本文關(guān)鍵詞：電動汽車鋰離子動力電池組健康狀態(tài)估計方法的研究　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】：作為新一代電動汽車的理想動力源,鋰離子動力電池組的健康狀態(tài)在電動汽車的實際運行過程中起著至關(guān)重要的作用。為保證電動汽車在運行過程中的安全性和穩(wěn)定性,需要對其車載動力電池進行必要的研究和管理。論文以鋰離子動力電池組為研究對象,對電動汽車鋰離子動力電池組SOH的估計方法進行了研究,經(jīng)過大量實驗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、結(jié)構(gòu)和工作原理,并給出了鋰離子動力電池SOH的評價指標。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角度分析了它們對電池組SOH的影響,并重點剖析了單體電池不同的連接方式對電池組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,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,考慮將兩個鋰離子單體電池并聯(lián)后作為一個簡化的子系統(tǒng),而N個這樣的子系統(tǒng)串聯(lián),就可以構(gòu)成所要研究的動力電池組等效模型,然后再對鋰離子電池組進行數(shù)學(xué)建模及實驗分析。接下來選用50AH/3.6V型號的鋰離子電池進行電池性能參數(shù)的測量試驗,依據(jù)測量所得的實驗數(shù)據(jù),采用最小二乘參數(shù)辨識方法進行模型參數(shù)辨識。最后在Matlab中利用最小二乘擬合工具驗證了模型參數(shù)辨識結(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電池組內(nèi)阻進行了跟蹤預(yù)測,但該算法存在嚴重的粒子退化現(xiàn)象。為克服這一現(xiàn)象,本文嘗試將支持向量機引入粒子濾波算法中,增加粒子多樣性以提高對電池組SOH的預(yù)測精度。最后對兩種算法下的實驗結(jié)果進行了對比分析,實驗表明SVM-PF算法下的跟蹤曲線整體波動幅度更小,其平穩(wěn)性與預(yù)測精度明顯優(yōu)于粒子濾波算法,在一定程度上說明了SVM-PF算法對電池組內(nèi)阻跟蹤預(yù)測的有效性與優(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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U469.72;TP18

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本文編號：1356238