State of charge estimation of Power lithium-ion battery based on an Affine Iterative Adaptive Extended Kalman Filter

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Impact Factor:9.4

DOI number:10.1016/j.est.2022.104472

Journal:Journal of Energy Storage

Key Words:State of charge (SOC), Power lithium-ion battery, Online identification, Adaptive forgetting factor recursive augmented least squares (AFFRALS), Affine Iterative Adaptive Extended Kalman Filter (AIAEKF), Unknown SOC initial value

Abstract:State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice.

Note:中科院2区Top

Co-author:Linlin Qin,Gang Wu,Yusha Huang,Chun Shi

First Author:Muyao Wu

Indexed by:Journal paper

Document Code:104472

Discipline:Engineering

Document Type:J

Volume:51

ISSN No.:2352-152X

Translation or Not:no

Date of Publication:2022-04-04

Included Journals:SCI、EI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S2352152X22004947