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  本文選題：荷電狀態(tài)　切入點：健康狀態(tài)　出處：《西南交通大學》2017年碩士論文

【摘要】：鋰電池在交通工具、電網(wǎng)、移動設備中被大量應用。然而在汽車領(lǐng)域,由于汽車使用環(huán)境差異極大,因此,鋰電池作為它的一種主要動力來源,不但需要在一些復雜的外部條件,而且需要在這些苛刻的使用條件下甚至是在遭受破壞時,保證駕駛者以及乘客的安全。在這一過程中,要保證電池的安全使用,并提供更好的電池管理策略,需要對電池自身狀態(tài)進行估計。常見鋰電池狀態(tài)描述變量有荷電狀態(tài)(State of Charge,SoC)、電池的健康狀態(tài)(State of Health,SoH)以及電池的功率狀態(tài)(State of Power,SoP)。三者分別描述電池可持續(xù)對外供電或受電的能力、電池剩余使用壽命以及電池即時對外進行輸出電能或接受電能的能力。雖然目前對于這三個狀態(tài)量的在線估計有大量的估計算法,但是汽車內(nèi)部嵌入式系統(tǒng)有限的計算能力限制了對諸多算法的使用。針對這一問題,給出一種聯(lián)合性算法,在統(tǒng)一使用改進后的Randle電池模型的基礎(chǔ)上,對三個狀態(tài)量進行在線估計。對于電池模型,一種標準化的迭代最小二乘算法被用于對電池模型參數(shù)進行識別并避免了最小二乘算法中,由于協(xié)方差矩陣反復迭代而導致的數(shù)據(jù)溢出問題。而識別的電池參數(shù)則被用于直接估計電池的健康狀態(tài)。由于實際情況中,噪聲的功率以及其對應的協(xié)方差矩陣是難以獲知的,因此,一種帶有自適應噪聲協(xié)方差矩陣的拓展卡爾曼濾波(Extended Kalman Filter,EKF)算法被用于電池進一步的SoC在線估計。估計過程中,電池開路電壓(Open Circuit Volt-age,OCV)也被作為狀態(tài)變量歸并入迭代過程中。其次,結(jié)合電池模型以及改進后EKF算法中的狀態(tài)方程,一種基于多限制條件下峰值功率的估計算法被給出。其計算過程涵蓋對電壓電流以及電池SoC處于極限狀態(tài)下的峰值功率計算。其結(jié)果結(jié)合電池額定功率被最終用于SoP的估計。三個部分的算法的準確性均有仿真實驗加以驗證。最后,硬件實現(xiàn)的電池數(shù)據(jù)采集卡配合BTS-5V300A設備通過實驗對電池模型參數(shù)識別算法以及電池SoC、SoH與SoP估計算法的估計效果進行了驗證。仿真實驗與電池實驗結(jié)果的對比,驗證了估計算法的有效性。
[Abstract]:Lithium batteries are widely used in vehicles, power grids and mobile devices.However, in the field of automobile, because of the great difference in the environment of automobile use, lithium battery is one of its main power sources, not only in some complicated external conditions, but also in some complicated external conditions.It is also necessary to ensure the safety of drivers and passengers in these harsh conditions, even in the event of destruction.In order to ensure the safe use of the battery and provide a better battery management strategy, it is necessary to estimate the state of the battery itself.The commonly described state variables of lithium batteries are the charged state of Chargeof SoC, the healthy state of the battery and the power state of the battery.They describe the ability of the battery to supply or receive electricity from the outside, the remaining life of the battery and the ability of the battery to output or receive the electric energy immediately.Although there are a large number of estimation algorithms for these three state variables online, the limited computing power of the embedded system limits the use of many algorithms.To solve this problem, a joint algorithm is proposed to estimate the three state variables on line on the basis of unified use of the improved Randle battery model.For the battery model, a standardized iterative least squares algorithm is used to identify the parameters of the battery model and to avoid the problem of data overflow caused by repeated iteration of the covariance matrix in the least squares algorithm.The identified battery parameters are used to estimate the health status of the battery directly.Because the power of noise and its corresponding covariance matrix are difficult to obtain in practice, an extended Kalman filter extended Kalman filter (EKF) algorithm with adaptive noise covariance matrix is used for further SoC on-line estimation of batteries.During the estimation process, the open Circuit voltage of the cell is also incorporated into the iterative process as a state variable.Secondly, combining the battery model and the state equation of the improved EKF algorithm, a new algorithm for estimating the peak power based on multiple constraints is presented.The calculation process includes the calculation of voltage and current and the peak power of SoC in the limit state.The results combined with battery rated power are ultimately used to estimate SoP.The accuracy of the algorithm is verified by simulation experiments.Finally, the hardware implementation of the battery data acquisition card and BTS-5V300A equipment through experiments to identify the parameters of the battery model algorithm and the battery SoH and SoP estimation algorithm is verified.The effectiveness of the estimation algorithm is verified by comparing the simulation results with the battery experiments.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM912

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本文編號：1716211