A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management
Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial...
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9454160/ |
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doaj-b637435771904992b2dae13625ea87382021-06-18T23:00:52ZengIEEEIEEE Access2169-35362021-01-019861668619310.1109/ACCESS.2021.30890329454160A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery ManagementShahid A. Hasib0https://orcid.org/0000-0003-4511-0310S. Islam1https://orcid.org/0000-0002-1795-2089Ripon K. Chakrabortty2https://orcid.org/0000-0002-7373-0149Michael J. Ryan3https://orcid.org/0000-0002-6335-3773D. K. Saha4https://orcid.org/0000-0002-9772-4130Md H. Ahamed5https://orcid.org/0000-0001-9389-2666S. I. Moyeen6https://orcid.org/0000-0003-3701-9357Sajal K. Das7https://orcid.org/0000-0002-0596-4816Md F. Ali8Md R. Islam9Z. Tasneem10Faisal R. Badal11https://orcid.org/0000-0002-7463-7675Department of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshSchool of Engineering and Information Technology, University of New South Wales (UNSW), Canberra, ACT, AustraliaCapability Systems Centre (CSC), University of New South Wales (UNSW), Canberra, ACT, AustraliaDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshBattery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.https://ieeexplore.ieee.org/document/9454160/Battery datasetsbattery data repositoryremaining useful life (RUL)battery managementli-ion batteryRUL prediction methods |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Shahid A. Hasib S. Islam Ripon K. Chakrabortty Michael J. Ryan D. K. Saha Md H. Ahamed S. I. Moyeen Sajal K. Das Md F. Ali Md R. Islam Z. Tasneem Faisal R. Badal |
| spellingShingle |
Shahid A. Hasib S. Islam Ripon K. Chakrabortty Michael J. Ryan D. K. Saha Md H. Ahamed S. I. Moyeen Sajal K. Das Md F. Ali Md R. Islam Z. Tasneem Faisal R. Badal A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management IEEE Access Battery datasets battery data repository remaining useful life (RUL) battery management li-ion battery RUL prediction methods |
| author_facet |
Shahid A. Hasib S. Islam Ripon K. Chakrabortty Michael J. Ryan D. K. Saha Md H. Ahamed S. I. Moyeen Sajal K. Das Md F. Ali Md R. Islam Z. Tasneem Faisal R. Badal |
| author_sort |
Shahid A. Hasib |
| title |
A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management |
| title_short |
A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management |
| title_full |
A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management |
| title_fullStr |
A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management |
| title_full_unstemmed |
A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management |
| title_sort |
comprehensive review of available battery datasets, rul prediction approaches, and advanced battery management |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2021-01-01 |
| description |
Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling. |
| topic |
Battery datasets battery data repository remaining useful life (RUL) battery management li-ion battery RUL prediction methods |
| url |
https://ieeexplore.ieee.org/document/9454160/ |
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