鋰離子電池狀態(tài)估算方法研究與實現(xiàn)
發(fā)布時間：2018-11-15 18:57
【摘要】：近年來,面對能源危機(jī)、環(huán)境污染這些日益嚴(yán)峻問題的威脅,世界各國都在加緊研發(fā)電動汽車。電動汽車以其優(yōu)良的節(jié)能環(huán)保無污染的特點成為未來汽車產(chǎn)業(yè)的發(fā)展重點。其中,作為電動汽車的動力源的電池成為制約電動汽車發(fā)展的瓶頸。電池管理系統(tǒng)(Battery Management System,BMS)作為全面監(jiān)控和管理電池的關(guān)鍵,通過充放電均衡檢測保障電池正常合理工作,能夠有效提高電池循環(huán)使用壽命,避免不合理使用和降低不必要的風(fēng)險。其中,電池及電池組剩余電量(State of Charge,SOC)和健康狀態(tài)(State of Health,SOH)的在線估算是電池管理系統(tǒng)合理高效運行的關(guān)鍵。研究具有較高精度的SOC及SOH估計算法對于電池管理系統(tǒng)而言是極其重要的,它能夠為延長電池壽命、提高電池利用率等提供有效的支持。本文以鋰離子電池狀態(tài)估算算法為主要研究內(nèi)容,在分析了現(xiàn)有BMS研究水平的基礎(chǔ)上,結(jié)合鋰離子電池本身的特點進(jìn)行鋰離子電池管理算法的研究和實現(xiàn)。論文首先通過對電池模型的研究介紹,選擇二階RC等效電路模型并通過MATLAB建立了鋰離子電池非線性模型并進(jìn)行了參數(shù)辨識,結(jié)合實驗數(shù)據(jù)驗證了所建模型的有效性。然后通過對現(xiàn)在幾種常用的電池荷電狀態(tài)(SOC)估算算法的優(yōu)缺點進(jìn)行分析,基于所建立的鋰離子電池二階模型研究了基于擴(kuò)展卡爾曼濾波的鋰離子電池SOC估算算法,采用Simulink對SOC估算算法進(jìn)行仿真驗證。對于鋰離子電池健康狀態(tài),通過對實驗數(shù)據(jù)的深入分析,提出了一種雙脈沖SOH的快速檢測算法。最后,根據(jù)對鋰離子電池SOH的估算采用粒子濾波(Particle Filter)算法對鋰離子電池剩余使用壽命進(jìn)行了預(yù)測在MATLAB中進(jìn)行代碼實現(xiàn)和仿真,與實驗數(shù)據(jù)進(jìn)行對比驗證,達(dá)到了較好的預(yù)測精度。本文通過對鋰離子電池剩余電量及健康狀態(tài)估算方法的研究,以及對電池壽命的預(yù)測算法的實現(xiàn),結(jié)合試驗數(shù)據(jù)進(jìn)行對比,達(dá)到了較好的估算精度,為鋰離子動力電池在電動汽車及儲能系統(tǒng)中的應(yīng)用奠定了技術(shù)基礎(chǔ)。
[Abstract]:In recent years, facing the threat of energy crisis and environmental pollution, every country in the world is speeding up the research and development of electric vehicles. Electric vehicle (EV) has become the focus of automotive industry in the future because of its excellent characteristics of energy saving, environmental protection and no pollution. Among them, as the power source of electric vehicles, battery becomes the bottleneck of the development of electric vehicles. Battery management system (Battery Management System,BMS) is the key to the overall monitoring and management of the battery. The battery cycle life can be effectively improved by the charge / discharge balance detection to ensure the normal and reasonable operation of the battery. Avoid unreasonable use and reduce unnecessary risks. The online estimation of (State of Charge,SOC and (State of Health,SOH is the key to the reasonable and efficient operation of the battery management system. It is very important to study SOC and SOH estimation algorithms with high accuracy for battery management system, which can provide effective support for prolonging battery life and improving battery utilization rate. In this paper, the state estimation algorithm of lithium ion battery is taken as the main research content. Based on the analysis of the existing BMS research level, the research and implementation of the lithium ion battery management algorithm are carried out according to the characteristics of the lithium ion battery itself. The second order RC equivalent circuit model is selected and the nonlinear model of lithium-ion battery is established by MATLAB. The validity of the model is verified by the experimental data. Then the advantages and disadvantages of several commonly used (SOC) estimation algorithms are analyzed. Based on the established second-order model of lithium-ion battery, the SOC estimation algorithm based on extended Kalman filter is studied. The SOC estimation algorithm is simulated by Simulink. For the healthy state of lithium ion battery, a fast detection algorithm of double pulse SOH is proposed by analyzing the experimental data. Finally, according to the estimation of lithium ion battery SOH, the residual service life of lithium ion battery is predicted by particle filter (Particle Filter) algorithm. The code realization and simulation are carried out in MATLAB, and the results are compared with the experimental data. Good prediction accuracy has been achieved. In this paper, the method of estimating the residual charge and health state of Li-ion battery is studied, and the prediction algorithm of battery life is realized. In combination with the experimental data, the estimation accuracy is achieved. It lays a technical foundation for the application of lithium ion power battery in electric vehicle and energy storage system.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912
本文編號：2334157
[Abstract]:In recent years, facing the threat of energy crisis and environmental pollution, every country in the world is speeding up the research and development of electric vehicles. Electric vehicle (EV) has become the focus of automotive industry in the future because of its excellent characteristics of energy saving, environmental protection and no pollution. Among them, as the power source of electric vehicles, battery becomes the bottleneck of the development of electric vehicles. Battery management system (Battery Management System,BMS) is the key to the overall monitoring and management of the battery. The battery cycle life can be effectively improved by the charge / discharge balance detection to ensure the normal and reasonable operation of the battery. Avoid unreasonable use and reduce unnecessary risks. The online estimation of (State of Charge,SOC and (State of Health,SOH is the key to the reasonable and efficient operation of the battery management system. It is very important to study SOC and SOH estimation algorithms with high accuracy for battery management system, which can provide effective support for prolonging battery life and improving battery utilization rate. In this paper, the state estimation algorithm of lithium ion battery is taken as the main research content. Based on the analysis of the existing BMS research level, the research and implementation of the lithium ion battery management algorithm are carried out according to the characteristics of the lithium ion battery itself. The second order RC equivalent circuit model is selected and the nonlinear model of lithium-ion battery is established by MATLAB. The validity of the model is verified by the experimental data. Then the advantages and disadvantages of several commonly used (SOC) estimation algorithms are analyzed. Based on the established second-order model of lithium-ion battery, the SOC estimation algorithm based on extended Kalman filter is studied. The SOC estimation algorithm is simulated by Simulink. For the healthy state of lithium ion battery, a fast detection algorithm of double pulse SOH is proposed by analyzing the experimental data. Finally, according to the estimation of lithium ion battery SOH, the residual service life of lithium ion battery is predicted by particle filter (Particle Filter) algorithm. The code realization and simulation are carried out in MATLAB, and the results are compared with the experimental data. Good prediction accuracy has been achieved. In this paper, the method of estimating the residual charge and health state of Li-ion battery is studied, and the prediction algorithm of battery life is realized. In combination with the experimental data, the estimation accuracy is achieved. It lays a technical foundation for the application of lithium ion power battery in electric vehicle and energy storage system.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912
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相關(guān)期刊論文 前1條
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相關(guān)碩士學(xué)位論文 前1條
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，本文編號：2334157
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