本文選題：磷酸鐵鋰電池 + 等效電路模型��； 參考：《吉林大學(xué)》2014年碩士論文

【摘要】：進(jìn)入20世紀(jì)后，能源危機(jī)與環(huán)境污染等問題在全球范圍內(nèi)表現(xiàn)的愈加突出，在此形勢(shì)下，新能源汽車尤其是電動(dòng)汽車，得到了世界各國(guó)主要汽車廠商的高度關(guān)注。電池管理系統(tǒng)（BMS）作為電動(dòng)汽車的重要組成部分，可以對(duì)動(dòng)力電池進(jìn)行有效管理和控制，保障電池高效使用及行車安全。電池管理技術(shù)仍處于發(fā)展期，很多技術(shù)并不成熟，而其中研究的重點(diǎn)和難點(diǎn)便是如何提高電池荷電狀態(tài)（SOC）的估算精度。本文的研究對(duì)象選定為磷酸鐵鋰電池，從電池模型和基于模型的SOC估算方法這兩個(gè)方面進(jìn)行深入研究。 首先，介紹了鋰電池的基本原理和主要參數(shù)，通過對(duì)電池一系列的試驗(yàn)分析了電池的開路電壓與荷電狀態(tài)關(guān)系、歐姆內(nèi)阻及容量等基本特性。以此為基礎(chǔ)，結(jié)合常用電路模型的優(yōu)缺點(diǎn)對(duì)比，提出考慮電池容量時(shí)變性的二階RC等效電路模型，并采用指數(shù)擬合方法，在Matlab軟件中得到模型參數(shù)初值。為了更好的反應(yīng)電池特性，本文利用Matlab/Simulink對(duì)參數(shù)初值進(jìn)行了修正，并在各SOC點(diǎn)處估算出電池模型參數(shù)。實(shí)驗(yàn)結(jié)果表明修正參數(shù)后的等效電路模型提高了跟蹤電池電壓變化的精度。 其次，分析了傳統(tǒng)SOC估算方法，結(jié)合磷酸鐵鋰電池的非線性特性和二階RC等效電路模型確立了磷酸鐵鋰電池的狀態(tài)空間方程。在擴(kuò)展卡爾曼濾波算法對(duì)非線性狀態(tài)方程估算精度有限基礎(chǔ)上，提出了中心差分卡爾曼濾波算法。仿真結(jié)果表明中心差分卡爾曼濾波算法在同條件下對(duì)SOC的估算精度優(yōu)于拓展卡爾曼濾波算法。 最后，利用AVL Cruise軟件搭建電動(dòng)汽車整車模型，并在模擬城市道路工況下進(jìn)行仿真實(shí)驗(yàn)，，得到了電池在工況下的仿真數(shù)據(jù)。通過Cruise軟件與Matlab的接口，將動(dòng)力電池組的仿真數(shù)據(jù)輸入到估計(jì)模型中，利用中心差分卡爾曼濾波算法對(duì)SOC進(jìn)行估算，并與拓展卡爾曼濾波算法對(duì)比，結(jié)果表明基于中心差分卡爾曼濾波算法對(duì)整車SOC估算具有抗干擾性、收斂性與更高估算精度。
[Abstract]:After entering the 20th century, the problems of energy crisis and environmental pollution have become more and more prominent in the world. Under this situation, new energy vehicles, especially electric vehicles, have been highly concerned by the major automobile manufacturers in the world. As an important part of electric vehicle, Battery Management system (BMS) can effectively manage and control the power battery and ensure the efficient use of the battery and the safety of driving. The battery management technology is still in the developing stage, and many technologies are not mature, and the emphasis and difficulty of the research is how to improve the estimation accuracy of SOC. The research object of this paper is lithium iron phosphate battery, which is studied from two aspects: battery model and SOC estimation method based on model. Firstly, the basic principle and main parameters of lithium battery are introduced. The relationship between open circuit voltage and charge state, ohmic internal resistance and capacity are analyzed by a series of experiments. Based on this, combined with the comparison of the advantages and disadvantages of the common circuit models, a second-order RC equivalent circuit model considering the time-varying capacity of the battery is proposed, and the initial values of the model parameters are obtained by using the exponential fitting method in the Matlab software. In order to improve the characteristics of the reaction cell, the initial parameters of the cell were modified by Matlab/Simulink and the parameters of the model were estimated at each SOC point. The experimental results show that the precision of tracking the voltage change of the battery is improved by the equivalent circuit model. Secondly, the traditional SOC estimation method is analyzed, and the state space equation of the lithium iron phosphate battery is established by combining the nonlinear characteristics of the lithium iron phosphate battery and the second-order RC equivalent circuit model. Based on the limited estimation accuracy of the extended Kalman filtering algorithm for nonlinear state equations, a central differential Kalman filter algorithm is proposed. The simulation results show that the SOC estimation accuracy of the central differential Kalman filter algorithm is better than that of the extended Kalman filter algorithm under the same conditions. Finally, the AVL Cruise software is used to build the whole vehicle model of electric vehicle, and the simulation experiment is carried out under the simulation of the urban road condition, and the simulation data of the battery under the working condition are obtained. Through the interface between Cruise software and Matlab, the simulation data of power battery pack is input into the estimation model, and the SOC is estimated by using the central differential Kalman filter algorithm, and compared with the extended Kalman filter algorithm. The results show that the central differential Kalman filter algorithm has the advantages of anti-jamming, convergence and higher estimation accuracy for vehicle SOC estimation.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM912;U469.72

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