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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/7/1866 |
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| Summary: | 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. |
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| ISSN: | 1996-1073 |