基于無跡卡爾曼濾波的動力鋰電池SOC估計(jì)與實(shí)現(xiàn)
發(fā)布時間：2018-07-14 19:41
【摘要】：隨著能源危機(jī)和環(huán)境污染的問題日益嚴(yán)重,各國政府對零排放和新能源的電動汽車的研發(fā)越來越重視。對電池狀態(tài)進(jìn)行控制和管理的電池管理系統(tǒng)是電動汽車發(fā)展需要突破的關(guān)鍵技術(shù)之一,準(zhǔn)確的電池荷電狀態(tài)(State Of Charge, SOC)估算是電池管理系統(tǒng)運(yùn)行的前提和關(guān)鍵,對電池使用壽命的提高和整車性能的提升都具有重要意義。為此本文開展動力鋰電池SOC的估計(jì)研究,主要內(nèi)容如下： 首先,介紹了鋰電池SOC估計(jì)的背景及意義,分析了SOC的估計(jì)現(xiàn)狀、定義以及影響因素。在了解動力鋰電池的工作原理基礎(chǔ)上,考慮工程實(shí)現(xiàn)的難易以及數(shù)學(xué)算法可以彌補(bǔ)等效模型的精確性,選擇將內(nèi)阻等效電路模型作為鋰離子電池的動力模型,此后進(jìn)行開路電壓和SOC關(guān)系標(biāo)定以及內(nèi)阻辨識實(shí)驗(yàn)獲得電池模型參數(shù)并驗(yàn)證該模型能較好的模擬電池特性。 其次,由于電池等效模型的開路電壓與SOC關(guān)系是高度非線性的函數(shù),無跡卡爾曼濾波算法相比擴(kuò)展卡爾曼濾波在解決非線性非高斯隨機(jī)系統(tǒng)的狀態(tài)問題有更好的估計(jì)精度。為此本文基于電池的內(nèi)阻模型,采用基于無跡卡爾曼濾波算法實(shí)現(xiàn)非線性條件下鋰電池SOC的估算。該算法將電池模型的內(nèi)阻和SOC作為狀態(tài)參數(shù),通過無跡變換來處理均值和協(xié)方差的非線性傳遞,在此基礎(chǔ)上采用卡爾曼濾波的框架,完成鋰電池SOC的估算的方法。通過對自定義充放電工況的SOC變化進(jìn)行了MATLAB估算仿真實(shí)驗(yàn),結(jié)果證明無跡卡爾曼濾波在該模型下能很好估算電池SOC,同時彌補(bǔ)模型的誤差。 最后搭建系統(tǒng)硬件平臺,該平臺主要有STM32最小系統(tǒng)、充放電保護(hù)電路、數(shù)據(jù)采集電路與CAN通訊的硬件電路設(shè)計(jì)。在IAR編譯環(huán)境下設(shè)計(jì)系統(tǒng)軟件程序,完成了電池組電壓、電流、溫度、以及SOC的估算各個模塊的軟件編程。通過實(shí)驗(yàn)驗(yàn)證系統(tǒng)的采集數(shù)據(jù)測量精度以及SOC估算精度。圖41幅,表4個,參考文獻(xiàn)60篇。
[Abstract]:With the problem of energy crisis and environmental pollution becoming more and more serious, governments pay more and more attention to the research and development of electric vehicles with zero emissions and new energy sources. The battery management system which controls and manages the battery state is one of the key technologies that need to be broken through in the development of electric vehicle. Accurate estimation of the state of charge (SOC) is the premise and key to the operation of the battery management system. It is of great significance for the improvement of battery life and the improvement of vehicle performance. The main contents of this paper are as follows: firstly, the background and significance of SOC estimation for lithium batteries are introduced, and the status quo, definition and influencing factors of SOC estimation are analyzed. On the basis of understanding the working principle of power lithium-ion battery and considering the difficulty of engineering and the mathematical algorithm which can make up for the accuracy of equivalent model, the equivalent circuit model of internal resistance is chosen as the dynamic model of lithium ion battery. Then the open circuit voltage and SOC relationship calibration and internal resistance identification experiments were carried out to obtain the parameters of the battery model and verify that the model can better simulate the characteristics of the battery. Secondly, because the open circuit voltage of the battery equivalent model is a highly nonlinear function, the unscented Kalman filter has better estimation accuracy than the extended Kalman filter in solving the state problem of nonlinear non-Gao Si stochastic systems. In this paper, based on the internal resistance model of the battery, the unscented Kalman filter algorithm is used to estimate the SOC of the lithium battery under the nonlinear condition. In this algorithm, the internal resistance and SOC of the battery model are taken as state parameters, and the nonlinear transfer of mean value and covariance is processed by unscented transformation. Based on this, the estimation method of SOC of lithium battery is completed by using Kalman filter framework. Based on the simulation experiment of SOC estimation based on MATLAB, the results show that the unscented Kalman filter can estimate the SOC of the battery well under the model, and make up the error of the model at the same time. Finally, the hardware platform of the system is built. The platform mainly includes STM32 minimum system, charge-discharge protection circuit, data acquisition circuit and can communication hardware circuit design. The software program of the system is designed under IAR compiling environment, and the software programming of each module of battery pack voltage, current, temperature and SOC estimation is completed. The measurement accuracy and SOC estimation accuracy of the system are verified by experiments. There are 41 figures, 4 tables and 60 references.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912
本文編號：2122740
[Abstract]:With the problem of energy crisis and environmental pollution becoming more and more serious, governments pay more and more attention to the research and development of electric vehicles with zero emissions and new energy sources. The battery management system which controls and manages the battery state is one of the key technologies that need to be broken through in the development of electric vehicle. Accurate estimation of the state of charge (SOC) is the premise and key to the operation of the battery management system. It is of great significance for the improvement of battery life and the improvement of vehicle performance. The main contents of this paper are as follows: firstly, the background and significance of SOC estimation for lithium batteries are introduced, and the status quo, definition and influencing factors of SOC estimation are analyzed. On the basis of understanding the working principle of power lithium-ion battery and considering the difficulty of engineering and the mathematical algorithm which can make up for the accuracy of equivalent model, the equivalent circuit model of internal resistance is chosen as the dynamic model of lithium ion battery. Then the open circuit voltage and SOC relationship calibration and internal resistance identification experiments were carried out to obtain the parameters of the battery model and verify that the model can better simulate the characteristics of the battery. Secondly, because the open circuit voltage of the battery equivalent model is a highly nonlinear function, the unscented Kalman filter has better estimation accuracy than the extended Kalman filter in solving the state problem of nonlinear non-Gao Si stochastic systems. In this paper, based on the internal resistance model of the battery, the unscented Kalman filter algorithm is used to estimate the SOC of the lithium battery under the nonlinear condition. In this algorithm, the internal resistance and SOC of the battery model are taken as state parameters, and the nonlinear transfer of mean value and covariance is processed by unscented transformation. Based on this, the estimation method of SOC of lithium battery is completed by using Kalman filter framework. Based on the simulation experiment of SOC estimation based on MATLAB, the results show that the unscented Kalman filter can estimate the SOC of the battery well under the model, and make up the error of the model at the same time. Finally, the hardware platform of the system is built. The platform mainly includes STM32 minimum system, charge-discharge protection circuit, data acquisition circuit and can communication hardware circuit design. The software program of the system is designed under IAR compiling environment, and the software programming of each module of battery pack voltage, current, temperature and SOC estimation is completed. The measurement accuracy and SOC estimation accuracy of the system are verified by experiments. There are 41 figures, 4 tables and 60 references.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912
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