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- W3036680416 abstract "Accurately estimation of battery state of charge (SOC) has great significance for predicting the remaining mileage of electric vehicles. In order to improve the estimation accuracy of battery SOC and enhance the stability of traditional unscented Kalman filter algorithm, an improved unscented Kalman filter based on singular value decomposition was proposed to estimate the battery SOC. First, the LiFePO4 was used as the research object to establish the Thevenin model of the battery. By using the forgetting factor recursive least squares to identify parameters such as ohmic internal resistance, polarization resistance, and polarization capacitance in the model under actual working conditions, the identified model parameters were imported into the improved algorithm in real time to estimate the SOC of the lithium battery and compared with the extended Kalman filter. Experiments proved that the maximum error estimated by the algorithm used in this paper is not more than 2%, which can effectively improve the accuracy of SOC estimation and ensure the normal operation of the algorithm." @default.
- W3036680416 created "2020-06-25" @default.
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- W3036680416 date "2020-05-01" @default.
- W3036680416 modified "2023-10-14" @default.
- W3036680416 title "SOC Estimation of Lithium Battery Based on Improved Unscented Kalman Filter" @default.
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- W3036680416 doi "https://doi.org/10.1109/icet49382.2020.9119501" @default.
- W3036680416 hasPublicationYear "2020" @default.
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