本文關(guān)鍵詞： 鋰離子電池 健康狀態(tài) 雙卡爾曼濾波 模糊推理系統(tǒng)-自適應(yīng)雙卡爾曼濾波 剩余使用壽命 曲線擬合 灰色模型　出處：《南京航空航天大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：鋰離子電池無論是在軍用還是民用領(lǐng)域都得到了廣泛的應(yīng)用,準確對其進行健康狀態(tài)(State of Health,SOH)評估及剩余使用壽命(Remaning Useful Life,RUL)預(yù)測對于提高電池安全性與使用壽命具有重要意義。本文以鋰離子電池為研究對象,重點開展鋰離子電池SOH評估以及RUL預(yù)測方法的研究,具體研究內(nèi)容包括:1)介紹了鋰離子電池的工作原理,闡明了鋰離子電池常用性能參數(shù)的基本概念,概述了鋰離子電池的充電方式并通過實驗研究了環(huán)境溫度、放電電流對電池端電壓的影響以及電池容量退化規(guī)律。2)針對目前卡爾曼濾波算法大多是離線估計電池SOH,無法滿足實際工程需要,本文研究了一種基于雙卡爾曼濾波算法的電池SOH在線估計方法。首先,通過最小二乘法對電池模型參數(shù)進行辨識,實現(xiàn)對電池的建模。然后交替使用兩個卡爾曼濾波器分別估計電池的荷電狀態(tài)與歐姆內(nèi)阻。為了進一步提高估計精度,本文提出了一種基于模糊推理系統(tǒng)-自適應(yīng)雙卡爾曼濾波(fuzzy inference system-adaptive dual extended Kalman filter,FIS-ADEKF)方法,分別運用Sage-Husa自適應(yīng)算法與模糊控制器對狀態(tài)噪聲協(xié)方差與觀測噪聲協(xié)方差進行修正。最后設(shè)計了動態(tài)應(yīng)力測試工況實驗進行驗證,實驗結(jié)果表明,改進后的雙卡爾曼濾波算法能夠?qū)崿F(xiàn)電池SOH的在線估計,且不依賴于初始值,也不需要事先計算模型參數(shù),具有更高的準確性、收斂性和可行性。3)針對鋰離子電池剩余使用壽命預(yù)測問題,本文分別利用曲線擬合法與灰色模型來預(yù)測電池RUL。在對預(yù)測模型進行驗證時,本文首先用美國航空航天局艾姆斯研究中心的鋰離子電池實驗數(shù)據(jù)來驗證,然后通過自主搭建的實驗平臺獲得的實驗數(shù)據(jù)來進一步驗證。實驗結(jié)果表明,一次函數(shù)模型與灰色模型能夠很好的預(yù)測電池RUL,且隨著訓(xùn)練數(shù)據(jù)的增加,預(yù)測精度更高。
[Abstract]:Lithium ion batteries have been widely used in both military and civil fields. Soh) evaluation and residual service life / emanation Useful Life. Rul) prediction is of great significance to improve battery safety and service life. This paper focuses on the study of lithium ion battery SOH evaluation and RUL prediction methods. The working principle of lithium ion battery is introduced, and the basic concept of common performance parameters of lithium ion battery is expounded. The charging method of lithium ion battery is summarized and the ambient temperature is studied by experiment. The effect of discharge current on the terminal voltage of the battery and the degradation rule of battery capacity. (2) aiming at the current Kalman filtering algorithm, most of the current Kalman filtering algorithms estimate the SOH of the battery off-line, which can not meet the practical engineering needs. In this paper, a battery SOH online estimation method based on double Kalman filter algorithm is studied. Firstly, the parameters of battery model are identified by least square method. Then, two Kalman filters are used alternately to estimate the charged state and ohmic resistance of the battery. In order to further improve the estimation accuracy. In this paper, an adaptive double Kalman filter based on fuzzy inference system is proposed. Fuzzy inference system-adaptive dual extended Kalman filter. FIS-ADEKF method. Sage-Husa adaptive algorithm and fuzzy controller are used to modify the state noise covariance and observation noise covariance respectively. Finally, the dynamic stress test condition experiment is designed to verify the proposed method. The experimental results show that the improved double Kalman filter algorithm can realize the on-line estimation of battery SOH, and it does not depend on the initial value, nor does it need to calculate the model parameters in advance, so it has higher accuracy. Convergence and Feasibility. 3) aiming at the residual service life prediction of lithium ion battery, this paper uses curve fitting method and grey model to predict the battery RUL.When the prediction model is verified. In this paper, the experimental data of lithium-ion battery from Ames Research Center of NASA are first used to verify, and then the experimental data obtained from the self-built experimental platform are further verified. The experimental results show that. The primary function model and grey model can predict the battery RUL well, and with the increase of training data, the prediction accuracy is higher.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM912

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