期刊
ENERGIES
卷 13, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/en13184968
关键词
lithium-ion batteries; state-of-charge estimation; adaptive square-root unscented Kalman filter; recursive least squares
资金
- National Natural Science Foundation of China [61903189]
- Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT20053]
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.
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