DocumentCode :
1592159
Title :
Pruning LS-SVM Based Battery Model for Electric Vehicles
Author :
Lei, Xiao ; Chan, C.C. ; Liu, Kaipei ; Ma, Li
Author_Institution :
Wuhan Univ., Wuhan
Volume :
3
fYear :
2007
Firstpage :
333
Lastpage :
337
Abstract :
This paper presents a new method to estimate the battery state of charge (SOC) in electric vehicles (EVs). The key of the proposed method is to establish the relationship of the SOC to the battery current, voltage and temperature by using least squares support vector machine (LS-SVM). For ease of practical application, the pruning procedure is developed to reduce the number of support vectors in terms of their significance. The results show that the proposed method can simulate the battery dynamics for the accurate estimation of the SOC in EVs.
Keywords :
battery powered vehicles; least squares approximations; power engineering computing; support vector machines; battery state of charge; electric vehicles; least squares support vector machine; pruning procedure; Batteries; Electric vehicles; Energy storage; Lagrangian functions; Least squares methods; Linear systems; Neural networks; State estimation; Support vector machines; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
Type :
conf
DOI :
10.1109/ICNC.2007.584
Filename :
4344532
Link To Document :
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