Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range
The state of charge (<i>SOC</i>) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of <i>SOC</i> estimation in lithium-ion batteries, we prop...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/7/1866 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850188062578966528 |
|---|---|
| author | Da Li Lu Liu Chuanxu Yue Xiaojin Gao Yunhai Zhu |
| author_facet | Da Li Lu Liu Chuanxu Yue Xiaojin Gao Yunhai Zhu |
| author_sort | Da Li |
| collection | DOAJ |
| description | The state of charge (<i>SOC</i>) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of <i>SOC</i> estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and <i>SOC</i> assessment using cat swarm optimization dual Kalman filtering (CSO–DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter filter for dynamic parameter identification, while the extended Kalman filtering (EKF) algorithm acts as a state filter for real-time <i>SOC</i> estimation. These two filters operate alternately throughout the iterative process. Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and <i>SOC</i> estimation. To support this algorithm, we establish an environmental temperature battery database and incorporate temperature variables to achieve accurate <i>SOC</i> estimation under variable-temperature conditions. The results indicate that creating a database that accommodates temperature variations and optimizing dual Kalman filtering through the cat swarm optimization algorithm significantly improves <i>SOC</i> estimation accuracy. |
| format | Article |
| id | doaj-art-70a859ece74f4a35a2fda2f271a2a4ec |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-70a859ece74f4a35a2fda2f271a2a4ec2025-08-20T02:15:58ZengMDPI AGEnergies1996-10732025-04-01187186610.3390/en18071866Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature RangeDa Li0Lu Liu1Chuanxu Yue2Xiaojin Gao3Yunhai Zhu4Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, ChinaInstitute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, ChinaInstitute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, ChinaScience and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, ChinaScience and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, ChinaThe state of charge (<i>SOC</i>) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of <i>SOC</i> estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and <i>SOC</i> assessment using cat swarm optimization dual Kalman filtering (CSO–DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter filter for dynamic parameter identification, while the extended Kalman filtering (EKF) algorithm acts as a state filter for real-time <i>SOC</i> estimation. These two filters operate alternately throughout the iterative process. Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and <i>SOC</i> estimation. To support this algorithm, we establish an environmental temperature battery database and incorporate temperature variables to achieve accurate <i>SOC</i> estimation under variable-temperature conditions. The results indicate that creating a database that accommodates temperature variations and optimizing dual Kalman filtering through the cat swarm optimization algorithm significantly improves <i>SOC</i> estimation accuracy.https://www.mdpi.com/1996-1073/18/7/1866variable temperatureenvironmental temperature battery databasecat swarm optimization algorithmdual Kalman filtering<i>SOC</i> estimation |
| spellingShingle | Da Li Lu Liu Chuanxu Yue Xiaojin Gao Yunhai Zhu Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range Energies variable temperature environmental temperature battery database cat swarm optimization algorithm dual Kalman filtering <i>SOC</i> estimation |
| title | Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range |
| title_full | Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range |
| title_fullStr | Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range |
| title_full_unstemmed | Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range |
| title_short | Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range |
| title_sort | real time estimation of the state of charge of lithium batteries under a wide temperature range |
| topic | variable temperature environmental temperature battery database cat swarm optimization algorithm dual Kalman filtering <i>SOC</i> estimation |
| url | https://www.mdpi.com/1996-1073/18/7/1866 |
| work_keys_str_mv | AT dali realtimeestimationofthestateofchargeoflithiumbatteriesunderawidetemperaturerange AT luliu realtimeestimationofthestateofchargeoflithiumbatteriesunderawidetemperaturerange AT chuanxuyue realtimeestimationofthestateofchargeoflithiumbatteriesunderawidetemperaturerange AT xiaojingao realtimeestimationofthestateofchargeoflithiumbatteriesunderawidetemperaturerange AT yunhaizhu realtimeestimationofthestateofchargeoflithiumbatteriesunderawidetemperaturerange |