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Estimation of battery SOC based on improved EKF algorithm
Shi G(石刚); Zhao W(赵伟); Han ZH(韩忠华); Liu SS(刘珊珊)
Department工业控制网络与系统研究室
Conference Name2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2016
Conference DateMay 20-22, 2016
Conference PlaceChongqing, China
Author of SourceChongqing Global Union Academy of Science and Technology; Global Union Academy of Science and Technology; IEEE Beijing Section
Source PublicationProceedings of 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2016
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2016
Pages151-154
Indexed ByEI ; CPCI(ISTP)
EI Accession number20164202910704
WOS IDWOS:000389505000032
Contribution Rank1
ISBN978-1-4673-9192-4
KeywordEkf Neural Network Battery Soc Thevenin Circuit Li-ion Battery
AbstractThis paper studies the estimation of the state of lithium battery (SOC), and develops an improved extended Kalman filter algorithm for this problem. To compensate deficiencies of the simple polynomial fitting, the neural network algorithm firstly is adopted to simulate the relation curve between the SOC and the parameters of circuit model, which is constructed based on Thevenin circuit. And then the state space equation among the battery's SOC and the voltage of the ends of the RC loop is established, also does the measurement equation which is based on the battery output voltage. In addition, extended Kalman is applied to estimate battery SOC. In the last, the effectiveness of the proposed method is verified using an experimental testing, and the results show that our method can estimate the SOC more accurately comparing with the standard extended Kalman algorithm.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19283
Collection工业控制网络与系统研究室
Corresponding AuthorShi G(石刚)
AffiliationLaboratory of Industrial Control Network and System, Shenyang Institute of Automation, Shenyang, China
Recommended Citation
GB/T 7714
Shi G,Zhao W,Han ZH,et al. Estimation of battery SOC based on improved EKF algorithm[C]//Chongqing Global Union Academy of Science and Technology; Global Union Academy of Science and Technology; IEEE Beijing Section. Piscataway, NJ, USA:IEEE,2016:151-154.
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