A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries

Xintian Liu, Xuhui Deng, Yao He, Xinxin Zheng and Guojian Zeng
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Xintian Liu: Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China
Xuhui Deng: Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China
Yao He: Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China
Xinxin Zheng: Automobile Engineering Technology Research Institution, Hefei University of Technology, Hefei 230009, China
Guojian Zeng: Anhui Ruineng Technology Company, Hefei 230011, China

Energies, 2019, vol. 13, issue 1, 1-16

Abstract: With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.

Keywords: lithium-ion battery; dynamic thevnin model; state of charge; unscented Kalman particle 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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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