{"title":"用非线性卡尔曼滤波估计锂离子电池组的充电状态","authors":"Shivanshu Kumar, Saikat Mondal, Amalendu Bikash Choudhury, Himadri Sekhar Bhattacharyya, Chandan Kumar Chanda","doi":"10.1007/s11581-025-06420-z","DOIUrl":null,"url":null,"abstract":"<div><p>Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7953 - 7968"},"PeriodicalIF":2.6000,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of state of charge for a lithium-ion battery pack using nonlinear Kalman filters\",\"authors\":\"Shivanshu Kumar, Saikat Mondal, Amalendu Bikash Choudhury, Himadri Sekhar Bhattacharyya, Chandan Kumar Chanda\",\"doi\":\"10.1007/s11581-025-06420-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.</p></div>\",\"PeriodicalId\":599,\"journal\":{\"name\":\"Ionics\",\"volume\":\"31 8\",\"pages\":\"7953 - 7968\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ionics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11581-025-06420-z\",\"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://link.springer.com/article/10.1007/s11581-025-06420-z","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Estimation of state of charge for a lithium-ion battery pack using nonlinear Kalman filters
Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.
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.