Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use
Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system’s proper operation. A direct way to estimate the SOH is through the measurement of the battery’s capacity; however, this measurem...
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MDPI AG
2017-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/12/2012 |
| _version_ | 1817992748211372032 |
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| author | Jinsong Yu Baohua Mo Diyin Tang Jie Yang Jiuqing Wan Jingjing Liu |
| author_facet | Jinsong Yu Baohua Mo Diyin Tang Jie Yang Jiuqing Wan Jingjing Liu |
| author_sort | Jinsong Yu |
| collection | DOAJ |
| description | Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system’s proper operation. A direct way to estimate the SOH is through the measurement of the battery’s capacity; however, this measurement during the battery’s operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery’s operation. These indicators are extracted from the battery’s voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery’s capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice. |
| first_indexed | 2024-04-14T01:30:23Z |
| format | Article |
| id | doaj.art-c54eb199448c441ba8613fd4997eaa65 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:30:23Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj.art-c54eb199448c441ba8613fd4997eaa652022-12-22T02:20:13ZengMDPI AGEnergies1996-10732017-12-011012201210.3390/en10122012en10122012Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized UseJinsong Yu0Baohua Mo1Diyin Tang2Jie Yang3Jiuqing Wan4Jingjing Liu5School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaNational Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing 100854, ChinaLithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system’s proper operation. A direct way to estimate the SOH is through the measurement of the battery’s capacity; however, this measurement during the battery’s operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery’s operation. These indicators are extracted from the battery’s voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery’s capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.https://www.mdpi.com/1996-1073/10/12/2012lithium-ion batteryindirect state-of-health (SOH) estimationrandomized loading conditionelastic netparticle filter |
| spellingShingle | Jinsong Yu Baohua Mo Diyin Tang Jie Yang Jiuqing Wan Jingjing Liu Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use Energies lithium-ion battery indirect state-of-health (SOH) estimation randomized loading condition elastic net particle filter |
| title | Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use |
| title_full | Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use |
| title_fullStr | Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use |
| title_full_unstemmed | Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use |
| title_short | Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use |
| title_sort | indirect state of health estimation for lithium ion batteries under randomized use |
| topic | lithium-ion battery indirect state-of-health (SOH) estimation randomized loading condition elastic net particle filter |
| url | https://www.mdpi.com/1996-1073/10/12/2012 |
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