Comparative Analysis of Battery State of Charge Estimation Methods
Lithium-ion batteries are widely used in electric vehicles, buses, etc., due to their high-power density, long lifespan, and high energy density. To efficiently manage energy in these vehicles, a Battery Management System (BMS) is crucial. A critical parameter for the BMS is the State of Charge (SoC...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00033.pdf |
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| Summary: | Lithium-ion batteries are widely used in electric vehicles, buses, etc., due to their high-power density, long lifespan, and high energy density. To efficiently manage energy in these vehicles, a Battery Management System (BMS) is crucial. A critical parameter for the BMS is the State of Charge (SoC), which indicates the available charge in the battery and ensures its operational range. This paper presents three methods for estimating SoC: the extended Kalman filter (EKF), the adaptive Luenberger observer (ALO), and a neural network model employing nonlinear auto-regressive with eXogenous inputs (NARX). These methods are evaluated under the LA92 driving cycle using metrics like Root Mean Square Error (RMSE) to assess their performance. Results show that the NARX model achieves the highest accuracy with an RMSE of 0.33%, followed by the EKF with 5.34% and finally the ALO with 5.94%. These findings indicate that all three methods are acceptable, and the proposed NARX model shows superior performance. With the NARX model exhibiting superior performance in SoC estimation for electric vehicle applications. |
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| ISSN: | 2267-1242 |