Study of impacts of parameters identification methods on model-based state estimation for LiFePO4 battery

Model-based methods are widely used for online states estimation of electric vehicles (EVs) due to its accuracy and robustness. Current research mainly focuses on improving estimation filters. However, there is less discussion on the parameters identification methods. In this work, the parameters id...

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Bibliographic Details
Published in:Ionics 2022-07, Vol.28 (7), p.3321-3339
Main Authors: Fu, Shiyi, Lv, Taolin, Liu, Wen, Wu, Lei, Luo, Chengdong, Xie, Jingying
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Chemistry
Chemistry and Materials Science
Condensed Matter Physics
Electric filters
Electric vehicles
Electrochemistry
Energy Storage
Identification
Identification methods
Kalman filters
Optical and Electronic Materials
Original Paper
Parameter identification
Regression models
Renewable and Green Energy
Root-mean-square errors
State estimation
State of charge
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Summary:Model-based methods are widely used for online states estimation of electric vehicles (EVs) due to its accuracy and robustness. Current research mainly focuses on improving estimation filters. However, there is less discussion on the parameters identification methods. In this work, the parameters identification method is divided into two parts: formulation of the regression model and application of identification algorithms. First, two methods of formulation of the regression model are studied, respectively. Then, performances on parameters identification of forgetting factor recursive least squares (FFRLS), optimal bounding ellipsoid (OBE), and linear Kalman filter (LKF) are discussed. Besides, cubature Kalman filter (CKF) is selected for state of charge (SOC) estimation. In order to obtain the experimental data and verify the parameters identification and states estimation accuracy, the Hybrid Pulse Power Characteristic (HPPC) test, urban dynamometer driving schedule (UDDS) test, and new European driving cycle (NEDC) test are carried out. In addition, the maximum absolute error (MAE), mean absolute error (MaE), and root mean square error (RMSE) are calculated for evaluating the SOC estimation accuracy. When the parameters are identified by LKF, the best performance in MAE, MaE, and RMSE of SOC estimation is obtained. Considering the estimated peak power fluctuation and the identified parameters fluctuation, the OBE algorithm is more suitable for co-estimation of SOC and state of power (SOP).
ISSN:0947-7047
1862-0760
DOI:10.1007/s11581-022-04574-8