本文選題：自動(dòng)引導(dǎo)車　切入點(diǎn)：電池剩余容量　出處：《湖北工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：自動(dòng)引導(dǎo)車(Automated Guided Vehicle,AGV車)是一種自動(dòng)化的搬運(yùn)設(shè)備,一般由蓄電池提供動(dòng)力,能夠按照既定的路線搬運(yùn)物料,是現(xiàn)代工業(yè)生產(chǎn)線必不可少的搬運(yùn)工具。AGV車工況特殊,實(shí)時(shí)準(zhǔn)確估算其電池SOC值難度較大。常用的安時(shí)積分法在特殊工況下準(zhǔn)確度較低,無法滿足工業(yè)生產(chǎn)需求。本文提出一種改進(jìn)擴(kuò)展卡爾曼濾波算法,使用該方法估算AGV車電池SOC值可以將誤差控制在5%以內(nèi)。具體研究?jī)?nèi)容如下:第一:詳細(xì)分析了AGV車實(shí)際運(yùn)行工況,明確其充電電流大,放電電流小,充電時(shí)間短,充電頻率高的工況特點(diǎn)。第二:建立Thevenin電池等效模型,使用擴(kuò)展卡爾曼濾波法估算AGV車電池SOC值,相比安時(shí)積分法估算精度有所提高,但是傳統(tǒng)擴(kuò)展卡爾曼濾波法在AGV車特殊工況下跟蹤效果差,由此帶來了較大的估算誤差。第三:針對(duì)傳統(tǒng)擴(kuò)展卡爾曼濾波法估算AGV車電池SOC值跟蹤效果差的問題,提出改進(jìn)擴(kuò)展卡爾曼濾波算法,將擴(kuò)展卡爾曼濾波法的濾波增益改進(jìn)為動(dòng)態(tài)調(diào)整濾波增益,提高擴(kuò)展卡爾曼濾波法在特殊工況下的跟蹤效果。第四:通過編程讀取AGV車實(shí)際運(yùn)行數(shù)據(jù)來模擬其工況,進(jìn)而分析擴(kuò)展卡爾曼濾波法估算AGV車電池SOC值的效果,驗(yàn)證改進(jìn)擴(kuò)展卡爾曼濾波算法的有效性。實(shí)驗(yàn)表明擴(kuò)展卡爾曼濾波法相對(duì)安時(shí)積分法估算精度較高,采用動(dòng)態(tài)校正的濾波增益提高了估算過程的跟蹤效果,解決了AGV車特殊工況下SOC估算不準(zhǔn)確的問題,將AGV車電池SOC值誤差控制在5%以內(nèi)。同時(shí)針對(duì)本算法進(jìn)行系統(tǒng)硬件和軟件設(shè)計(jì),并進(jìn)行了實(shí)驗(yàn)驗(yàn)證。鑒于AGV車與電動(dòng)汽車有很多相似之處,特別是隨著電動(dòng)汽車的發(fā)展其工況越來越復(fù)雜,如何提高復(fù)雜工況下電池SOC值估算精度具有較大的研究意義,因此本文研究成果有一定的推廣意義。
[Abstract]:Automated Guided vehicle (AGV) is a kind of automatic handling equipment, which is generally powered by batteries and can carry materials according to the established route. It is a necessary handling tool for modern industrial production line. It is difficult to estimate the SOC value of the battery in real time and accurately, and the common amp-hour integration method can not meet the demand of industrial production because of its low accuracy under special working conditions. In this paper, an improved extended Kalman filter algorithm is proposed. Using this method to estimate the SOC value of AGV vehicle battery, the error can be controlled within 5%. The specific research contents are as follows: first, the actual operating conditions of AGV vehicle are analyzed in detail, and it is clear that the charging current is large, the discharge current is small, and the charging time is short. Second, the equivalent model of Thevenin battery is established, and the SOC value of AGV vehicle battery is estimated by using extended Kalman filter method. However, the traditional extended Kalman filter method has poor tracking effect under the special working condition of AGV vehicle, which brings great estimation error. Third, aiming at the problem of poor tracking effect of traditional extended Kalman filter method in estimating SOC value of AGV vehicle battery, An improved extended Kalman filter (EKF) algorithm is proposed, in which the filter gain of the EKF is improved to dynamically adjust the filter gain. Improve the tracking effect of extended Kalman filter method under special working conditions. 4th: read the actual operation data of AGV vehicle by programming to simulate its working condition, and then analyze the effect of extended Kalman filter method to estimate the SOC value of AGV vehicle battery. The experimental results show that the extended Kalman filter method has higher estimation accuracy than the Anchorage integration method, and the tracking effect of the estimation process is improved by using the dynamic correction filter gain. The problem of inaccurate SOC estimation under special working conditions of AGV vehicle is solved, and the error of SOC value of AGV vehicle battery is controlled within 5%. At the same time, the system hardware and software are designed for this algorithm. In view of the similarities between AGV vehicle and electric vehicle, especially with the development of electric vehicle, it is of great significance to study how to improve the estimation accuracy of battery SOC value under complex operating conditions, especially with the development of electric vehicle. Therefore, the research results of this paper have certain popularization significance.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP23

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