本文選題：蓄電池 + SOC(state��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：本文得到了來自中國航天科工集團“蓄電池剩余電量監(jiān)控系統(tǒng)”項目的支持,主要研究和建立了基于神經(jīng)網(wǎng)絡(luò)的蓄電池剩余電量的預(yù)測模型,系統(tǒng)可應(yīng)用于對蓄電池剩余電量有精確預(yù)測需求的設(shè)備中。本文建立了基于自適應(yīng)神經(jīng)網(wǎng)絡(luò)模糊推理系統(tǒng)(ANFIS)的蓄電池剩余電量預(yù)測模型。首先介紹了ANFIS的相關(guān)理論,指出選用該算法的原因;接著分析了影響蓄電池SOC的主要因素,以此確定作為模型輸入的相關(guān)蓄電池參數(shù),再對各輸入?yún)?shù)的論域進行了模糊集劃分和隸屬度函數(shù)計算;最后得出ANFIS模型結(jié)構(gòu),確定了網(wǎng)絡(luò)學(xué)習(xí)算法。進行相關(guān)實驗,依照實驗數(shù)據(jù)對模型進行了訓(xùn)練及驗證。首先介紹了實驗所用設(shè)備及制定的實驗方案,對實驗數(shù)據(jù)進行初步分析;然后編寫了MATLAB仿真程序并制定了具體仿真方案,用不同方法構(gòu)造初始ANFIS模型,利用實驗數(shù)據(jù)對模型網(wǎng)絡(luò)進行訓(xùn)練,分析過程中ANFIS的結(jié)構(gòu)和參數(shù)的變化,將模型值與實際測得的結(jié)果進行對比。仿真結(jié)果表明了該模型對幾種ANFIS網(wǎng)絡(luò)的預(yù)測都比較準確,采用減法聚類法產(chǎn)生的ANFIS網(wǎng)絡(luò)最優(yōu)—經(jīng)訓(xùn)練后,其節(jié)點數(shù)相對最少。最后,對網(wǎng)絡(luò)的各個參數(shù)進行了調(diào)整并再次用仿真比對預(yù)測效果。完成了硬件的設(shè)計和軟件的開發(fā)。硬件設(shè)計方面,測量單元采用單片機集成了蓄電池內(nèi)阻、電壓、電流、溫度的測量及通信等模塊,其中的難點在于蓄電池內(nèi)阻的測量,文中用交流(正弦波)注入法測量內(nèi)阻。軟件方面,系統(tǒng)由測量單元和顯控單元組成,前者主要完成蓄電池數(shù)據(jù)的采集并傳輸給顯控,后者主要運行ANFIS算法并將結(jié)果提供給用戶。測量單元同時要求完成蓄電池各參數(shù)各測量及低功耗控制等工作;另外,顯控單元包括用戶UI的設(shè)計。最后,本部分還制定了基于MODBUS-RTU的通信協(xié)議的指令及應(yīng)答規(guī)則。文章最后部分測試了系統(tǒng)的實際運行效果。測試結(jié)果顯示:測量單元所測得數(shù)據(jù)誤差在5%以內(nèi),ANFIS算法處理顯控單元及測量單元數(shù)據(jù)得到的蓄電池SOC預(yù)測值與實測值誤差最大為0.0046,該結(jié)果滿足工程應(yīng)用需求。然而,系統(tǒng)(尤其是顯控單元)存在運行速度較慢且占用顯控單元資源較多的問題,導(dǎo)致了顯控系統(tǒng)的整體性能降低,鑒于此,提出了兩種改進方法,并對前景進行了審慎的預(yù)測。
[Abstract]:This paper is supported by the project of "Monitoring and Control system of Battery residual quantity" of China Aerospace Science and Technology Group. The prediction model of battery residual quantity based on neural network is studied and established. The system can be used to accurately predict the demand for battery surplus. In this paper, a prediction model of battery residual quantity based on adaptive neural network fuzzy inference system (ANFIS) is established. This paper first introduces the relevant theory of ANFIS, points out the reason why the algorithm is selected, then analyzes the main factors that affect the storage battery SOC, and then determines the relevant battery parameters as the input of the model. Then the fuzzy set partition and membership function calculation of each input parameter are carried out. Finally, the structure of ANFIS model is obtained, and the network learning algorithm is determined. The model was trained and validated according to the experimental data. This paper first introduces the equipment used in the experiment and the experimental scheme, analyzes the experimental data, then writes the MATLAB simulation program and formulates the specific simulation scheme, and constructs the initial ANFIS model with different methods. The model network is trained with experimental data, and the changes of the structure and parameters of ANFIS are analyzed. The model values are compared with the measured results. The simulation results show that the model is accurate for several kinds of ANFIS networks, and the subtractive clustering method is used to generate the optimal ANFIS networks. Finally, the parameters of the network are adjusted and the simulation results are compared again. Completed the hardware design and software development. In the aspect of hardware design, the single chip microcomputer is used to integrate the internal resistance, voltage, current, temperature and communication module of the battery. The difficulty lies in the measurement of the internal resistance of the battery. The AC (sinusoidal wave) injection method is used to measure the internal resistance in this paper. In software, the system consists of measurement unit and display control unit, the former mainly completes the data acquisition and transmission to display and control, and the latter mainly runs ANFIS algorithm and provides the results to the user. In addition, the display and control unit includes the design of user UI. Finally, this part also formulates the instruction and reply rule of communication protocol based on MODBUS-RTU. In the last part of the paper, the actual running effect of the system is tested. The test results show that the error of the measured data is less than 5%. The maximum error between the predicted value and the measured value of battery SOC is 0.0046, which meets the requirement of engineering application. However, the system (especially the display and control unit) has the problems of slow running speed and occupying more resources of the display and control unit, which leads to the deterioration of the overall performance of the display and control system. In view of this, two improved methods are proposed. The prospect is forecasted prudently.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM912;TP274

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本文編號：1919430