State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm
The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRL...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/10/11/388 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846154303528501248 |
|---|---|
| author | Tianqing Yuan Yang Liu Jing Bai Hao Sun |
| author_facet | Tianqing Yuan Yang Liu Jing Bai Hao Sun |
| author_sort | Tianqing Yuan |
| collection | DOAJ |
| description | The ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively. |
| format | Article |
| id | doaj-art-23eff302ea1441a984360a64ae611108 |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-23eff302ea1441a984360a64ae6111082024-11-26T17:51:04ZengMDPI AGBatteries2313-01052024-11-01101138810.3390/batteries10110388State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering AlgorithmTianqing Yuan0Yang Liu1Jing Bai2Hao Sun3Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, ChinaYuda Engineering (Jilin) Co., Ltd., Siping 136000, ChinaCollege of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaThe ability to quickly and accurately estimate the state of charge (SOC) of lithium batteries is a key function of the battery management system (BMS). To enhance the accuracy of SOC estimation for lithium batteries, we propose a method that combines the dynamic factor recursive least squares (DFFRLS) algorithm and the strong tracking H-infinity filtering (STF-HIF) algorithm. To address the issue of fixed forgetting factors in recursive least squares (RLS) that struggle to maintain both fast convergence and stability in battery parameter identification, we introduce dynamic forgetting factors. This approach adjusts the forgetting factor based on the residuals between the model’s estimated and actual values. To improve the H-infinity filtering (HIF) algorithm’s poor performance in tracking sudden state changes, we propose a combined STF-HIF algorithm, integrating HIF with strong tracking filtering (STF). Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.https://www.mdpi.com/2313-0105/10/11/388lithium batterystate of chargedynamic forgetting factorrecursive least squaresstrong tracking H-infinity filtering algorithm |
| spellingShingle | Tianqing Yuan Yang Liu Jing Bai Hao Sun State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm Batteries lithium battery state of charge dynamic forgetting factor recursive least squares strong tracking H-infinity filtering algorithm |
| title | State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm |
| title_full | State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm |
| title_fullStr | State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm |
| title_full_unstemmed | State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm |
| title_short | State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm |
| title_sort | state of charge estimation of lithium battery utilizing strong tracking h infinity filtering algorithm |
| topic | lithium battery state of charge dynamic forgetting factor recursive least squares strong tracking H-infinity filtering algorithm |
| url | https://www.mdpi.com/2313-0105/10/11/388 |
| work_keys_str_mv | AT tianqingyuan stateofchargeestimationoflithiumbatteryutilizingstrongtrackinghinfinityfilteringalgorithm AT yangliu stateofchargeestimationoflithiumbatteryutilizingstrongtrackinghinfinityfilteringalgorithm AT jingbai stateofchargeestimationoflithiumbatteryutilizingstrongtrackinghinfinityfilteringalgorithm AT haosun stateofchargeestimationoflithiumbatteryutilizingstrongtrackinghinfinityfilteringalgorithm |