DocumentCode :
3029470
Title :
Design and Study on the State of Charge Estimation for Lithium-ion Battery Pack in Electric Vehicle
Author :
Xu, Jie ; Gao, Mingyu ; He, Zhiwei ; Yao, Jianbin ; Xu, Hongfeng
Author_Institution :
Electron. Circuit & Syst. Dept., Hangzhou Dianzi Univ. Hangzhou, Hangzhou, China
Volume :
3
fYear :
2009
fDate :
7-8 Nov. 2009
Firstpage :
316
Lastpage :
320
Abstract :
State of charge (SOC) estimation is an increasingly important issue in battery management system (BMS) and has become a core factor to promote the development of electric vehicle (EV). In addition to offering the real time display of battery parameters to user, the accurate SOC information would exert some controls over the charging and discharging process that in turn reduces the risk of cell over voltage. Considering the shortcoming of traditional estimation methods and the harsh requirements in EV environment, a new method named combination algorithm is proposed in this paper in accordance with the characteristics of lithium-ion power battery. Some capacity effect factors, such as current rate and cell temperature, are also taken into consideration in the algorithm. The dynamic discharge test shows that the maximal SOC estimation error is less than 5%, which validates the feasibility and availability of the combination algorithm in the SOC estimation of electric vehicle.
Keywords :
battery management systems; dynamic testing; electric vehicles; power system parameter estimation; secondary cells; battery management system; cell over voltage risk reduction; combination algorithm; dynamic discharge test; electric vehicle; lithium-ion power battery pack; maximal SOC estimation error; real time display; state of charge estimation; Artificial intelligence; Automobiles; Battery management systems; Computational intelligence; Electric vehicles; Helium; State estimation; Temperature; Vehicle dynamics; Voltage control; ampere hour (Ah); battery management system (BMS); combination algorithm; electric vehicle (EV); extended Kalman filtering (EKF); open circuit voltage (OCV); state of charge (SOC);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
Type :
conf
DOI :
10.1109/AICI.2009.289
Filename :
5376674
Link To Document :
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