本文選題：SOC + 三階RC等效電路模型��； 參考：《桂林電子科技大學(xué)》2014年碩士論文

【摘要】：電荷狀態(tài)(State of Charge，SOC)是電池管理系統(tǒng)(BMS)中的重要參數(shù)，準(zhǔn)確估算SOC，可保證電池維持在合理的電壓范圍內(nèi)，防止由于過充或深放對(duì)電池的損傷，延長電池壽命。 該文建立了三階RC等效電路模型，通過雙卡爾曼濾波(DKF)方法在線辨識(shí)電池模型所有參數(shù)，全面準(zhǔn)確地反映電池的動(dòng)態(tài)特性，并在MATLAB/Simulink環(huán)境下搭建了仿真模型以驗(yàn)證其有效性。 針對(duì)狀態(tài)變量較多、耦合性較強(qiáng)、噪聲隨機(jī)性強(qiáng)和可能出現(xiàn)野值的電池系統(tǒng)，運(yùn)用疊加原理，分解測量方程，將狀態(tài)變量分開估算，消弱了它們之間的耦合關(guān)系，提出了一種基于擴(kuò)展卡爾曼濾波(EKF)的新型SOC估算方法，即New-EKF算法。在所設(shè)計(jì)的DKF基礎(chǔ)上，運(yùn)用EKF與所提出的方法在不同電流工況下對(duì)SOC進(jìn)行估算，其結(jié)果表明EKF在恒流工況下，SOC估算精度較好，但在變電流工況下，其SOC的估算效果較差，甚至發(fā)散，而所提出的新型SOC估算方法在不同電流工況下，SOC估算精度較高，特別適合用于電流劇烈變化的電動(dòng)汽車用動(dòng)力電池的SOC估算中，同時(shí)驗(yàn)證了所建立電池模型、DKF參數(shù)辨識(shí)方法及所提出的SOC估算方法的有效性和可行性。 為了進(jìn)一步提高SOC估算精度，運(yùn)用EKF估算SOC時(shí)，加入了自適應(yīng)濾波方法，即自適應(yīng)擴(kuò)展卡爾曼濾波（AEKF）算法，，同時(shí)在使用DKF在線辨識(shí)模型參數(shù)過程中，為了防止由計(jì)算舍入誤差的影響，導(dǎo)致估算誤差矩陣失去非負(fù)定性所產(chǎn)生的濾波發(fā)散現(xiàn)象，將UD分解方法用于濾波時(shí)間更新和狀態(tài)更新中，增強(qiáng)了算法穩(wěn)定性，降低了計(jì)算復(fù)雜度，所提出方法稱為UD-DKF方法�；赨D-DKF，運(yùn)用AEKF算法在不同電流工況下對(duì)SOC進(jìn)行估算，實(shí)驗(yàn)結(jié)果表明AEKF算法能準(zhǔn)確估算SOC，即使在不同SOC初值誤差下，也能較快地收斂到真值，具有良好的魯棒性和收斂性，對(duì)噪聲有較強(qiáng)的抑制作用。
[Abstract]:The charge state (State of Charge, SOC) is an important parameter in the battery management system (BMS). The accurate estimation of SOC can ensure that the battery is maintained in a reasonable range of voltage and prevents the battery from overcharging or deeply damaging the battery and prolonging the battery life.
The three order RC equivalent circuit model is established in this paper. The two Calman filter (DKF) method is used to identify all the parameters of the battery model online. The dynamic characteristics of the battery are fully and accurately reflected, and a simulation model is built in the MATLAB/Simulink environment to verify its effectiveness.
For the battery system with more state variables, stronger coupling, strong noise randomness and possible field value, the superposition principle is used to decompose measurement equations, to estimate the state variables separately and to weaken the coupling relationship between them. A new SOC estimation method based on extended Calman filter (EKF), that is, New-EKF algorithm, is put forward. On the basis of DKF, EKF and the proposed method are used to estimate the SOC under different current conditions. The results show that the SOC estimation precision is better under the constant current condition, but the estimation effect of SOC is poor and even diverges under the condition of variable current, and the proposed new SOC estimation method has a higher estimation precision of SOC under different current conditions. It is particularly suitable for the SOC estimation of power batteries for electric vehicles with intense current changes, and the validity and feasibility of the established battery model, the DKF parameter identification method and the proposed SOC estimation method are also verified.
In order to further improve the accuracy of SOC estimation, the adaptive filtering method, adaptive extended Calman filter (AEKF) algorithm is added to the estimation of SOC by EKF, and in the process of identifying the model parameters in the DKF online, in order to prevent the influence of the calculation rounding error, the estimation error matrix loses the filtering divergence produced by non negative qualitative analysis. The UD decomposition method is used in filtering time updating and state updating, which enhances the stability of the algorithm and reduces the computational complexity. The proposed method is called the UD-DKF method. Based on UD-DKF, the AEKF algorithm is used to estimate the SOC under different current conditions. The experimental results show that the AEKF algorithm can accurately estimate SOC, even if the initial value of SOC is mistaken. It also converges to the true value quickly, and has good robustness and convergence, and has a strong inhibitory effect on noise.

【學(xué)位授予單位】：桂林電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM912

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