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Energies 2018, 11(6), 1358; https://doi.org/10.3390/en11061358

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

1
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
2
Sunwoda Electronic Co., Ltd., Shenzhen 518108, China
*
Author to whom correspondence should be addressed.
Received: 2 May 2018 / Revised: 19 May 2018 / Accepted: 24 May 2018 / Published: 26 May 2018
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Abstract

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor. View Full-Text
Keywords: variable forgetting factor; recursive least squares; lithium-ion battery; online parameter identification; state of charge variable forgetting factor; recursive least squares; lithium-ion battery; online parameter identification; state of charge
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Lao, Z.; Xia, B.; Wang, W.; Sun, W.; Lai, Y.; Wang, M. A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares. Energies 2018, 11, 1358.

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