Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares
An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC,...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | ZH |
| Publicado: |
Editorial Office of Journal of Shanghai Jiao Tong University
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/83f105db78eb4e438ce5a3d626cb156e |
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| Sumario: | An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC, a first-order resistor-capacitance (RC) equivalent circuit model is used to describe the external characteristic of batteries. The model parameters are identified by forgetting factor recursive least-squares (FFRLS). Open circuit voltage (OCV) is one of the model parameters, and then SOC can be estimated by the SOC-OCV model. The CALCE battery research group in the University of Maryland has proposed some data, which include the data of LNMC/graphite battery working under dynamic stress test (DST) and Beijing dynamic stress test (BJDST) conditions. These data are used to verify the proposed algorithm. The results show that the estimation error does not exceed 3.419 0% in DST and 4.233 5% in BJDST, which indicates that the proposed method can realize online SOC estimation. |
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