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

Quan Ouyang, Rui Ma, Zhaoxiang Wu, Guotuan Xu and Zhisheng Wang
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Quan Ouyang: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Rui Ma: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Zhaoxiang Wu: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Guotuan Xu: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Zhisheng Wang: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Energies, 2020, vol. 13, issue 18, 1-14

Abstract: 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.

Keywords: lithium-ion batteries; state-of-charge estimation; adaptive square-root unscented Kalman filter; recursive least squares (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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