中圖分類(lèi)號(hào)： TN06；TM912 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200607 中文引用格式： 崔耕韜，江衛(wèi)華，涂煒. 基于EKF算法的鋰電池SOC估算策略[J].電子技術(shù)應(yīng)用，2021，47(3)：36-39. 英文引用格式： Cui Gengtao，Jiang Weihua，Tu Wei. SOC estimation of lithium battery based on extended Kalman filter algorithm[J]. Application of Electronic Technique，2021，47(3)：36-39.
SOC estimation of lithium battery based on extended Kalman filter algorithm
Cui Gengtao，Jiang Weihua，Tu Wei
School of Electrical Information,Wuhan Institute of Technology，Wuhan 443000，China
Abstract： In the battery management system, the accurate estimation of State of Charge(SOC) has an important position, its impor-
tance is not only to the user prompt battery remaining power, more is that it is the basis of the battery charge and discharge management and balanced control management.And SOC is affected by many factors, such as temperature and current size,direction,etc,so it is difficult to predict it accurately.In this paper, an extended Kalman filter(EKF) algorithm is proposed to estimate the SOC of lithium battery.The battery model was established, and the parameter identification was carried out through the Hybrid Pulse Power Characteristic(HPPC) test.The SOC estimation error of the model is about 2.1% under constant discharge,it shows that the model is effective and easy to apply.
Key words : the lithium battery；EKF algorithm；HPPC test；SOC estimation
當(dāng)前，有多種經(jīng)典的電池模型：理想等效模型中各參數(shù)均為不變量，因此精度較低；Thevenin模型增加了電池極化的影響，但不能反映各參數(shù)與荷電狀態(tài)(State of Charge，SOC)之間的關(guān)系[1-3]；PNGV模型對(duì)Thevenin模型進(jìn)行了改進(jìn)，但精度仍較低；RC模型能反映電池內(nèi)阻、電流對(duì)SOC的影響，具有較好的動(dòng)靜態(tài)特性。
