本文選題：遞推最小二乘法 + 遺傳粒子濾波　； 參考：《河南師范大學(xué)》2017年碩士論文

【摘要】：隨著油價(jià)的上升、PM2.5不斷地爆表給傳統(tǒng)汽車生產(chǎn)企業(yè)帶來(lái)不小的壓力,也迫使這些企業(yè)朝著更具發(fā)展前景的新能源汽車方向轉(zhuǎn)型。這些新能源汽車大多以電池組供能為主,結(jié)合電池管理系統(tǒng)來(lái)滿足人們的日常行駛需求。在汽車領(lǐng)域,新能源汽車目前還處于剛起步階段,人們將大部分研究重心放在對(duì)電池剩余電量(SOC)的精確研究上,而對(duì)電池的健康狀況涉及的較少。目前,電池自燃、爆炸等現(xiàn)象頻頻出現(xiàn),電池的健康成為了人們關(guān)注的對(duì)象,促使著人們將重心開(kāi)始朝向電池健康狀況(SOH)偏移。電池的健康狀況已經(jīng)成為電池管理中重要的一個(gè)環(huán)節(jié),如何準(zhǔn)確的預(yù)測(cè)SOH對(duì)新能源汽車的發(fā)展具有重要的意義。本文就是以此為背景對(duì)電池的健康狀態(tài)進(jìn)行研究,具體工作如下:首先對(duì)鋰電池壽命衰減分析并參照現(xiàn)有的相關(guān)文獻(xiàn),發(fā)現(xiàn)電池中歐姆內(nèi)阻阻值的大小可以作為SOH的評(píng)判準(zhǔn)則。然后對(duì)現(xiàn)有的三類模型進(jìn)行對(duì)比分析,最終選擇了二階RC電池等效電路為模型。最后,選取一節(jié)電池并測(cè)取該節(jié)的數(shù)據(jù),通過(guò)遞推最小二乘法進(jìn)行參數(shù)辨識(shí),將辨識(shí)的值代入求解來(lái)確定該電池相對(duì)應(yīng)的模型。通過(guò)對(duì)電池等效電路分析得出該模型是一個(gè)非線性系統(tǒng),因此提出了適合處理非線性、非高斯系統(tǒng)的粒子濾波算法。本文通過(guò)序貫重要性采樣(SIS)在高維函數(shù)采樣的思想,將時(shí)間函數(shù)替代高維函數(shù)引出粒子濾波這一算法。然而粒子濾波在處理問(wèn)題時(shí)會(huì)隨著迭代次數(shù)的增加而出現(xiàn)粒子消失的退化現(xiàn)象,會(huì)嚴(yán)重影響到預(yù)測(cè)的精度。為此本文在原有粒子濾波的基礎(chǔ)上又引入了重采樣的概念。其思想是當(dāng)有效粒子數(shù)低于設(shè)定的閾值時(shí),將所有粒子進(jìn)行等權(quán)值重新分配。將分配的粒子繼續(xù)代入循環(huán)中,直到運(yùn)行到規(guī)定的迭代次數(shù)后結(jié)束。根據(jù)等效電路模型建立動(dòng)態(tài)方程與觀測(cè)方程,并列出具體的操作步驟。根據(jù)所列的操作步驟進(jìn)行編程并通過(guò)MATLAB對(duì)其仿真。通過(guò)對(duì)仿真圖形的觀察,發(fā)現(xiàn)重采樣粒子濾波對(duì)預(yù)測(cè)電池內(nèi)阻具有較高的精確度。為了更加精確的估算電池內(nèi)阻,本文提出了將遺傳算法中進(jìn)化思想來(lái)代替重采樣過(guò)程。該算法不僅解決了粒子退化的問(wèn)題而且運(yùn)用基因重組、基因突變的方式來(lái)豐富了粒子的種類。其思想是當(dāng)有效粒子數(shù)低于設(shè)定的閾值時(shí),將所有粒子采用遺傳算法的方式進(jìn)行處理。將處理過(guò)的粒子繼續(xù)代入循環(huán)中,直到運(yùn)行到規(guī)定的迭代次數(shù)后停止。最后,建立動(dòng)態(tài)方程與觀測(cè)方程,列出關(guān)于遺傳粒子濾波具體的操作步驟。根據(jù)所列的操作步驟進(jìn)行編程并通過(guò)MATLAB對(duì)其仿真。將上述兩種算法仿真出來(lái)的圖形進(jìn)行觀察比較,發(fā)現(xiàn)采用遺傳粒子濾波預(yù)測(cè)的曲線更為平緩且抖動(dòng)更小。因此表明遺傳粒子濾波對(duì)估算內(nèi)阻時(shí)具有更優(yōu)越的性能。
[Abstract]:As oil prices rise, PM2.5 keeps popping up, putting pressure on traditional auto makers and forcing them to shift towards more promising new energy vehicles. Most of these new energy vehicles are powered by battery pack and combined with battery management system to meet people's daily driving needs. In the automotive field, the new energy vehicle is still in its infancy. Most of the researches focus on the accurate study of the battery residual charge (SOC), but less on the battery health. At present, the phenomena of battery spontaneous combustion and explosion appear frequently, and the health of battery becomes the object of concern, which urges people to shift the center of gravity towards the battery health condition (SOH). Battery health has become an important link in battery management. How to accurately predict SOH is of great significance to the development of new energy vehicles. In this paper, the health status of the battery is studied in this paper. The main work is as follows: firstly, the life attenuation of lithium battery is analyzed, and referring to the existing literature, it is found that the ohmic resistance in the battery can be regarded as the criterion of SOH. Then the three kinds of models are compared and the second order RC battery equivalent circuit is selected as the model. Finally, a battery is selected and the data of the section is measured. The parameters are identified by recursive least square method, and the corresponding model of the battery is determined by substituting the identified value into the solution. Through the analysis of the equivalent circuit of the battery, the model is a nonlinear system. Therefore, a particle filter algorithm suitable for dealing with nonlinear and non-Gao Si systems is proposed. Based on the idea of sequential importance sampling (SIS) sampling in high dimensional function, the particle filter algorithm is derived by replacing the time function with the high dimensional function. However, particle filter will degenerate with the increase of iteration times, which will seriously affect the accuracy of prediction. In this paper, the concept of resampling is introduced based on the original particle filter. The idea is to redistribute all particles with equal weights when the number of effective particles is lower than the set threshold. Continue the allocated particles into the loop until the end of the specified number of iterations. According to the equivalent circuit model, the dynamic equation and the observation equation are established, and the concrete operation steps are listed. According to the listed operation steps to program and through MATLAB to its simulation. By observing the simulation figure, it is found that the resampling particle filter has a high accuracy in predicting the internal resistance of the battery. In order to estimate the internal resistance of the battery more accurately, the evolutionary idea of genetic algorithm is proposed to replace the resampling process. The algorithm not only solves the problem of particle degradation, but also enriches the species of particles by gene recombination and gene mutation. The idea is that all particles are processed by genetic algorithm when the number of effective particles is lower than the set threshold. The processed particles continue to be inserted into the loop until they have stopped running at the specified number of iterations. Finally, the dynamic equation and observation equation are established, and the operation steps of genetic particle filter are listed. According to the listed operation steps to program and through MATLAB to its simulation. By observing and comparing the figures simulated by the above two algorithms, it is found that the curve predicted by genetic particle filter is more gentle and the jitter is smaller. Therefore, genetic particle filter has better performance in estimating internal resistance.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM912

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