發(fā)布時(shí)間：2018-07-03 19:52

  本文選題：電動(dòng)汽車 + 鋰離子動(dòng)力電池��； 參考：《天津大學(xué)》2014年博士論文

【摘要】：電動(dòng)汽車發(fā)展過程中，儲(chǔ)能元件一直是阻礙電動(dòng)汽車發(fā)展的瓶頸。動(dòng)力電池作為汽車的儲(chǔ)能元件，在汽車行駛過程中，需要時(shí)刻知道電池的核電狀態(tài)(state ofcharge, SOC)。由于電池是一種非線性動(dòng)力系統(tǒng)，使用過程中，電池模型參數(shù)又受溫度和老化等因素的影響而發(fā)生變化，因此實(shí)時(shí)估計(jì)電池SOC具有很大的難度。本文采用自適應(yīng)卡爾曼濾波算法估計(jì)電池狀態(tài)，即可以實(shí)時(shí)的辨識(shí)出電池模型的參數(shù)，又提高了估計(jì)精度。電動(dòng)汽車中電池是成組使用的，為了提高其使用效率，延長使用壽命，必須對(duì)電池組進(jìn)行均衡管理。本文采用基于專家系統(tǒng)的均衡控制策略，可以減小系統(tǒng)的均衡損耗，提高系統(tǒng)的均衡速度。通過研究工作，本文取得了以下研究成果： 1．統(tǒng)計(jì)電池外部電流和端電壓信號(hào)的方法估計(jì)電池健康狀態(tài)(state of health,SOH)。電池外部電流與端電壓信號(hào)獲取方便，本文從三個(gè)角度對(duì)其做統(tǒng)計(jì)分析，發(fā)現(xiàn)電池外部信號(hào)關(guān)于SOH具有統(tǒng)計(jì)規(guī)律，為電池SOH估計(jì)提供了一種新的思路。 2．多模型自適應(yīng)的方法估計(jì)電池SOC。采用卡爾曼濾波器估計(jì)電池SOC時(shí)，估計(jì)精度受電池模型準(zhǔn)確性的影響較大，電池模型參數(shù)隨著電池的老化和溫度變化而變化，因此傳統(tǒng)的卡爾曼濾波算法估計(jì)誤差較大。針對(duì)上述問題，本文采用了多模型自適應(yīng)的估計(jì)方法，提高了估計(jì)精度。多模型卡爾曼濾波算法是在電池的SOH分布范圍內(nèi)，選取幾種不同SOH電池建立模型，，分別基于每個(gè)模型設(shè)計(jì)卡爾曼濾波器，利用各個(gè)濾波器并行估計(jì)電池SOC，計(jì)算各個(gè)單一模型的權(quán)值，所有單一模型SOC估計(jì)的加權(quán)和即為最終SOC估計(jì)值。 3．自適應(yīng)無跡卡爾曼濾波算法估計(jì)電池SOC與歐姆內(nèi)阻。無跡卡爾曼濾波器不需要對(duì)系統(tǒng)模型做線性化處理，這樣既減小了計(jì)算量又提高了估計(jì)精度。本文利用無跡卡爾曼濾波器估計(jì)電池SOC，利用擴(kuò)展的卡爾曼濾波器辨識(shí)電池歐姆內(nèi)阻，兩個(gè)濾波器聯(lián)立構(gòu)成循環(huán)迭代算法，可以實(shí)時(shí)更新電池模型參數(shù)，提高了模型的準(zhǔn)確性，進(jìn)而提高了電池SOC的估計(jì)精度。由于電池歐姆內(nèi)阻可以表征電池SOH，因此可以進(jìn)一步估計(jì)出電池的SOH。 4．基于專家系統(tǒng)的電池組非能耗型電壓均衡控制策略。電池組電壓均衡控制的目標(biāo)是電池組工作過程中保持各單體電壓一致，均衡原則是減小均衡過程中的能耗，提高均衡速度。本文以開關(guān)電容均衡電路為例，分析了均衡電路容量，開關(guān)頻率與電池工作電流之間的關(guān)系，為均衡電路設(shè)計(jì)提供了一種理論分析的方法。
[Abstract]:In the development of electric vehicles, energy storage components have been the bottleneck of the development of electric vehicles. Power battery is the energy storage component of automobile. It is necessary to know the nuclear power state of the battery (state ofcharge, SOC at all times in the driving process of the vehicle. Since the battery is a nonlinear dynamic system, the parameters of the battery model are affected by temperature and aging, so it is very difficult to estimate the SOC of the battery in real time. In this paper, the adaptive Kalman filter algorithm is used to estimate the state of the battery, which can identify the parameters of the battery model in real time and improve the estimation accuracy. Batteries in electric vehicles are used in groups. In order to improve their efficiency and prolong their service life, the battery pack must be balanced management. In this paper, the equalization control strategy based on expert system is adopted, which can reduce the equilibrium loss of the system and improve the equalization speed of the system. The research results are as follows: 1. The method of estimating the healthy state of (state of by the method of estimating the external current and terminal voltage signals of the battery. The external current and terminal voltage signal of the battery is easy to obtain. This paper makes a statistical analysis of the signal from three angles, and finds that the external signal of the battery has the statistical law about SOH. It provides a new idea for SOH estimation of battery. 2. Multi-model adaptive method is used to estimate SOC. When using Kalman filter to estimate battery SOC, the estimation accuracy is greatly affected by the accuracy of the battery model. The parameters of the battery model vary with the aging of the battery and the change of temperature, so the estimation error of the traditional Kalman filter algorithm is large. In order to solve the above problems, a multi-model adaptive estimation method is used to improve the estimation accuracy. In the SOH distribution range of the battery, several different SOH cell models are selected and the Kalman filter is designed based on each model. The weight of each single model is calculated by using each filter to estimate the SOC in parallel. The weighted sum of SOC estimation of all single models is the final SOC estimation. 3. Adaptive unscented Kalman filter algorithm is used to estimate the SOC and ohmic internal resistance of the cell. The unscented Kalman filter does not need to linearize the system model, which not only reduces the computational complexity but also improves the estimation accuracy. In this paper, the unscented Kalman filter is used to estimate the SOCand the extended Kalman filter is used to identify the ohmic internal resistance of the battery. The two filters are combined to form a cyclic iterative algorithm, which can update the parameters of the battery model in real time and improve the accuracy of the model. Furthermore, the estimation accuracy of battery SOC is improved. Because the ohmic internal resistance of the battery can represent the SOH of the battery, the SOH.4 of the battery can be further estimated. The non-energy type voltage equalization control strategy of the battery pack based on expert system can be further estimated. The goal of voltage equalization control is to keep the voltage of each cell consistent during battery operation. The principle of equalization is to reduce the energy consumption and improve the equalization speed. Taking the switched capacitor equalization circuit as an example, this paper analyzes the relationship among the equalization circuit capacity, switching frequency and battery operating current, and provides a theoretical analysis method for the equalization circuit design.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：U469.72;TM912

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 莊全超;徐守冬;邱祥云;崔永麗;方亮;孫世剛;;鋰離子電池的電化學(xué)阻抗譜分析[J];化學(xué)進(jìn)展;2010年06期

2 何洪文;熊瑞;;基于滑模觀測器的鋰離子動(dòng)力電池荷電狀態(tài)估計(jì)[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2011年03期

3 薛振框,鄭榮良;電動(dòng)汽車蓄電池剩余電量計(jì)量技術(shù)的研究[J];江蘇理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2001年02期

4 戴海峰;孫澤昌;魏學(xué)哲;;利用雙卡爾曼濾波算法估計(jì)電動(dòng)汽車用鋰離子動(dòng)力電池的內(nèi)部狀態(tài)[J];機(jī)械工程學(xué)報(bào);2009年06期

5 李德偉;陳實(shí);吳鋒;楊凱;胡道中;;基于神經(jīng)網(wǎng)絡(luò)的動(dòng)力電池荷電狀態(tài)的預(yù)測[J];計(jì)算機(jī)應(yīng)用與軟件;2007年09期

6 陳清泉,孫立清;電動(dòng)汽車的現(xiàn)狀和發(fā)展趨勢[J];科技導(dǎo)報(bào);2005年04期

7 胡兵;;汽車尾氣排放與防治措施[J];科技信息;2010年11期

8 孫小慧;劉鍇;左志;;我國電動(dòng)汽車補(bǔ)貼政策研究[J];科技與管理;2011年06期

9 曹秉剛,張傳偉,白志峰,李竟成;電動(dòng)汽車技術(shù)進(jìn)展和發(fā)展趨勢[J];西安交通大學(xué)學(xué)報(bào);2004年01期

10 任光,劉軍,朱利民;并行多參考模型卡爾曼濾波系統(tǒng)仿真研究[J];系統(tǒng)仿真學(xué)報(bào);2000年06期

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