Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing

Xin Lai, Ming Yuan, Xiaopeng Tang (), Yi Yao, Jiahui Weng, Furong Gao, Weiguo Ma and Yuejiu Zheng
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Xin Lai: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Ming Yuan: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Xiaopeng Tang: Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Yi Yao: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Jiahui Weng: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Furong Gao: Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Weiguo Ma: School of Electrical Engineering, Nantong University, Nantong 226019, China
Yuejiu Zheng: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Energies, 2022, vol. 15, issue 19, 1-20

Abstract: State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time under different battery ageing degrees. Then, the TLS algorithm is used to estimate the battery SOH to improve the prior estimation accuracy of SOC. Next, the SOC is calculated by the UKF algorithm, and finally, a more accurate SOH can be obtained according to the UKF-based SOC trajectory. The battery-in-the-loop experiments are utilized to verify the proposed algorithm. For the cases of temperature change up to 35 °C and capacity decay up to 10%, our joint estimator can achieve ultra-low errors, bounded by 2%, respectively, for SOH and SOC. The proposed method paves the way for the advancement of battery use in applications, such as electric vehicles and microgrid applications.

Keywords: lithium-ion batteries; joint SOC-SOH estimation; forgetting factor recursive least squares; total least squares; unscented Kalman filter (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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