State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models
The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1978930 |
| Summary: | The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the Particle Swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge–discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters. |
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| ISSN: | 0954-0091 1360-0494 |