本文選題：電動汽車 + 電池管理系統(tǒng)�。� 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：近年來,全球環(huán)境污染日益加重,國內(nèi)外霧霾天氣頻發(fā),嚴(yán)重影響了人們的生活質(zhì)量、威脅著人們的生命健康安全。因此,科研機(jī)構(gòu)、政府部門都開始將目光投向環(huán)境友好的新型交通工具——新能源電動汽車。新能源電動汽車在運(yùn)行過程中,其動力電池組是呈現(xiàn)非線性特點,動力電池組中的每一個單電池之間具有不一致性。所以,對電池組的剩余電量(State of Charge,SOC)估算及其均衡控制研究具有實際應(yīng)用價值。本文采用松下18650特斯拉電動汽車同型號電池開展研究工作。本文首先闡述了電池管理系統(tǒng)(Battery Management System,BMS)對電池組的剩余電量估算及均衡控制的重要意義。為了能夠滿足新能源電動汽車的動力需求,需要將許多單電池串聯(lián)或并聯(lián)構(gòu)成一個電池組,因為單電池具有不一致性,在使用過程中,對電動汽車動力電池的整體性能和安全性帶來嚴(yán)重影響,針對這一問題,本文對鋰電池不一致性進(jìn)行分析,對比充電均衡與放電均衡控制兩種均衡控制管理方法,并用MATLAB軟件對其進(jìn)行仿真。結(jié)果表明:當(dāng)剩余電量低于10%的時候,放電均衡好于充電均衡;其次,對于估算電池組荷電狀態(tài),本文采用BP神經(jīng)網(wǎng)絡(luò)算法(Back-Propagation),通過實驗獲樣本數(shù)據(jù),分析它們之間的數(shù)學(xué)關(guān)系,利用MATLAB軟件訓(xùn)練實驗所獲得的數(shù)據(jù),并用訓(xùn)練好的模型來估算電池的SOC,其最大誤差小于4%;最后,對雙電源管理系統(tǒng)的整體方案進(jìn)行設(shè)計。
[Abstract]:In recent years, the global environmental pollution is becoming more and more serious, and the frequent weather of haze at home and abroad has seriously affected people's quality of life and threatened people's health and safety. As a result, research institutions and government departments are beginning to focus on new environmentally-friendly vehicles-new energy electric vehicles. In the operation of new energy electric vehicle, the power battery pack is nonlinear, and there is inconsistency between each single cell in the power battery pack. Therefore, the research on the estimation of the state of charge SOC and its equalization control of the battery pack has practical application value. In this paper, the same type battery of Panasonic 18650 Tesla electric vehicle is used to carry out research work. In this paper, the importance of Battery Management system to the estimation and equalization control of the battery pack is described. In order to meet the power needs of new energy electric vehicles, many single cells need to be connected in series or in parallel to form a battery pack, because the single cell is inconsistent and in use, This paper analyzes the inconsistency of lithium battery and compares the two equalization control methods: charging equalization and discharge equalization control. It is simulated by MATLAB software. The results show that the discharge equalization is better than the charging equalization when the remaining charge is less than 10%. Secondly, the BP neural network algorithm is used to estimate the charging state of the battery pack, and the sample data are obtained by experiments, and the mathematical relations between them are analyzed. The data obtained from the MATLAB software training experiment are used to estimate the SOC of the battery with the trained model, and the maximum error is less than 4. Finally, the overall scheme of the dual power management system is designed.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM912;U469.72

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