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一種基于UKF的SOC估算方法
2020年信息技術(shù)與網(wǎng)絡(luò)安全第10期
官洪運(yùn),張抒藝,井倩倩,王亞青,繆新苗
東華大學(xué) 信息科學(xué)與技術(shù)學(xué)院,上海201620
摘要: 隨著新能源汽車市場(chǎng)規(guī)模的增長(zhǎng),電池管理系統(tǒng)(Battery Management Systems,BMS)的市場(chǎng)需求也進(jìn)一步擴(kuò)大。作為保障電池安全及延長(zhǎng)電池壽命的BMS而言,動(dòng)力鋰電池組的荷電狀態(tài)(State of Charge,SOC)估算是BMS研究的重點(diǎn)。在研究了安時(shí)積分法估算SOC時(shí)受SOC初始值影響較大,且具有累積誤差的問題,以及擴(kuò)展卡爾曼濾波算法(EKF)估算SOC時(shí)收斂較慢的基礎(chǔ)上,采用二階RC等效電路模型對(duì)鋰電池進(jìn)行建模分析,針對(duì)鋰電池各參數(shù)受SOC變化的影響,引進(jìn)無跡卡爾曼濾波(UKF)算法,給出了鋰電池的SOC仿真實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,該種基于UKF的估算方法對(duì)SOC的估算更準(zhǔn)確,誤差更小且收斂速度快,對(duì)傳統(tǒng)采用定值電池參數(shù)BMS的改進(jìn)具有重要意義。
中圖分類號(hào): TM912
文獻(xiàn)標(biāo)識(shí)碼: A
DOI: 10.19358/j.issn.2096-5133.2020.10.010
引用格式: 官洪運(yùn),張抒藝,井倩倩,等. 一種基于UKF的SOC估算方法[J].信息技術(shù)與網(wǎng)絡(luò)安全,2020,39(10):49-54.
A method of SOC estimation based on UKF
Guan Hongyun,Zhang Shuyi,Jing Qianqian,Wang Yaqing,Miao Xinmiao
School of Information Science and Technology,Donghua University,Shanghai 201620,China
Abstract: With the growth of the new energy vehicle market, market demand for battery management systems(BMS) has also further expanded. For BMS, which guarantees battery safety and prolongs battery life, the estimation of the state of charge(SOC) of a powered lithium battery pack is the focus of BMS research. Based on the study of the problem that the AH integration method is greatly affected by the initial value of the SOC and has a cumulative error, and the extended Kalman filter algorithm(EKF) has a slower convergence when estimating the SOC, a second-order RC equivalent circuit model was used to model and analyze the lithium battery. Considering that the parameters of the lithium battery are affected by SOC changes, the Unscented Kalman Filter(UKF) algorithm was introduced to simulate the SOC of the lithium battery. The experimental results show that the UKF-based SOC estimation is more accurate, the error is smaller, and the convergence speed is faster, which is of great significance to the improvement of the traditional fixed-value battery parameter BMS.
Key words : lithium battery;unscented Kalman filter;state of charge;equivalent circuit;estimation method

0 引言

    廣泛使用鋰電池作為動(dòng)力電池的新能源汽車正逐漸普及,而鋰電池荷電狀態(tài)(SOC)的估計(jì)對(duì)新能源汽車的剩余可用電量具有指導(dǎo)作用,是電池管理系統(tǒng)研究的關(guān)鍵問題之一。研究估算鋰電池的SOC,首先需要進(jìn)行電池建模。目前,鋰電池模型主要有能夠較好描述電化學(xué)特性電化學(xué)模型,抽象電池電化學(xué)特性的等效電路模型及神經(jīng)網(wǎng)絡(luò)模型[1]等。DOYLE M等[2]提出經(jīng)典電化學(xué)模型——Doyle-Fuller-Newman模型用疊加法簡(jiǎn)化了數(shù)值計(jì)算;馬玉菲等[3]提出一種改進(jìn)的PNGV模型并使用該模型較準(zhǔn)確地估算了電池的SOC;PENG J C等[4]提出了一種三層神經(jīng)網(wǎng)絡(luò)模型較準(zhǔn)確地預(yù)測(cè)電池的SOC。這些電池模型雖然各有優(yōu)勢(shì),但是電化學(xué)模型計(jì)算量太大,PNGV模型由于模型較復(fù)雜,神經(jīng)網(wǎng)絡(luò)模型需要復(fù)雜的訓(xùn)練,均不太適合應(yīng)用于電池管理系統(tǒng)。所以,考慮模型復(fù)雜性及模型準(zhǔn)確性,本文采用二階RC等效電路作為鋰電池模型進(jìn)行研究。

    鋰電池SOC估計(jì)方法有安時(shí)積分法、擴(kuò)展卡爾曼濾波算法、神經(jīng)網(wǎng)絡(luò)算法等[5],安時(shí)積分法受初始值及累計(jì)誤差影響,擴(kuò)展卡爾曼濾波算法收斂速度較慢且估算不夠精確,神經(jīng)網(wǎng)絡(luò)算法訓(xùn)練數(shù)據(jù)大。綜合比較各種估算電池SOC的方法,且在證明建立二階RC等效電路模型穩(wěn)定有效的基礎(chǔ)上,本文引進(jìn)UKF濾波算法對(duì)鋰電池SOC進(jìn)行估計(jì)。




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作者信息:

官洪運(yùn),張抒藝,井倩倩,王亞青,繆新苗

(東華大學(xué) 信息科學(xué)與技術(shù)學(xué)院,上海201620)

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