Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification

The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model paramet...

Full description

Saved in:
Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 18; p. 4968
Main Authors Ouyang, Quan, Ma, Rui, Wu, Zhaoxiang, Xu, Guotuan, Wang, Zhisheng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2020
Subjects
Accuracy
adaptive square-root unscented Kalman filter
Algorithms
Batteries
Experiments
Kalman filters
Lithium
Lithium-ion batteries
Neural networks
Noise
Parameter estimation
Parameter identification
recursive least squares
State of charge
state-of-charge estimation
Online AccessGet full text

Cover

More Information
Summary:The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13184968