Abstract
In order to realize the on-line estimation of the state of charge (SOC) of lead-acid batteries, an unscented Kalman filtering (UKF) algorithm is proposed. Thevenin’s circuit is used as the equivalent circuit model, and the state space expression is established. The least squares algorithm is used to identify the model parameters. On this basis, the functional relationship between the state of charge of the battery and various parameters of the model is fitted. By analyzing the principle of unscented Kalman filtering, the equivalent circuit model verification experiment and battery SOC test experiment are designed. The experimental results show that under constant current conditions the proposed method has the advantages of online estimation, high estimation accuracy, and high environmental adaptability.
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This work is supported by the Science and Technology Program of Shenyang under Grant 18-013-0-18 and the National Nature Science Foundation of Liaoning Province under Grant 20180550922.
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Yu, Y. (2020). Estimation of Lead-Acid Battery State of Charge Based on Unscented Kalman Filtering. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_53
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DOI: https://doi.org/10.1007/978-3-030-31129-2_53
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