Title :
Battery state of charge online estimation based on particle filter
Author :
Gao, Mingyu ; Liu, Yuanyuan ; He, Zhiwei
Abstract :
Battery state of charge estimation is one of the key techniques to battery manage system. An accurate estimation of the state of charge can help to improve the performance of the battery and increase the security of the electric vehicle. A particle filter based battery state of charge estimation method is proposed in this paper. The battery is looked on as a nonlinear dynamic system and the state of charge of the battery is used as the only state variable in it. Two models are used to describe the dynamics of the system, one is the state transmission model and the other is the measurement model which describes the relationship between the state of charge and the terminal voltage, the discharge current, etc. Experiment results show that the proposed method is effective and efficient.
Keywords :
battery charge measurement; battery management systems; battery powered vehicles; estimation theory; particle filtering (numerical methods); battery manage system; battery state; charge estimation method; charge online estimation; electric vehicle security; nonlinear dynamic system; particle filter; state transmission model; Batteries; Battery charge measurement; Estimation; Mathematical model; Particle filters; System-on-a-chip; Voltage measurement; battery management system; particle filter; state of charge;
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
DOI :
10.1109/CISP.2011.6100603
