Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation

King Hang Wu (), Mehdi Seyedmahmoudian, Saad Mekhilef, Prashant Shrivastava and Alex Stojcevski
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King Hang Wu: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia
Mehdi Seyedmahmoudian: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia
Saad Mekhilef: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia
Prashant Shrivastava: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Alex Stojcevski: PVC and President, Curtin University, Singapore 117684, Singapore

Energies, 2024, vol. 17, issue 19, 1-26

Abstract: Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

Keywords: lithium-ion battery; state of charge; temperature; battery model; data sampling time; extended Kalman filter; closed-loop control system; dual closed-loop control system; forgetting factor-based least square; internal ohmic resistance; automatic data sampling time correction (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: 2024
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