Yeon; Kwon, Sanguk; Kang, Deokhun; Cho, Inho; Kim, Jong-Hoon

doi:10.1016/j.est.2022.104026

Cited by 13 publications

(5 citation statements)

References 59 publications

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“…There is some loss of accuracy in this process, but there is the possibility that fewer composite variables can be more responsive to the information of the original variables. Lee et al considered the effect of cell inconsistency and applied PCA to extract new features related to degradation and inconsistency, input to LSTM-RNN, and train regression models to predict SOH [214]. Banguero et al applied PCA to internal parameters of the battery energy storage system (capacity, internal resistance, and OCV).…”

Section: Un-supervised Learningmentioning

confidence: 99%

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Yang

Zhang

et al. 2023

Batteries

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Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).

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Section: Un-supervised Learningmentioning

confidence: 99%

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Yang

Zhang

et al. 2023

Batteries

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“…In Ref. 46, the principal component analysis combined with Long short-term memory networks was proposed to estimate SOH.…”

Section: List Of Symbolsmentioning

confidence: 99%

An Adaptive Double Extended Kalman Filter Algorithm Based on Incremental Change Rate for Co-estimation of Battery SOC and Capacity

Lei¹,

Wu²,

Li³

et al. 2023

J. Electrochem. Soc.

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The battery state of charge (SOC) and capacity are important state management indicators of the battery management system, and their estimation accuracy directly affects the safety of power battery use and the driver's driving experience. Since the increment change rate of the estimated variable can reflect the changing trend of the estimated variable, an extended Kalman filter algorithm based on the increment change rate is proposed, on this basis, and an adaptive double-extended Kalman filter algorithm based on incremental change rate is constructed for the co-estimation of SOC and capacity of batteries. The tests under various operating conditions show that the target algorithm proposed in this paper has greater advantages over the traditional adaptive double-extended Kalman filter algorithm, and the maximum absolute error value (MAE) and root mean square error (RMSE) of the target algorithm can be reduced by 36.3% and 74.4% (SOC), 95.5% and 97.6% (capacity) compared with the traditional adaptive double-extended Kalman filter algorithm under DST operating conditions; The MAE and RMSE of the target algorithm can be reduced by 79.1% and 92.3% (SOC), 95.4% and 96.2% (capacity) under BBDST operating conditions.

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“…Particle swarm optimization algorithm and multi-seed region-growing technique provided the best segmentation results that are closer to the ground truth images. Lee et al [32] merged the PCA based optimized feature extraction merged with nonlinear regression model for improving state-of-health prediction. The prediction performance showed well in feature extraction based on PCA takes into account various degradation features.…”

Section: Pretreatment Of Imagementioning

confidence: 99%

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Gou

Qingying

Yao

et al. 2023

Meas. Sci. Technol.

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Active infrared thermography has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis algorithm with sliding-window mechanism and the 1DResnet50-CBAM (One Dimensional - Residual Neural Network - Convolutional Block Attention Module) model was proposed for the reconstructed image sequence of active infrared thermography to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using principal component analysis algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional (3D) defect data were transformed into one-dimensional (1D) temporal infrared thermal signal. Moveover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion principal component analysis algorithm with sliding-window mechanism and 1DResnet50-CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and quantitative analysis using infrared thermal detection technology.

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Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

Cited by 13 publications

References 59 publications

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

An Adaptive Double Extended Kalman Filter Algorithm Based on Incremental Change Rate for Co-estimation of Battery SOC and Capacity

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

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