【摘要】：蓄電池以其容量大、成本低、安全性好、技術(shù)成熟、原料豐富以及免維護(hù)等優(yōu)點(diǎn),廣泛應(yīng)用于光伏發(fā)電系統(tǒng)、風(fēng)力發(fā)電系統(tǒng)、不間斷電源系統(tǒng)(UPS)、照明以及電動(dòng)汽車等領(lǐng)域,是應(yīng)用最為廣泛的二次電池,至今尚未有任何一種電池能夠完全取代它。為了提高蓄電池的工作效率,有效延長(zhǎng)使用壽命,就必須對(duì)蓄電池的剩余容量或荷電狀態(tài)(SOC)進(jìn)行準(zhǔn)確的估算。而SOC的估算需建立電池等效電路模型,選擇合適的估算方法,因此本文針對(duì)閥控式密封鉛酸(VRLA)蓄電池,重點(diǎn)研究SOC的估算并圍繞以下幾個(gè)方面展開(kāi)：首先分析了VRLA蓄電池的工作原理及特性,并闡述了充放電倍率、環(huán)境溫度、電池健康狀態(tài)等因素對(duì)SOC的影響：重點(diǎn)介紹了國(guó)內(nèi)外常見(jiàn)的SOC估算方法,主要有：安時(shí)法、電動(dòng)勢(shì)法、神經(jīng)網(wǎng)絡(luò)法、模糊法以及卡爾曼濾波算法；重點(diǎn)分析比較了幾種常用的電池等效電路模型,選取改進(jìn)的PNGV模型作為蓄電池SOC估算的模型,對(duì)該模型采用HPPC脈沖實(shí)驗(yàn)進(jìn)行參數(shù)辨識(shí),并通過(guò)cftool工具擬合出模型的各個(gè)參數(shù)。通過(guò)對(duì)比分析采用兩種方法來(lái)估算SOC：一種是改進(jìn)的安時(shí)法結(jié)合電動(dòng)勢(shì)法。由于安時(shí)法只受到蓄電池工作電流以及充放電倍率的影響,可以彌補(bǔ)電動(dòng)勢(shì)法對(duì)電動(dòng)勢(shì)模型依賴的缺陷；而安時(shí)法存在的積累誤差問(wèn)題也可以通過(guò)電動(dòng)勢(shì)法得以校正。因此,本文是將電動(dòng)勢(shì)法與經(jīng)過(guò)修正的安時(shí)法通過(guò)并聯(lián)加權(quán)的結(jié)構(gòu)來(lái)對(duì)SOC進(jìn)行估算,從而兩種方法可以優(yōu)勢(shì)互補(bǔ),提高SOC的估算精度。另一種方法是擴(kuò)展卡爾曼濾波(EKF)算法,根據(jù)改進(jìn)的PNGV模型,建立電池系統(tǒng)的狀態(tài)方程和觀測(cè)方程,確定EKF估算SOC的步驟,實(shí)現(xiàn)SOC最小均方差估計(jì)。采用MATLAB進(jìn)行離線仿真,其估算結(jié)果接近于理論值。EKF估算精度高,在蓄電池SOC估算領(lǐng)域有著廣闊的應(yīng)用前景,因此進(jìn)一步實(shí)現(xiàn)基于EKF估算SOC的工程化很有必要。
[Abstract]:Battery is widely used in photovoltaic power system, wind power system, uninterruptible power supply system (UPS), lighting and electric vehicle for its advantages of large capacity, low cost, good safety, mature technology, abundant raw materials and no maintenance. Is the most widely used secondary battery, so far no battery can completely replace it. In order to improve the working efficiency and prolong the service life of the battery, it is necessary to estimate the residual capacity or the state of charge (SOC) accurately. But the SOC estimation needs to establish the battery equivalent circuit model, selects the suitable estimation method, therefore this article aims at the valve type sealed lead acid (VRLA) battery, This paper focuses on the estimation of SOC and focuses on the following aspects: firstly, the working principle and characteristics of VRLA battery are analyzed, and the charge-discharge rate and ambient temperature are described. The influence of battery health on SOC: the SOC estimation methods at home and abroad are introduced, such as ampere-hour method, electromotive force method, neural network method, fuzzy method and Kalman filter algorithm. Several commonly used equivalent circuit models of battery are analyzed and compared emphatically. The improved PNGV model is selected as the model of battery SOC estimation. The parameters of the model are identified by HPPC pulse experiment, and the parameters of the model are fitted by cftool tool. Two methods are used to estimate SOC through comparative analysis: one is an improved ampere-hour method combined with electromotive force method. Because the amperage method is only affected by the operating current of the battery and the charge / discharge rate, it can make up for the defect of the electromotive force method which depends on the electromotive force model, and the accumulated error problem of the ampere-hour method can also be corrected by the electromotive force method. Therefore, in this paper, the EMF method and the modified ampere-hour method are used to estimate SOC through parallel weighted structure, so that the two methods can complement each other and improve the precision of SOC estimation. Another method is extended Kalman filter (EKF) algorithm. According to the improved PNGV model, the state equation and observation equation of battery system are established, and the steps of estimating SOC by EKF are determined, and the minimum mean-variance estimation of SOC is realized. The off-line simulation with MATLAB shows that the estimation results are close to the theoretical value. EKF has high precision and has a broad application prospect in the field of SOC estimation of batteries. Therefore, it is necessary to further realize the engineering of SOC estimation based on EKF.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM912

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