SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect he...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/2/375 |
| _version_ | 1798038225476911104 |
|---|---|
| author | Jianfang Jia Jianyu Liang Yuanhao Shi Jie Wen Xiaoqiong Pang Jianchao Zeng |
| author_facet | Jianfang Jia Jianyu Liang Yuanhao Shi Jie Wen Xiaoqiong Pang Jianchao Zeng |
| author_sort | Jianfang Jia |
| collection | DOAJ |
| description | The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy. |
| first_indexed | 2024-04-11T21:37:19Z |
| format | Article |
| id | doaj.art-206c88ede18f4c7c8a22f674a26f1446 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:37:19Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-206c88ede18f4c7c8a22f674a26f14462022-12-22T04:01:43ZengMDPI AGEnergies1996-10732020-01-0113237510.3390/en13020375en13020375SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health IndicatorsJianfang Jia0Jianyu Liang1Yuanhao Shi2Jie Wen3Xiaoqiong Pang4Jianchao Zeng5School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, ChinaSchool of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, ChinaSchool of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, ChinaThe state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.https://www.mdpi.com/1996-1073/13/2/375lithium-ion batteriesstate of healthremaining useful lifeindirect health indicatorgrey relation analysisgaussian process regression |
| spellingShingle | Jianfang Jia Jianyu Liang Yuanhao Shi Jie Wen Xiaoqiong Pang Jianchao Zeng SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators Energies lithium-ion batteries state of health remaining useful life indirect health indicator grey relation analysis gaussian process regression |
| title | SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators |
| title_full | SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators |
| title_fullStr | SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators |
| title_full_unstemmed | SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators |
| title_short | SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators |
| title_sort | soh and rul prediction of lithium ion batteries based on gaussian process regression with indirect health indicators |
| topic | lithium-ion batteries state of health remaining useful life indirect health indicator grey relation analysis gaussian process regression |
| url | https://www.mdpi.com/1996-1073/13/2/375 |
| work_keys_str_mv | AT jianfangjia sohandrulpredictionoflithiumionbatteriesbasedongaussianprocessregressionwithindirecthealthindicators AT jianyuliang sohandrulpredictionoflithiumionbatteriesbasedongaussianprocessregressionwithindirecthealthindicators AT yuanhaoshi sohandrulpredictionoflithiumionbatteriesbasedongaussianprocessregressionwithindirecthealthindicators AT jiewen sohandrulpredictionoflithiumionbatteriesbasedongaussianprocessregressionwithindirecthealthindicators AT xiaoqiongpang sohandrulpredictionoflithiumionbatteriesbasedongaussianprocessregressionwithindirecthealthindicators AT jianchaozeng sohandrulpredictionoflithiumionbatteriesbasedongaussianprocessregressionwithindirecthealthindicators |