Chuanwei Zhang, Ting Wang, Meng Wei, Lin Qiao, Gaoqi Lian
{"title":"基于栅极递归单元和无符号卡尔曼滤波的锂离子电池充电状态估算","authors":"Chuanwei Zhang, Ting Wang, Meng Wei, Lin Qiao, Gaoqi Lian","doi":"10.1007/s11581-024-05811-y","DOIUrl":null,"url":null,"abstract":"<p>Accurate and robust state of charge (SOC) estimation for lithium-ion batteries is crucial for battery management systems. In this study, we proposed an SOC estimation approach for lithium-ion batteries that integrates the gate recurrent unit (GRU) with the unscented Kalman filtering (UKF) algorithm. This integration aims to enhance the robustness of SOC estimation under complex working conditions and varying temperatures. The GRU neural network is employed to establish an offline training model, while the fusion of the UKF online estimation is utilized to obtain smooth SOC estimation results for lithium-ion batteries. This approach realized a closed-loop SOC estimation strategy. The 18,650 and 26,650 LiFePO<sub>4</sub> batteries were selected for experiments conducted under different charging and discharging conditions at operating temperatures of 10℃, 25℃, and 40 °C. The experiment verified the high accuracy and robustness of the proposed GRU and UKF fusion approach, with both the root mean square error (RMSE) and the mean absolute error (MAE) maintained within 1%.</p>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"69 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering\",\"authors\":\"Chuanwei Zhang, Ting Wang, Meng Wei, Lin Qiao, Gaoqi Lian\",\"doi\":\"10.1007/s11581-024-05811-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Accurate and robust state of charge (SOC) estimation for lithium-ion batteries is crucial for battery management systems. In this study, we proposed an SOC estimation approach for lithium-ion batteries that integrates the gate recurrent unit (GRU) with the unscented Kalman filtering (UKF) algorithm. This integration aims to enhance the robustness of SOC estimation under complex working conditions and varying temperatures. The GRU neural network is employed to establish an offline training model, while the fusion of the UKF online estimation is utilized to obtain smooth SOC estimation results for lithium-ion batteries. This approach realized a closed-loop SOC estimation strategy. The 18,650 and 26,650 LiFePO<sub>4</sub> batteries were selected for experiments conducted under different charging and discharging conditions at operating temperatures of 10℃, 25℃, and 40 °C. The experiment verified the high accuracy and robustness of the proposed GRU and UKF fusion approach, with both the root mean square error (RMSE) and the mean absolute error (MAE) maintained within 1%.</p>\",\"PeriodicalId\":599,\"journal\":{\"name\":\"Ionics\",\"volume\":\"69 1\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ionics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1007/s11581-024-05811-y\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ionics","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s11581-024-05811-y","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering
Accurate and robust state of charge (SOC) estimation for lithium-ion batteries is crucial for battery management systems. In this study, we proposed an SOC estimation approach for lithium-ion batteries that integrates the gate recurrent unit (GRU) with the unscented Kalman filtering (UKF) algorithm. This integration aims to enhance the robustness of SOC estimation under complex working conditions and varying temperatures. The GRU neural network is employed to establish an offline training model, while the fusion of the UKF online estimation is utilized to obtain smooth SOC estimation results for lithium-ion batteries. This approach realized a closed-loop SOC estimation strategy. The 18,650 and 26,650 LiFePO4 batteries were selected for experiments conducted under different charging and discharging conditions at operating temperatures of 10℃, 25℃, and 40 °C. The experiment verified the high accuracy and robustness of the proposed GRU and UKF fusion approach, with both the root mean square error (RMSE) and the mean absolute error (MAE) maintained within 1%.
期刊介绍:
Ionics is publishing original results in the fields of science and technology of ionic motion. This includes theoretical, experimental and practical work on electrolytes, electrode, ionic/electronic interfaces, ionic transport aspects of corrosion, galvanic cells, e.g. for thermodynamic and kinetic studies, batteries, fuel cells, sensors and electrochromics. Fast solid ionic conductors are presently providing new opportunities in view of several advantages, in addition to conventional liquid electrolytes.