Xuhui Deng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xuhui Deng has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Guojian Zeng
Anhui Ruineng Technology Company, Hefei 230011, China
1 shared publication - Xintian Liu
College of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230002, China
1 shared publication - Xinxin Zheng
College of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
1 shared publication - Yao He
Intelligent: Manufacturing Institute, Hefei University of Technology, Hefei, China
1 shared publication
Latest Publications
- Open Access
A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries
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.
Open AccessA Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries
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.