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- W2058795621 abstract "In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-ion battery model parameters are identified using an adaptive unscented Kalman filtering method, and based on the updated model, the battery SoC is estimated consequently. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter (UKF) context. The effectiveness of the proposed method is evaluated through experiments under different power duties in the laboratory environment. The obtained results are compared with that of the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms. The comparison shows that the proposed method provides better accuracy both in battery model parameters estimation and the battery SoC estimation." @default.
- W2058795621 created "2016-06-24" @default.
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- W2058795621 date "2015-01-01" @default.
- W2058795621 modified "2023-10-14" @default.
- W2058795621 title "An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots" @default.
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- W2058795621 doi "https://doi.org/10.1109/tcst.2014.2317781" @default.
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