本文關(guān)鍵詞：電動(dòng)汽車(chē)鋰離子動(dòng)力電池組健康狀態(tài)估計(jì)方法的研究　出處：《青島科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 電池組SOH內(nèi)阻 等效物理模型 粒子濾波算法 SVM-PF算法

【摘要】：作為新一代電動(dòng)汽車(chē)的理想動(dòng)力源,鋰離子動(dòng)力電池組的健康狀態(tài)在電動(dòng)汽車(chē)的實(shí)際運(yùn)行過(guò)程中起著至關(guān)重要的作用。為保證電動(dòng)汽車(chē)在運(yùn)行過(guò)程中的安全性和穩(wěn)定性,需要對(duì)其車(chē)載動(dòng)力電池進(jìn)行必要的研究和管理。論文以鋰離子動(dòng)力電池組為研究對(duì)象,對(duì)電動(dòng)汽車(chē)鋰離子動(dòng)力電池組SOH的估計(jì)方法進(jìn)行了研究,經(jīng)過(guò)大量實(shí)驗(yàn)和仿真研究了以下內(nèi)容:(1)首先分析了鋰離子電池的特性,包括鋰離子電池的特點(diǎn)、結(jié)構(gòu)和工作原理,并給出了鋰離子動(dòng)力電池SOH的評(píng)價(jià)指標(biāo)。然后介紹了影響鋰離子電池組SOH的因素,從單體電池不一致性和單體電池連接方式兩個(gè)角度分析了它們對(duì)電池組SOH的影響,并重點(diǎn)剖析了單體電池不同的連接方式對(duì)電池組SOH性能可靠性的影響。由于電池的內(nèi)阻既能夠與電池電壓、電流等其他相關(guān)參數(shù)聯(lián)系起來(lái),又能很好體現(xiàn)電池特性的變化,因此本文選擇內(nèi)阻的變化來(lái)作為電池SOH的表征參量。(2)在分析了幾種單體電池模型后發(fā)現(xiàn),電池等效物理模型可以體現(xiàn)電池的物理特性,也能與其化學(xué)特性建立一定聯(lián)系。因此在該模型基礎(chǔ)上,考慮將兩個(gè)鋰離子單體電池并聯(lián)后作為一個(gè)簡(jiǎn)化的子系統(tǒng),而N個(gè)這樣的子系統(tǒng)串聯(lián),就可以構(gòu)成所要研究的動(dòng)力電池組等效模型,然后再對(duì)鋰離子電池組進(jìn)行數(shù)學(xué)建模及實(shí)驗(yàn)分析。接下來(lái)選用50AH/3.6V型號(hào)的鋰離子電池進(jìn)行電池性能參數(shù)的測(cè)量試驗(yàn),依據(jù)測(cè)量所得的實(shí)驗(yàn)數(shù)據(jù),采用最小二乘參數(shù)辨識(shí)方法進(jìn)行模型參數(shù)辨識(shí)。最后在Matlab中利用最小二乘擬合工具驗(yàn)證了模型參數(shù)辨識(shí)結(jié)果的可行性。(3)結(jié)合粒子濾波算法理論與所建立的電池組模型對(duì)電池組內(nèi)阻進(jìn)行了跟蹤預(yù)測(cè),但該算法存在嚴(yán)重的粒子退化現(xiàn)象。為克服這一現(xiàn)象,本文嘗試將支持向量機(jī)引入粒子濾波算法中,增加粒子多樣性以提高對(duì)電池組SOH的預(yù)測(cè)精度。最后對(duì)兩種算法下的實(shí)驗(yàn)結(jié)果進(jìn)行了對(duì)比分析,實(shí)驗(yàn)表明SVM-PF算法下的跟蹤曲線整體波動(dòng)幅度更小,其平穩(wěn)性與預(yù)測(cè)精度明顯優(yōu)于粒子濾波算法,在一定程度上說(shuō)明了SVM-PF算法對(duì)電池組內(nèi)阻跟蹤預(yù)測(cè)的有效性與優(yōu)越性。
[Abstract]:As a new generation of electric vehicles, the ideal power source. The healthy state of Li-ion battery pack plays an important role in the actual operation of electric vehicle. In order to ensure the safety and stability of electric vehicle during operation. It is necessary to study and manage the on-board battery. In this paper, the SOH estimation method of Li-ion battery pack for electric vehicle is studied by taking the Li-ion battery pack as the research object. Firstly, the characteristics of lithium ion battery, including the characteristics, structure and working principle of lithium ion battery are analyzed. The evaluation index of SOH of Li-ion battery is given, and the factors influencing SOH of Li-ion battery are introduced. The influence of single cell inconsistency and single cell connection mode on the SOH of the battery pack is analyzed. The effect of different connection modes of single cell on the reliability of battery pack SOH is analyzed, because the internal resistance of the battery can be related to other related parameters such as battery voltage, current and so on. It can well reflect the change of battery characteristics, so this paper chooses the change of internal resistance as the characterization parameter of SOH.) after analyzing several kinds of single cell model, we find out. Battery equivalent physical model can reflect the physical characteristics of the battery, but also can establish a certain relationship with its chemical characteristics, so on the basis of the model. Considering two lithium ion monomer cells in parallel as a simplified subsystem, and N such subsystems in series, we can construct the equivalent model of power battery. Then the mathematical modeling and experimental analysis of the lithium ion battery pack. Then 50 AH / 3.6 V lithium ion battery was selected to measure the battery performance parameters according to the measured experimental data. The method of least square parameter identification is used to identify the model parameters. Finally, the feasibility of the model parameter identification results is verified by using the least square fitting tool in Matlab. Combined with the theory of particle filter algorithm and the established battery pack model, the internal resistance of the battery pack was tracked and predicted. However, this algorithm has serious particle degradation phenomenon. In order to overcome this phenomenon, this paper attempts to introduce support vector machine into particle filter algorithm. Increasing particle diversity to improve the prediction accuracy of battery pack SOH. Finally, the experimental results of the two algorithms are compared and analyzed. Experiments show that the overall fluctuation of the tracking curve under the SVM-PF algorithm is smaller, and its smoothness and prediction accuracy are obviously better than that of the particle filter algorithm. To a certain extent, the effectiveness and superiority of SVM-PF algorithm in the prediction of battery pack internal resistance tracking are demonstrated.
【學(xué)位授予單位】：青島科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U469.72;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 王相勤;;當(dāng)前我國(guó)電動(dòng)汽車(chē)發(fā)展的瓶頸問(wèn)題及對(duì)策[J];能源技術(shù)經(jīng)濟(jì);2011年03期

2 于申軍;周永超;李賀;陳志奎;;內(nèi)阻差異對(duì)鋰離子電池組安全性能的影響[J];化工學(xué)報(bào);2010年11期

3 高飛;李建玲;趙淑紅;王子冬;;鋰動(dòng)力電池壽命預(yù)測(cè)研究進(jìn)展[J];電子元件與材料;2009年06期

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本文編號(hào)：1356238