Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit
Abstract The health state of lithium‐ion batteries is influenced by the operating conditions of energy storage stations and battery characteristics. It is challenging to obtain real‐time characterisation parameters like maximum discharge capacity and internal resistance. It is necessary to extract s...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Energy Systems Integration |
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| Online Access: | https://doi.org/10.1049/esi2.12159 |
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| _version_ | 1832582945728102400 |
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| author | Ziwei Dai Aikui Li Wei Sun Shenwu Zhang Hao Zhou Ren Rao Quan Luo |
| author_facet | Ziwei Dai Aikui Li Wei Sun Shenwu Zhang Hao Zhou Ren Rao Quan Luo |
| author_sort | Ziwei Dai |
| collection | DOAJ |
| description | Abstract The health state of lithium‐ion batteries is influenced by the operating conditions of energy storage stations and battery characteristics. It is challenging to obtain real‐time characterisation parameters like maximum discharge capacity and internal resistance. It is necessary to extract sensitivity indicators from electrical parameters such as voltage, current, and temperature. Utilising the Stanford‐MIT Research Institute battery dataset, this paper selects batteries with over 1000 cycles and five distinct charging and discharging strategies as samples. During the daily operation and maintenance of the energy storage station, health indicators are extracted from the voltage, current, and temperature curves within the state of charge range of 20%–80%. The ridge regression method is used to establish the health status estimation model. The gated recurrent unit (GRU) model is leveraged for health state prediction. Simulation results demonstrate that the proposed health indicators effectively assess lithium battery health, the health state estimation errors mean absolute error (MAE) and root mean squared error (RMSE) based on the ridge regression model are within 1.5% and 2%, and the health state prediction errors MAE and RMSE based on GRU model are within 1%. This approach exhibits stability, high accuracy, and strong generalisation capabilities. |
| format | Article |
| id | doaj-art-53464cf6bf7443b9bf0da666ca67346c |
| institution | Kabale University |
| issn | 2516-8401 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj-art-53464cf6bf7443b9bf0da666ca67346c2025-01-29T05:18:54ZengWileyIET Energy Systems Integration2516-84012024-12-016S173975310.1049/esi2.12159Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unitZiwei Dai0Aikui Li1Wei Sun2Shenwu Zhang3Hao Zhou4Ren Rao5Quan Luo6School of Electrical Engineering Dalian University of Technology Dalian Liaoning Province ChinaSchool of Electrical Engineering Dalian University of Technology Dalian Liaoning Province ChinaShuimu Shenyan Neijiang Technology Incubation Accelerator Co., Ltd Neijiang Sichuan Province ChinaShuimu Shenyan Neijiang Technology Incubation Accelerator Co., Ltd Neijiang Sichuan Province ChinaShuimu Shenyan Neijiang Technology Incubation Accelerator Co., Ltd Neijiang Sichuan Province ChinaShuimu Shenyan Neijiang Technology Incubation Accelerator Co., Ltd Neijiang Sichuan Province ChinaShuimu Shenyan Neijiang Technology Incubation Accelerator Co., Ltd Neijiang Sichuan Province ChinaAbstract The health state of lithium‐ion batteries is influenced by the operating conditions of energy storage stations and battery characteristics. It is challenging to obtain real‐time characterisation parameters like maximum discharge capacity and internal resistance. It is necessary to extract sensitivity indicators from electrical parameters such as voltage, current, and temperature. Utilising the Stanford‐MIT Research Institute battery dataset, this paper selects batteries with over 1000 cycles and five distinct charging and discharging strategies as samples. During the daily operation and maintenance of the energy storage station, health indicators are extracted from the voltage, current, and temperature curves within the state of charge range of 20%–80%. The ridge regression method is used to establish the health status estimation model. The gated recurrent unit (GRU) model is leveraged for health state prediction. Simulation results demonstrate that the proposed health indicators effectively assess lithium battery health, the health state estimation errors mean absolute error (MAE) and root mean squared error (RMSE) based on the ridge regression model are within 1.5% and 2%, and the health state prediction errors MAE and RMSE based on GRU model are within 1%. This approach exhibits stability, high accuracy, and strong generalisation capabilities.https://doi.org/10.1049/esi2.12159battery storage plantspredictive controlregression analysis |
| spellingShingle | Ziwei Dai Aikui Li Wei Sun Shenwu Zhang Hao Zhou Ren Rao Quan Luo Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit IET Energy Systems Integration battery storage plants predictive control regression analysis |
| title | Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit |
| title_full | Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit |
| title_fullStr | Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit |
| title_full_unstemmed | Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit |
| title_short | Estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit |
| title_sort | estimation and prediction method of lithium battery state of health based on ridge regression and gated recurrent unit |
| topic | battery storage plants predictive control regression analysis |
| url | https://doi.org/10.1049/esi2.12159 |
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