本文關(guān)鍵詞：基于卡爾曼濾波的動(dòng)力電池組SOC精確估計(jì)，由筆耕文化傳播整理發(fā)布。

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杭州電子科技大學(xué)碩士學(xué)位論文基于卡爾曼濾波的動(dòng)力電池組SOC精確估計(jì)姓名:徐杰申請(qǐng)學(xué)位級(jí)別:碩士專業(yè):電路與系統(tǒng)指導(dǎo)教師:高明煜20091201杭州電子科技大學(xué)碩士學(xué)位論文I 摘要隨著石油能源的短缺和大氣污染的加劇,開(kāi)發(fā)節(jié)能環(huán)保型電動(dòng)汽車已經(jīng)成為現(xiàn)今汽車工業(yè)領(lǐng)域發(fā)展的主要趨勢(shì)。作為電動(dòng)汽車的動(dòng)力來(lái)源和能量載體,電池自身制造工藝以及成組應(yīng)用技術(shù)已成為推動(dòng)電動(dòng)汽車商業(yè)化的關(guān)鍵因數(shù)。因此,為了保證動(dòng)力電池能夠安全有效的工作,電動(dòng)汽車必須配置特定的電池管理系統(tǒng)對(duì)動(dòng)力電池組的狀態(tài)進(jìn)行控制和管理。電池剩余電量SOC估計(jì)一直是電池管理系統(tǒng)的核心,是反映電池運(yùn)作狀態(tài)的主要參數(shù),為整車控制策略提供判斷依據(jù)。本文以磷酸鐵鋰聚合物動(dòng)力電池為研究對(duì)象,采用卡爾曼濾波修正算法對(duì)動(dòng)力電池組進(jìn)行SOC估計(jì)。本文首先介紹了電動(dòng)汽車的發(fā)展現(xiàn)狀和車用動(dòng)力電池的性能要求,以磷酸鐵鋰電池的電化學(xué)特性為出發(fā)點(diǎn),分析了電池SOC的各種內(nèi)外影響因素及在線估算難點(diǎn)。從SOC的定義出發(fā),通過(guò)比較幾種常用SOC估計(jì)方法,并結(jié)合車用動(dòng)力電池的應(yīng)用環(huán)境,本文提出了卡爾曼濾波修正算法。該算法充分發(fā)揮了開(kāi)路電壓法、安時(shí)計(jì)量法和擴(kuò)展卡爾曼濾波法的優(yōu)點(diǎn),從而使得SOC的估算精度和實(shí)時(shí)性有了很大的提高。根據(jù)放電實(shí)驗(yàn)數(shù)據(jù),系統(tǒng)建立了與算法相關(guān)的充放電倍率SOC模型、溫度SOC模型、開(kāi)路電壓SOC模型和擴(kuò)展卡爾曼濾波復(fù)合模型。然后在此基礎(chǔ)上,系統(tǒng)分別從軟硬件角度建立了動(dòng)力電池組SOC估計(jì)系統(tǒng)。該系統(tǒng)是由數(shù)據(jù)采樣、算法執(zhí)行、通訊管理、保護(hù)控制、信息存儲(chǔ)及數(shù)據(jù)顯示等模塊組成,完成了電池電壓、充放電電流、溫度的在線檢測(cè)、卡爾曼濾波修正算法執(zhí)行、單片機(jī)SPI及串口通訊、LCD在線顯示、電池狀態(tài)診斷及保護(hù)等功能,從而在真正意義上實(shí)現(xiàn)了車用動(dòng)力電池組剩余電量的實(shí)時(shí)在線精確估計(jì)。最后制定電池組實(shí)驗(yàn)的充放電方案,使用汽車行駛工況HWFET、UDDS、FUDS來(lái)對(duì)電池組SOC估計(jì)算法進(jìn)行檢驗(yàn)和優(yōu)化。經(jīng)過(guò)工況測(cè)試和Matlab分析比較,本文提出的卡爾曼濾波修正算法具有很好的實(shí)際估計(jì)效果,完全符合電動(dòng)汽車對(duì)電池組SOC估計(jì)的準(zhǔn)確性要求。關(guān)鍵字:動(dòng)力電池,電動(dòng)汽車,卡爾曼濾波,剩余電量(SOC),在線實(shí)時(shí)估計(jì)杭州電子科技大學(xué)碩士學(xué)位論文II ABSTRACT Because of oil resources shortage and air quality degradation, electric vehicle with advantages of energy saving and environment protectionhas emerged as the main trend in automobile industry. As the major energy carrier and power source, battery’s manufacturing process and group application technology have been the key factors in promoting mercial progress of electric vehicle. Therefore, in order to keep power battery work securely and effectively, electric vehicle must be equipped with a specific management system to control and supervise the function of battery. The state of charge SOC estimation has always been the ponent in battery management system, which is one of the main parameters toreflect the battery working states and can provide judgment basis to vehicle control strategy. This paper takes the LiFeO4 polymer power battery as the research object, and usesKalman filter correction algorithm for battery pack online SOC estimation. Firstly, the paper describes the development of electric vehicle and the performance requirements of vehicular power battery, and it takes the electrochemical characteristics of LiFeO4battery as a starting point to analyze the various SOC effect factors and study the difficulties of online accurate estimation. paring monly used methods and considering the electric vehicle environment, this paper proposes a newmethod named Kalman filter correction algorithm on the basis of SOC definition. The algorithm gives such full play to the advantages of open circuit voltage method, ampere hour counting method and extended Kalman fitering algorithm that makes the estimation accuracy and real-time ability improved significantly. According to discharging experimental data, the system has established discharging or charging rate model, temperature model, open circuit voltage model and extended Kalman bined model. Secondly, the paper builds up the power battery pack’s SOC estimation system in view of software and hardware design.

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  本文關(guān)鍵詞：基于卡爾曼濾波的動(dòng)力電池組SOC精確估計(jì)，，由筆耕文化傳播整理發(fā)布。