本文選題：鋰電池 + 參數(shù)采集�。� 參考：《河北工業(yè)大學》2015年碩士論文

【摘要】：由于能源危機與環(huán)境污染的影響,各國政府越來越重視新能源的開發(fā)與利用。鋰電池作為一種儲能裝置被研究人員廣泛應用于機器人與電動汽車上。針對鋰電池參數(shù)采集與綜合管理的要求,需要實時監(jiān)測鋰電池的狀態(tài)。本文以PIC18F45K80主控芯片,完成了以下主要任務:首先搭建了鋰電池參數(shù)采集平臺,其中分別以LTC6802電壓采集芯片、霍爾元件ACS712、熱敏電阻完成鋰電池電壓、電流、溫度的采集,實時采集鋰電池在工作時的電壓、電流、溫度以及剩余電量(SOC)的變化,并且通過串口通信將其在上位機上顯示。通過對電池參數(shù)的監(jiān)控,能根據(jù)系統(tǒng)的過壓、過流、過溫等故障進行合理管理,并及時提醒用戶切斷用電設(shè)備,延長了電池的壽命,提高系統(tǒng)的安全性。其次,將采集到的鋰電池參數(shù)信息進行保存,根據(jù)數(shù)據(jù)綜合分析鋰電池的電壓、內(nèi)阻、容量以及庫倫效率等基本性能,研究不同放電倍率對其的影響。最后采用BP神經(jīng)網(wǎng)絡對鋰電池SOC進行估算。以BP網(wǎng)絡的理論為基礎(chǔ),通過大量的實驗,確定了BP網(wǎng)絡的各層結(jié)構(gòu)、神經(jīng)元數(shù)、訓練函數(shù)等。同時為了提高網(wǎng)絡訓練的效果,對訓練集進行大量的實驗,逐步完善訓練集的結(jié)構(gòu),最終以MATLAB為平臺,進行網(wǎng)絡訓練實驗,并用訓練好的網(wǎng)絡對測試樣本進行SOC估算,實驗取得了較好的效果。通過基于PIC18F45K80的鋰電池參數(shù)采集與綜合管理系統(tǒng)的研究表明:本系統(tǒng)能夠準確的實現(xiàn)鋰電池電壓、電流、溫度等信息的采集,硬件采集模塊與LABVIEW上位機具有良好的通信效果,并且利用采集到的數(shù)據(jù)結(jié)合MATLAB實現(xiàn)了鋰電池剩余電量的估算,對鋰電池的管理較為完善,達到了設(shè)計要求。
[Abstract]:Due to the impact of energy crisis and environmental pollution, governments in various countries pay more and more attention to the development and utilization of new energy. As a kind of energy storage device, lithium battery is widely used in robots and electric vehicles. It is necessary to monitor the status of lithium battery in real time according to the requirement of parameter acquisition and integrated management. The main tasks of this paper are as follows: firstly, a lithium battery parameter acquisition platform is built, in which LTC6802 voltage acquisition chip, Hall element ACS712, thermistor completes the acquisition of lithium battery voltage, current and temperature. The changes of voltage, current, temperature and residual power of the lithium battery are collected in real time and displayed on the host computer by serial communication. By monitoring the battery parameters, the system can be reasonably managed according to the overvoltage, overcurrent and over-temperature faults, and the users should be reminded to cut off the electric equipment in time, thus prolonging the battery life and improving the safety of the system. Secondly, the collected lithium battery parameter information is saved, and the basic performance of lithium battery such as voltage, internal resistance, capacity and Coulomb efficiency are analyzed synthetically according to the data, and the influence of different discharge rate on lithium battery is studied. Finally, BP neural network is used to estimate the SOC of lithium battery. Based on the theory of BP network, the structure of each layer, the number of neurons and the training function of BP network are determined by a large number of experiments. At the same time, in order to improve the effect of network training, a large number of experiments are carried out on the training set, and the structure of the training set is improved step by step. Finally, the network training experiment is carried out on the platform of MATLAB, and the SOC of the test sample is estimated by the trained network. The experiment has achieved good results. The research of Lithium battery parameter acquisition and integrated management system based on PIC18F45K80 shows that the system can accurately realize the acquisition of lithium battery voltage, current, temperature and so on. The hardware acquisition module has good communication effect with LabVIEW host computer. The residual power of lithium battery is estimated by using the collected data and MATLAB. The management of lithium battery is perfect and the design requirement is met.
【學位授予單位】：河北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM912

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本文編號：2043097