基于雙卡爾曼濾波算法的磷酸鐵鋰電池建模及SOC估計
發(fā)布時間：2019-03-13 18:55
【摘要】：當(dāng)前世界的能源儲備急劇減少,環(huán)境污染問題也變得越來越嚴(yán)峻。大力開發(fā)能夠替代傳統(tǒng)能源并且對環(huán)境無污染的新能源越來越成為大家重視的問題。在汽車領(lǐng)域,世界各國都加大了對新能源汽車的研究。動力電池作為電動汽車的動力來源,是能夠影響電動汽車發(fā)展程度的一個重要因素。相比于其他電池,磷酸鐵鋰電池由于其優(yōu)越的性能在作為電動汽車動力電池方面脫穎而出,應(yīng)用越來越廣泛。但磷酸鐵鋰電池存在單體電池間同一性較差的問題,因此設(shè)計一套對電池組進行管理的電池管理系統(tǒng)(Battery Management System,BMS)是非常關(guān)鍵的。對電池的荷電狀態(tài)(State of charge,SOC)進行準(zhǔn)確地估計是電池管理系統(tǒng)能夠有效運行的核心和關(guān)鍵。本文以一款50AH的磷酸鐵鋰電池作為研究對象,對其建立電池模型,并在該模型的基礎(chǔ)上重點研究SOC的估計方法。論文主要工作及成果如下:1、對電池SOC估計的研究背景進行了詳細(xì)的介紹,介紹磷酸鐵了鋰電池的優(yōu)點和特性和當(dāng)前對電池模型和電池SOC估計研究的現(xiàn)狀,為后文對本文研究對象磷酸鐵鋰電池進行電池建模和SOC估算建立了基礎(chǔ)。在對磷酸鐵鋰電池的工作原理和主要特性進行分析和總結(jié)的基礎(chǔ)上設(shè)計了實驗對電池特性進行測定。最后,介紹了當(dāng)前得到廣泛認(rèn)可的SOC的定義方法,并在傳統(tǒng)SOC定義方法的基礎(chǔ)上進行了改進,得到了動態(tài)SOC的定義方法,這是后文對電池進行建模和對電池進行SOC估計的理論依據(jù)。2、對電池的四種等效模型進行了分析和比較,最終確定二階RC模型作為本文研究電池的模型,考慮到電池單體間的同一性較差,因此在二階RC模型上做出改進,得到了改進二階RC模型,并對模型進行了公式推導(dǎo),并在Matlab中進行了仿真分析,驗證了模型的準(zhǔn)確性。3、詳細(xì)介紹了 Kalman算法的基本原理,并在經(jīng)典卡爾曼濾波算法的基礎(chǔ)上對適用于非線性系統(tǒng)的擴展卡爾曼濾波算法進行了原理介紹和公式推導(dǎo)。采用經(jīng)典卡爾曼濾波器和擴展卡爾曼濾波器相結(jié)合的雙卡爾曼濾波算法聯(lián)合估計電池SOC和電池模型參數(shù),通過實驗及Matlab仿真在橫流放電工況和脈沖放電這兩種工況下驗證了雙卡爾曼濾波算法聯(lián)合估計電池SOC和電池模型參數(shù)方法的準(zhǔn)確性。4、研究了基于CKF估計電池SOC的方法,并將這種估算方法和基于UKF估計電池SOC方法進行了比較,最后通過仿真實驗發(fā)現(xiàn)基于CKF估計電池SOC具有更高的精確性。5、利用最小二乘支持向量機(Least Squares Support Vector Machine,LSSVM)構(gòu)建LSSVM模型,在此基礎(chǔ)上實現(xiàn)對電池SOC的估計,并引入粒子群優(yōu)化算法(PSO)以提高訓(xùn)練效率與模型精度。通過恒流放電實驗和脈沖充放電實驗驗證了 PSO-LSSVM方法對電池SOC估計的有效性。
[Abstract]:At present, the world's energy reserves are sharply reduced, and environmental pollution is becoming more and more serious. More and more attention has been paid to the development of new energy which can replace the traditional energy and not pollute the environment. In the field of automobiles, countries all over the world have increased their research on new energy vehicles. As the power source of electric vehicle, power battery is an important factor that can influence the development degree of electric vehicle. Compared with other batteries, lithium iron phosphate battery is more and more widely used as electric vehicle power battery because of its superior performance. However, lithium iron phosphate batteries have the problem of poor identity among single batteries, so it is very important to design a battery management system (Battery Management System,BMS) for battery pack management. Accurate estimation of the charge state (State of charge,SOC) of the battery is the core and key to the effective operation of the battery management system. In this paper, a 50AH lithium iron phosphate battery is taken as the research object, and the battery model is established. On the basis of this model, the SOC estimation method is mainly studied. The main work and achievements are as follows: 1. The research background of battery SOC estimation is introduced in detail. The advantages and characteristics of iron phosphate lithium battery and the current research status of battery model and battery SOC estimation are also introduced. The foundation of the battery modeling and SOC estimation for the lithium ferric phosphate battery studied in this paper is established. Based on the analysis and summary of the working principle and main characteristics of the lithium iron phosphate battery, an experiment was designed to measure the characteristics of the battery. Finally, the definition method of SOC, which has been widely accepted, is introduced and improved on the basis of traditional SOC definition method, and the definition method of dynamic SOC is obtained. This is the theoretical basis of the battery modeling and SOC estimation. 2, the four equivalent models of the battery are analyzed and compared. Finally, the second-order RC model is selected as the model of the battery in this paper. Considering the poor identity between cells, this paper improves the second order RC model, obtains the improved second order RC model, deduces the formula of the model, and simulates the model in Matlab to verify the accuracy of the model. The basic principle of Kalman algorithm is introduced in detail. On the basis of classical Kalman filtering algorithm, the extended Kalman filter algorithm suitable for nonlinear systems is introduced in principle and formula derived. The dual Kalman filter algorithm combined with classical Kalman filter and extended Kalman filter is used to jointly estimate the parameters of battery SOC and battery model. The accuracy of the dual Kalman filter algorithm to jointly estimate the battery SOC and battery model parameters is verified by experiments and Matlab simulation under the condition of cross-flow discharge and pulse discharge. 4. 4. The method of estimating battery SOC based on CKF is studied. The method is compared with the SOC method based on UKF. Finally, the simulation results show that the estimation of battery SOC based on CKF has higher accuracy. 5. The least squares support vector machine (Least Squares Support Vector Machine,) is used to estimate the battery SOC with least square support vector machine (LSVM). LSSVM) constructs the LSSVM model, then realizes the estimation of battery SOC, and introduces the particle swarm optimization algorithm (PSO) to improve the training efficiency and model precision. The validity of the PSO-LSSVM method for SOC estimation is verified by the constant current discharge experiment and the pulse charge-discharge experiment.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM912
本文編號：2439680
[Abstract]:At present, the world's energy reserves are sharply reduced, and environmental pollution is becoming more and more serious. More and more attention has been paid to the development of new energy which can replace the traditional energy and not pollute the environment. In the field of automobiles, countries all over the world have increased their research on new energy vehicles. As the power source of electric vehicle, power battery is an important factor that can influence the development degree of electric vehicle. Compared with other batteries, lithium iron phosphate battery is more and more widely used as electric vehicle power battery because of its superior performance. However, lithium iron phosphate batteries have the problem of poor identity among single batteries, so it is very important to design a battery management system (Battery Management System,BMS) for battery pack management. Accurate estimation of the charge state (State of charge,SOC) of the battery is the core and key to the effective operation of the battery management system. In this paper, a 50AH lithium iron phosphate battery is taken as the research object, and the battery model is established. On the basis of this model, the SOC estimation method is mainly studied. The main work and achievements are as follows: 1. The research background of battery SOC estimation is introduced in detail. The advantages and characteristics of iron phosphate lithium battery and the current research status of battery model and battery SOC estimation are also introduced. The foundation of the battery modeling and SOC estimation for the lithium ferric phosphate battery studied in this paper is established. Based on the analysis and summary of the working principle and main characteristics of the lithium iron phosphate battery, an experiment was designed to measure the characteristics of the battery. Finally, the definition method of SOC, which has been widely accepted, is introduced and improved on the basis of traditional SOC definition method, and the definition method of dynamic SOC is obtained. This is the theoretical basis of the battery modeling and SOC estimation. 2, the four equivalent models of the battery are analyzed and compared. Finally, the second-order RC model is selected as the model of the battery in this paper. Considering the poor identity between cells, this paper improves the second order RC model, obtains the improved second order RC model, deduces the formula of the model, and simulates the model in Matlab to verify the accuracy of the model. The basic principle of Kalman algorithm is introduced in detail. On the basis of classical Kalman filtering algorithm, the extended Kalman filter algorithm suitable for nonlinear systems is introduced in principle and formula derived. The dual Kalman filter algorithm combined with classical Kalman filter and extended Kalman filter is used to jointly estimate the parameters of battery SOC and battery model. The accuracy of the dual Kalman filter algorithm to jointly estimate the battery SOC and battery model parameters is verified by experiments and Matlab simulation under the condition of cross-flow discharge and pulse discharge. 4. 4. The method of estimating battery SOC based on CKF is studied. The method is compared with the SOC method based on UKF. Finally, the simulation results show that the estimation of battery SOC based on CKF has higher accuracy. 5. The least squares support vector machine (Least Squares Support Vector Machine,) is used to estimate the battery SOC with least square support vector machine (LSVM). LSSVM) constructs the LSSVM model, then realizes the estimation of battery SOC, and introduces the particle swarm optimization algorithm (PSO) to improve the training efficiency and model precision. The validity of the PSO-LSSVM method for SOC estimation is verified by the constant current discharge experiment and the pulse charge-discharge experiment.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM912
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