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

【摘要】：鋰電池在交通工具、電網(wǎng)、移動(dòng)設(shè)備中被大量應(yīng)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?jiǎng)恿碓?不但需要在一些復(fù)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(shí),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(duì)電池自身狀態(tài)進(jìn)行估計(jì)。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(duì)外供電或受電的能力、電池剩余使用壽命以及電池即時(shí)對(duì)外進(jìn)行輸出電能或接受電能的能力。雖然目前對(duì)于這三個(gè)狀態(tài)量的在線估計(jì)有大量的估計(jì)算法,但是汽車內(nèi)部嵌入式系統(tǒng)有限的計(jì)算能力限制了對(duì)諸多算法的使用。針對(duì)這一問題,給出一種聯(lián)合性算法,在統(tǒng)一使用改進(jìn)后的Randle電池模型的基礎(chǔ)上,對(duì)三個(gè)狀態(tài)量進(jìn)行在線估計(jì)。對(duì)于電池模型,一種標(biāo)準(zhǔn)化的迭代最小二乘算法被用于對(duì)電池模型參數(shù)進(jìn)行識(shí)別并避免了最小二乘算法中,由于協(xié)方差矩陣反復(fù)迭代而導(dǎo)致的數(shù)據(jù)溢出問題。而識(shí)別的電池參數(shù)則被用于直接估計(jì)電池的健康狀態(tài)。由于實(shí)際情況中,噪聲的功率以及其對(duì)應(yīng)的協(xié)方差矩陣是難以獲知的,因此,一種帶有自適應(yīng)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(jìn)一步的SoC在線估計(jì)。估計(jì)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(jìn)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(jì)算法被給出。其計(jì)算過程涵蓋對(duì)電壓電流以及電池SoC處于極限狀態(tài)下的峰值功率計(jì)算。其結(jié)果結(jié)合電池額定功率被最終用于SoP的估計(jì)。三個(gè)部分的算法的準(zhǔn)確性均有仿真實(shí)驗(yàn)加以驗(yàn)證。最后,硬件實(shí)現(xiàn)的電池?cái)?shù)據(jù)采集卡配合BTS-5V300A設(shè)備通過實(shí)驗(yàn)對(duì)電池模型參數(shù)識(shí)別算法以及電池SoC、SoH與SoP估計(jì)算法的估計(jì)效果進(jìn)行了驗(yàn)證。仿真實(shí)驗(yàn)與電池實(shí)驗(yàn)結(jié)果的對(duì)比,驗(yàn)證了估計(jì)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(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM912

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