本文選題：SOC估計　切入點：參數(shù)辨識　出處：《吉林大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來，電動汽車發(fā)展迅速，作為其核心部件，動力電池成為全球研究的熱點，而準確地估計電池荷電狀態(tài)（State of Charge, SOC）一直是電動汽車電池研究領(lǐng)域的重點和難點。 本文圍繞提高動力電池SOC估計的精度問題主要做了以下幾方面的工作： 首先明確了研究動力電池的意義，接著比較了幾種常用的動力電池，指出了鋰離子動力電池的優(yōu)越性，討論了SOC的定義及其影響因素，并對電池SOC估計的常用方法進行了較為全面地介紹和分析。 接下來研究了鋰離子電池模型，分析比較了幾種常用的電化學(xué)模型和等效電路模型，綜合各模型優(yōu)缺點，建立了便于工程實現(xiàn)的二階RC網(wǎng)絡(luò)等效電路模型，并推導(dǎo)出了其狀態(tài)空間模型。測取了鋰離子電池的開路電壓（Open Curcit Valtage,OCV）OCV-SOC關(guān)系，對二階等效電路模型的參數(shù)進行離線辨識，并對模型進行了驗證。 采用適合實時運算的遺忘因子遞推最小二乘法在線辨識鋰離子電池等效電路模型的參數(shù)，并在Matlab/Simulink環(huán)境下編寫辨識程序，同時封裝成為可以在線運行的S-Function形式。在自定義電流放電情況下辨識了等效電路模型各參數(shù)，結(jié)合鋰離子電池特性分析實驗結(jié)果，證明了辨識方法的正確性和合理性。 提出用聯(lián)合參數(shù)辨識的結(jié)合卡爾曼濾波的開路電壓法（簡稱“聯(lián)合估計算法”）來估計鋰離子電池的SOC，即使用遺忘因子最小二乘法對模型參數(shù)在線辨識、修正，利用卡爾曼濾波算法估計電池的開路電壓，再根據(jù)開路電壓法來估算電池SOC。在這部分中，首先，，討論了實驗結(jié)果的參考值；其次，搭建了易于實時在線運行的聯(lián)合估計算法Simulink模型；最后，在模擬工況下驗證了聯(lián)合估計算法估計SOC的準確性。
[Abstract]:In recent years, electric vehicle (EV) has developed rapidly, as its core component, power battery has become a global research hotspot, and accurate estimation of the state of charge (SOCC) has been the focus and difficulty in the field of EV battery research. The main work of this paper is to improve the accuracy of SOC estimation of power battery. In this paper, the significance of studying power battery is clarified, then several kinds of commonly used battery are compared, the superiority of lithium ion battery is pointed out, and the definition of SOC and its influencing factors are discussed. The common methods of battery SOC estimation are introduced and analyzed. Then, the model of lithium ion battery is studied, and several common electrochemical models and equivalent circuit models are analyzed and compared. Combining the advantages and disadvantages of each model, the equivalent circuit model of second-order RC network is established, which is convenient for engineering implementation. The open circuit voltage open Curcit value OCVOCV-SOC relationship of the lithium ion battery is measured, and the parameters of the second order equivalent circuit model are identified off-line, and the model is verified. The parameters of the equivalent circuit model of lithium ion battery are on-line identified by the recursive least square method of forgetting factor, which is suitable for real-time operation, and the identification program is written under the environment of Matlab/Simulink. At the same time, the encapsulation becomes the S-Function form which can be run online. The parameters of the equivalent circuit model are identified in the case of self-defined current discharge, and the correctness and rationality of the identification method are proved by analyzing the experimental results of the characteristics of the lithium-ion battery. An open-circuit voltage method ("joint estimation algorithm") based on combined parameter identification and Kalman filter is proposed to estimate SOCof lithium-ion batteries, even though the forgetting factor least square method is used to identify and modify the model parameters online. The open circuit voltage of the battery is estimated by Kalman filter algorithm, and the SOC of the battery is estimated according to the open circuit voltage method. In this part, the reference value of the experimental results is discussed firstly; secondly, the reference value of the experimental results is discussed. The Simulink model of joint estimation algorithm is built, which is easy to run on line in real time. Finally, the accuracy of joint estimation algorithm is verified under the simulation condition.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U469.72;TM912

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