Improved State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Parameter Estimation and Multi-Innovation Adaptive Robust Unscented Kalman Filter

In this study, an improved adaptive robust unscented Kalman Filter (ARUKF) is proposed for an accurate state-of-charge (SOC) estimation of battery management system (BMS) in electric vehicles (EV). The extended Kalman Filter (EKF) algorithm is first used to achieve online identification of the model...

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Bibliographic Details
Published in:Energies (Basel) 2024-01, Vol.17 (1), p.272
Main Authors: Li, Cheng, Kim, Gi-Woo
Format: Article
Language:English
Subjects:
Accuracy
adaptive extended Kalman filter
adaptive robust unscented Kalman filter
Algorithms
Automobiles, Electric
Datasets
Efficiency
Embedded systems
Innovations
Lithium
Methods
multi-innovation
multiscale time framework
Neural networks
online parameter identification
Parameter estimation
Parameter identification
state of charge
Support vector machines
Temperature
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Summary:In this study, an improved adaptive robust unscented Kalman Filter (ARUKF) is proposed for an accurate state-of-charge (SOC) estimation of battery management system (BMS) in electric vehicles (EV). The extended Kalman Filter (EKF) algorithm is first used to achieve online identification of the model parameters. Subsequently, the identified parameters obtained from the EKF are processed to obtain the SOC of the batteries using a multi-innovation adaptive robust unscented Kalman filter (MIARUKF), developed by the ARUKF based on the principle of multi-innovation. Co-estimation of parameters and SOC is ultimately achieved. The co-estimation algorithm EKF-MIARUKF uses a multi-timescale framework with model parameters estimated on a slow timescale and the SOC estimated on a fast timescale. The EKF-MIARUKF integrates the advantages of multiple Kalman filters and eliminates the disadvantages of a single Kalman filter. The proposed algorithm outperforms other algorithms in terms of accuracy because the average root mean square error (RMSE) and the mean absolute error (MAE) of the SOC estimation were the smallest under three dynamic conditions.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17010272