Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features
Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/22/4366 |
| _version_ | 1811187990977839104 |
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| author | Muhammad Umair Ali Amad Zafar Sarvar Hussain Nengroo Sadam Hussain Gwan-Soo Park Hee-Je Kim |
| author_facet | Muhammad Umair Ali Amad Zafar Sarvar Hussain Nengroo Sadam Hussain Gwan-Soo Park Hee-Je Kim |
| author_sort | Muhammad Umair Ali |
| collection | DOAJ |
| description | Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles. |
| first_indexed | 2024-04-11T14:11:55Z |
| format | Article |
| id | doaj.art-e04b38331f9e4881ab258bb486b4d10f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T14:11:55Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e04b38331f9e4881ab258bb486b4d10f2022-12-22T04:19:41ZengMDPI AGEnergies1996-10732019-11-011222436610.3390/en12224366en12224366Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data FeaturesMuhammad Umair Ali0Amad Zafar1Sarvar Hussain Nengroo2Sadam Hussain3Gwan-Soo Park4Hee-Je Kim5School of Electrical Engineering, Pusan National University, Pusan 46241, KoreaDepartment of Electrical Engineering, Wah Engineering College, University of Wah, Wah Cantt 47040, PakistanSchool of Electrical Engineering, Pusan National University, Pusan 46241, KoreaSchool of Electrical Engineering, Pusan National University, Pusan 46241, KoreaSchool of Electrical Engineering, Pusan National University, Pusan 46241, KoreaSchool of Electrical Engineering, Pusan National University, Pusan 46241, KoreaOnline accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.https://www.mdpi.com/1996-1073/12/22/4366battery management system (bms)remaining useful life (rul)support vector machine (svm)partial discharge data (pdd)classification |
| spellingShingle | Muhammad Umair Ali Amad Zafar Sarvar Hussain Nengroo Sadam Hussain Gwan-Soo Park Hee-Je Kim Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features Energies battery management system (bms) remaining useful life (rul) support vector machine (svm) partial discharge data (pdd) classification |
| title | Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features |
| title_full | Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features |
| title_fullStr | Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features |
| title_full_unstemmed | Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features |
| title_short | Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features |
| title_sort | online remaining useful life prediction for lithium ion batteries using partial discharge data features |
| topic | battery management system (bms) remaining useful life (rul) support vector machine (svm) partial discharge data (pdd) classification |
| url | https://www.mdpi.com/1996-1073/12/22/4366 |
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