Nonparametric Identification of Linear Dynamic Output-errors Systems

Sun, Qing; Zou, Si-Ting; Du, Dajun; Fei, Minrui

doi:10.1007/s12555-020-0401-1

Cited by 1 publication

(2 citation statements)

References 24 publications

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“…[18,19] However, the model-based Kalman filtering series approach can estimate SOC accurately even if the initial SOC value is inaccurate, thus the approach is employed widely in battery SOC estimation. [20,21] Sun et al [22] propose a joint approach based on the Thevenin model for estimating SOC, which combines the forgetting factor recursive least squares (FFRLS) method with the EKF algorithm. Similarly, a Thevenin model of Li-ion batteries considering the ambient temperature in reference [23] is proposed to increase the accuracy of SOC estimated by the EKF method.…”

Section: Doi: 101002/ente202201364mentioning

confidence: 99%

“…Similarly, a Thevenin model of Li-ion batteries considering the ambient temperature in reference [23] is proposed to increase the accuracy of SOC estimated by the EKF method. To precisely estimate SOC for lithium iron phosphate batteries, the method combining the PNGV model with the EKF algorithm for estimating SOC is proposed by Li et al [24] Therefore, the precision of the SOC estimation adopting the Kalman filtering series approach in references [22][23][24] is significantly affected by the accuracy of the battery model. Moreover, the process noise and measurement noise are supposed to follow the Gaussian distribution in estimating SOC, and the assumption is not in accordance with the actual conditions.…”

Section: Doi: 101002/ente202201364mentioning

confidence: 99%

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State‐of‐Charge Estimation of Lithium‐Ion Battery Based on an Improved Dual‐Polarization Model

et al. 2023

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This article proposes a state‐of‐charge (SOC) estimation method to eliminate the influence of the hysteresis effect and the ambient temperature. First, an improved dual‐polarization (DP) model considering the hysteresis effect and the ambient temperature is established. A hysteresis voltage source is connected in series with a couple of resistance–capacitance pairs in the improved DP model, all the parameters of which are related to the ambient temperature to depict the temperature characteristics of the battery. Second, the forgetting factor recursive least squares method is utilized to identify the parameters under the battery dynamic test data at different temperatures. The proposed model and parameterization scheme integrate the effects of hysteresis and temperature, greatly enhancing the performance of the proposed method at different temperatures. Finally, an extended Kalman filter algorithm for SOC estimation is adopted to verify the improved DP model and the simulation indicates that the error of SOC estimation is within 1.5% at different ambient temperatures. The proposed method can improve the precision of the SOC estimation even if the temperature is below −10 or above 50 °C.

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