Title :
Study of battery state-of-charge estimation for hybrid electric vehicles
Author :
Zhou Yongqin ; Zhang Yanming ; Zhao Pengshu ; Han Chunli
Author_Institution :
Coll. of Electr. & Electr. Eng., Harbin Univ. of Sci. & Technol., Harbin, China
Abstract :
Aiming at the identification problem of battery state of charge (SOC) in hybrid electric vehicle, the neural network method is applied on the battery SOC estimation to build the power battery model based on radial basis function (RBF) after considering the nonlinear relationship between the battery state of charge and the observable external characteristics. Two simulation experiments were done under the situation of constant current charging and discharging mode and dynamic vehicle driving mode. The results show that the proposed SOC estimation method is feasible and of high estimation accuracy.
Keywords :
battery powered vehicles; hybrid electric vehicles; power engineering computing; radial basis function networks; RBF; battery SOC estimation; battery state-of-charge estimation; constant current charging; discharging mode; dynamic vehicle driving mode; high estimation accuracy; hybrid electric vehicles; neural network method; nonlinear relationship; power battery model; radial basis function; Chemicals; Estimation; Load modeling; Mathematical model; Nickel; System-on-a-chip; Vehicles; ADVISOR; battery SOC; neural network; radial basis function;
Conference_Titel :
Strategic Technology (IFOST), 2011 6th International Forum on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-4577-0398-0
DOI :
10.1109/IFOST.2011.6021024