State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine
An adaptive online sequential extreme learning machine (AOS-ELM) is proposed to predict the state-of-charge of the battery cells at different ambient temperatures. With limited samples and sequential data for training during the initial design stage, conventional neural network training gives higher...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/4/711 |
| _version_ | 1811263982322843648 |
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| author | Cheng Siong Chin Zuchang Gao |
| author_facet | Cheng Siong Chin Zuchang Gao |
| author_sort | Cheng Siong Chin |
| collection | DOAJ |
| description | An adaptive online sequential extreme learning machine (AOS-ELM) is proposed to predict the state-of-charge of the battery cells at different ambient temperatures. With limited samples and sequential data for training during the initial design stage, conventional neural network training gives higher errors and longer computing times when it maps the available inputs to SOC. The use of AOS-ELM allows a gradual increase in the dataset that can be time-consuming to obtain during the initial stage of the neural network training. The SOC prediction using AOS-ELM gives a smaller root mean squared error in testing (and small standard deviation in the trained results) and reasonable training time as compared to other types of ELM-based learnings and gradient-based machine learning. In addition, the subsequent identification of the cells’ static capacity and battery parameters from actual experiments is not required to estimate the SOC of each cell and the battery stack. |
| first_indexed | 2024-04-12T19:54:57Z |
| format | Article |
| id | doaj.art-e61b060fac814ca2a09e1e5ebfbd1e97 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-12T19:54:57Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e61b060fac814ca2a09e1e5ebfbd1e972022-12-22T03:18:41ZengMDPI AGEnergies1996-10732018-03-0111471110.3390/en11040711en11040711State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning MachineCheng Siong Chin0Zuchang Gao1Faculty of Science, Agriculture and Engineering, Newcastle University Singapore, Singapore 599493, SingaporeSchool of Engineering, Temasek Polytechnic, 21 Tampines Avenue 1, Singapore 529757, SingaporeAn adaptive online sequential extreme learning machine (AOS-ELM) is proposed to predict the state-of-charge of the battery cells at different ambient temperatures. With limited samples and sequential data for training during the initial design stage, conventional neural network training gives higher errors and longer computing times when it maps the available inputs to SOC. The use of AOS-ELM allows a gradual increase in the dataset that can be time-consuming to obtain during the initial stage of the neural network training. The SOC prediction using AOS-ELM gives a smaller root mean squared error in testing (and small standard deviation in the trained results) and reasonable training time as compared to other types of ELM-based learnings and gradient-based machine learning. In addition, the subsequent identification of the cells’ static capacity and battery parameters from actual experiments is not required to estimate the SOC of each cell and the battery stack.http://www.mdpi.com/1996-1073/11/4/711state-of-chargebattery cellextreme learning machineadaptive online sequential extreme learning machine |
| spellingShingle | Cheng Siong Chin Zuchang Gao State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine Energies state-of-charge battery cell extreme learning machine adaptive online sequential extreme learning machine |
| title | State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine |
| title_full | State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine |
| title_fullStr | State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine |
| title_full_unstemmed | State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine |
| title_short | State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine |
| title_sort | state of charge estimation of battery pack under varying ambient temperature using an adaptive sequential extreme learning machine |
| topic | state-of-charge battery cell extreme learning machine adaptive online sequential extreme learning machine |
| url | http://www.mdpi.com/1996-1073/11/4/711 |
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