本文選題：磷酸鐵鋰蓄電池　切入點(diǎn)：荷電狀態(tài)　出處：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：磷酸鐵鋰(LiFePO_4)蓄電池具有體積小、使用壽命長、可進(jìn)行大電流放電、免維護(hù)等優(yōu)勢,已廣泛應(yīng)用于電動車、通訊工具、儲能系統(tǒng)等領(lǐng)域。目前LiFePO_4電池作為純電動汽車的動力來源,最常被使用在該類場合,而作為電動汽車的核心部件,動力電池的性能對整車性能產(chǎn)生重要影響。動力電池的荷電狀態(tài)(State of Charge,SOC)是電動汽車的重要參數(shù)之一,反映了其剩余電量的多少。由于在電動汽車在行駛過程中電池的環(huán)境溫度對電池的影響很大,進(jìn)而影響到了電池SOC估計的精確度。因此,高效率地管理這些蓄電池,準(zhǔn)確預(yù)估實(shí)際運(yùn)行中的電池SOC,能更有效地進(jìn)行電池和整車管理,對預(yù)測電動車的剩余行駛里程以及電池組的使用和維護(hù)有著重要的意義。論文首先介紹了LiFePO_4電池在純電動汽車中的應(yīng)用背景,及其電化學(xué)原理和工作特性,分析了影響電池性能的多種因素。然后通過分析對比LiFePO_4電池的常見的傳統(tǒng)電池模型,選擇本文使用的電池模型。針對LiFePO_4電池的SOC估計受環(huán)境溫度影響較大這一現(xiàn)象,通過分析比對,基于Nernst電化學(xué)方程提出了一種新型的電池建模方法,將實(shí)驗(yàn)數(shù)據(jù)應(yīng)用于統(tǒng)計學(xué)方法試驗(yàn)設(shè)計(Design of Experiment,DOE),通過測量較少的數(shù)據(jù)得到較為精確的電池內(nèi)阻模型,模型中的其他參數(shù)能夠用連續(xù)變化的溫度、電池不同時刻SOC進(jìn)行擬合,從而實(shí)現(xiàn)整個電池模型的實(shí)時估計。最后,介紹了擴(kuò)展卡爾曼濾波(Extended Kalman Filter,EKF)算法,分析了基于改進(jìn)后的Nernst電池模型的LiFePO_4電池的狀態(tài)空間方程,在實(shí)驗(yàn)室條件下進(jìn)行不同溫度、不同工況的充放電實(shí)驗(yàn),并在MATLAB/Simulink模塊里搭建了LiFePO_4電池SOC估計的EKF模型進(jìn)行實(shí)驗(yàn)數(shù)據(jù)的仿真分析,實(shí)現(xiàn)對LiFePO_4電池SOC的動態(tài)估計,比較改進(jìn)后的電池模型與傳統(tǒng)電池模型在SOC估計時的誤差大小,結(jié)果表明改進(jìn)的Nernst電池模型可以獲得較高的SOC估計精度。
[Abstract]:LiFePO4) battery has been widely used in electric vehicles, communication tools, energy storage systems and other fields because of its advantages such as small size, long service life, high current discharge, no maintenance and so on.At present, as the power source of pure electric vehicle, LiFePO_4 battery is most often used in this kind of situation. As the core component of electric vehicle, the performance of power battery has an important impact on the performance of the whole vehicle.The state of charge state of electric vehicle (SOC) is one of the important parameters of electric vehicle, which reflects the amount of its remaining power.Because the ambient temperature of the battery has a great influence on the battery during the driving process of the electric vehicle, the accuracy of the battery SOC estimation is affected.Therefore, it is of great significance to manage these batteries efficiently and accurately predict the actual operation of SOCs, which can effectively manage the battery and the whole vehicle. It is of great significance to predict the remaining mileage of electric vehicle and the use and maintenance of battery pack.This paper first introduces the application background of LiFePO_4 battery in pure electric vehicle, and its electrochemical principle and working characteristics, and analyzes many factors that affect the performance of the battery.Then the conventional battery model of LiFePO_4 battery is analyzed and compared, and the battery model used in this paper is selected.In view of the fact that the SOC estimation of LiFePO_4 cells is greatly affected by ambient temperature, a new modeling method for LiFePO_4 cells is proposed based on the Nernst electrochemical equation.The experimental data were applied to the design of experimental design of experimental materials in statistical method. A more accurate model of battery internal resistance was obtained by measuring less data. The other parameters in the model could be fitted by continuously varying temperature and SOC at different time points of the battery.In order to realize the real-time estimation of the whole battery model.Finally, the extended Kalman filter extended Kalman filter (EKF) algorithm is introduced, and the state space equation of the LiFePO_4 battery based on the improved Nernst model is analyzed. The charging and discharging experiments at different temperatures and different working conditions are carried out in the laboratory.The EKF model of SOC estimation of LiFePO_4 battery is built in MATLAB/Simulink module to simulate and analyze the experimental data, and the dynamic estimation of SOC of LiFePO_4 battery is realized. The error between the improved model and the traditional model in SOC estimation is compared.The results show that the improved Nernst cell model can obtain high accuracy of SOC estimation.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM912

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