State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter
Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great signifi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.938467/full |
| _version_ | 1828466913297039360 |
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| author | Zhaowei Zhang Zhaowei Zhang Xinghao Zhang Xinghao Zhang Zhiwei He Zhiwei He Chunxiang Zhu Chunxiang Zhu Wenlong Song Mingyu Gao Mingyu Gao Yining Song |
| author_facet | Zhaowei Zhang Zhaowei Zhang Xinghao Zhang Xinghao Zhang Zhiwei He Zhiwei He Chunxiang Zhu Chunxiang Zhu Wenlong Song Mingyu Gao Mingyu Gao Yining Song |
| author_sort | Zhaowei Zhang |
| collection | DOAJ |
| description | Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed. |
| first_indexed | 2024-12-11T03:57:45Z |
| format | Article |
| id | doaj.art-aec2071690c14b9eb7e3102dfc331955 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-11T03:57:45Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-aec2071690c14b9eb7e3102dfc3319552022-12-22T01:21:44ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-07-011010.3389/fenrg.2022.938467938467State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman FilterZhaowei Zhang0Zhaowei Zhang1Xinghao Zhang2Xinghao Zhang3Zhiwei He4Zhiwei He5Chunxiang Zhu6Chunxiang Zhu7Wenlong Song8Mingyu Gao9Mingyu Gao10Yining Song11College of Electronic Information, Hangzhou Dianzi University, Hangzhou, ChinaZhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, ChinaCollege of Electronic Information, Hangzhou Dianzi University, Hangzhou, ChinaZhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, ChinaCollege of Electronic Information, Hangzhou Dianzi University, Hangzhou, ChinaZhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, ChinaCollege of Electronic Information, Hangzhou Dianzi University, Hangzhou, ChinaZhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, ChinaTianneng Battery Group Co., Ltd., Changxing, ChinaCollege of Electronic Information, Hangzhou Dianzi University, Hangzhou, ChinaZhejiang Provincial Key Lab of Equipment Electronics, Hangzhou, ChinaZhejiang Leapmotor Technology Co., Ltd., Hangzhou, ChinaAccurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed.https://www.frontiersin.org/articles/10.3389/fenrg.2022.938467/fullstate of chargelithium-ion batteriessimple recurrent unitsunscented Kalman filterlong short-term memory |
| spellingShingle | Zhaowei Zhang Zhaowei Zhang Xinghao Zhang Xinghao Zhang Zhiwei He Zhiwei He Chunxiang Zhu Chunxiang Zhu Wenlong Song Mingyu Gao Mingyu Gao Yining Song State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter Frontiers in Energy Research state of charge lithium-ion batteries simple recurrent units unscented Kalman filter long short-term memory |
| title | State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter |
| title_full | State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter |
| title_fullStr | State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter |
| title_full_unstemmed | State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter |
| title_short | State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter |
| title_sort | state of charge estimation for lithium ion batteries using simple recurrent units and unscented kalman filter |
| topic | state of charge lithium-ion batteries simple recurrent units unscented Kalman filter long short-term memory |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.938467/full |
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