Open AccessState estimation based on least square support vector
Author(s) -
Jiabo Li,
Min Ye,
Kangping Gao,
Meng Wei,
Shengjie Jiao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1983/1/012069
Subject(s) - state of charge , battery (electricity) , estimation , computer science , state (computer science) , voltage , current (fluid) , lithium ion battery , control theory (sociology) , engineering , algorithm , electrical engineering , power (physics) , control (management) , artificial intelligence , physics , systems engineering , quantum mechanics
As one of the important parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) of lithium-ion battery (LIB) can ensure the safety of electric vehicles and improve the utilization rate of batteries. A new SOC estimation algorithm based LSSVM is applied. The battery parameters, including current and voltage, which are used as the inputs to estimate SOC. To promote the accuracy of SOC estimation, the SOC estimated at the previous time is taken as the feedback vector to estimate the SOC at the current time. The experimental results show that the proposed model can improve the estimation accuracy of SOC.