Satellite Lithium-ion Battery Remaining Useful Life Estimation by Coyote Optimization Algorithm

Abdelghafar, Sara; Goda, Essam; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1109/icicis46948.2019.9014752

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…The study showed that GA-SVR with a radial basis function (RBF) and Particle Swarm Optimization (POS)-SVR with an RBF produced more accurate prediction results. Abdelghafar et al [35] demonstrated that Coyote Optimization Algorithm (COA) with SVR performed better in RUL prediction compared to conventional SVM. COA was used for parameter optimization of SVM.…”

Section: Support Vector Machine (Svm)-based Strategiesmentioning

confidence: 99%

“…As capacity is an important indicator of RUL, thus Abdelghafar et al [35] proposed a combined Coyote Optimization Algorithm (COA) with a Support Vector Regression (SVR)-based capacity estimation approach for RUL prediction. Further, a comparative analysis also showed that the COA-SVR-based approach is more effective compared to the conventional SVR algorithm with randomized parameter selection and a relevance vector machine (RVM).…”

Section: Support Vector Machine (Svm)-based Strategiesmentioning

confidence: 99%

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Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review

Samanta

Williamson

2023

IEEJ Journal IA

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Lithium-ion batteries (LIBs) are used to power a range of applications starting from portable consumer electronics to electric vehicles and grid-tied energy storage systems. Now, with the increasing application of LIB in high power and sophisticated applications, it is of great significance to predict the remaining useful life (RUL) for reliable operation and to protect the battery pack from unwanted incidents including catastrophic failure. Real-time information on RUL is essential to predict battery failure condition resulting in effective prevention or at least reduction of the damage that may cause by the battery failure. Moreover, accurate RUL is extremely helpful for scheduling routine maintenance and necessary replacement at the end of its useful life. Consequently, RUL prediction has become a topic of interest to researchers. There are several RUL estimation techniques proposed in the last decade where machine learning (ML)-based techniques showed superiority in terms of accuracy, adaptability, and modeling. Therefore, ML-based RUL prediction methods are comprehensively reviewed based on their essential performance parameters in this paper. A detailed discussion on the issues, challenges, trends, and future research scopes are also presented to provide clear guideline to the researchers.

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Section: Support Vector Machine (Svm)-based Strategiesmentioning

confidence: 99%

Section: Support Vector Machine (Svm)-based Strategiesmentioning

confidence: 99%

Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review

Samanta

Williamson

2023

IEEJ Journal IA

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“…A multilayer bi-directional long short-term memory (Bi-LSTM)-based fault prediction algorithm was proposed by J Gao et al [19] to improve the prediction accuracy of intelligent algorithms. In order to avoid excessive waste of resources, Abdelghafar et al proposed an optimized regression method based on the coyote optimization algorithm (COA) and support vector regression (SVR) to predict the remaining battery life [20]. Song et al [21] presented an iterative update method to improve the long-term predictive performance of battery remaining life prediction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

Hui,

Cheng,

Jiang

et al. 2023

Applied Sciences

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This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications.

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Advances in Coyote Optimization Algorithm: Variants and Applications

Meraihi

Gabis

Ramdane-Chérif

et al. 2022

Advances in Computational Intelligence and Communication

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Satellite Lithium-ion Battery Remaining Useful Life Estimation by Coyote Optimization Algorithm

Cited by 9 publications

References 18 publications

Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review

Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

Advances in Coyote Optimization Algorithm: Variants and Applications

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