本文選題：電池SOC　切入點：主元分析　出處：《哈爾濱理工大學》2014年碩士論文

【摘要】：近年來，隨著人們對環(huán)保和能源問題重視程度的日益提高，促使了以電動汽車為主流的節(jié)能減排汽車行業(yè)的興起，而作為新興的電動汽車能量的來源——鋰離子動力電池的研究也就受到了更多的重視。 電池SOC作為電動汽車動力電池的重要參數(shù)，它的準確估算關乎整車的控制策略是否能夠得以正確實施，因此，電池SOC的估算研究也就成為了廣大學者的重要研究課題。由于現(xiàn)有的估算方法往往很難實現(xiàn)對電池SOC的準確估算，因此，如何準確的估算電池SOC已經成為人們關注的焦點。主元分析算法作為多元統(tǒng)計分析的方法，能夠進行數(shù)據(jù)的簡化和數(shù)據(jù)壓縮并提取變量中的重要因子，進而進行數(shù)據(jù)的分析和結果預測，因此，利用該算法對電池SOC建立估算模型具有很強的理論意義和實際意義。 本文通過測試實驗對影響電池SOC的性能參數(shù)進行了分析，得出影響電池SOC的關鍵因素。然后，通過對主元分析理論的深入剖析，分析了該算法對于電池SOC估算的可行性，提出了利用主元分析算法結合最小二乘回歸法建立電池SOC的估算模型，并通過模型的仿真與實驗對該模型進行了實驗驗證。鑒于PCA算法不能提取參數(shù)中的非線性因子而造成電池SOC的估算誤差，進一步提出了采用核主元分析算法建立電池SOC估算模型，并通過仿真實驗對所建立的模型進行了驗證。 針對核主元分析電池SOC估算模型不能適應溫度多變或者電池劣化程度不同的情況，基于電池工作溫度和基于電池劣化程度兩種方式對模型進行了改進。改進后的電池SOC估算模型通過實驗進行了模型的仿真與實驗驗證。仿真結果表明，改進后的模型能夠適應更復雜的環(huán)境，滿足實時性和可靠性的要求，估算精度有所提高，平均估算誤差為1.46%，，優(yōu)于安時計量法電池SOC估算模型，具有更好的電池SOC估算效果。
[Abstract]:In recent years, with the increasing attention paid to environmental protection and energy issues, the emergence of energy-saving and emission reduction automobile industry, which is the mainstream of electric vehicles, has been promoted. As a new energy source of electric vehicle, lithium ion battery has been paid more attention. As an important parameter of electric vehicle power battery, the accurate estimation of battery SOC is related to whether the control strategy of the whole vehicle can be implemented correctly. The estimation of battery SOC has become an important research topic for many scholars. Because the existing estimation methods are often difficult to realize the accurate estimation of battery SOC, therefore, How to estimate battery SOC accurately has become the focus of attention. As a method of multivariate statistical analysis, principal component analysis (PCA) algorithm can simplify and compress data and extract important factors from variables. Then the data are analyzed and the results are predicted. Therefore, it is of great theoretical and practical significance to establish the estimation model of battery SOC by using this algorithm. In this paper, the performance parameters of battery SOC are analyzed through testing experiments, and the key factors affecting battery SOC are obtained. Then, the feasibility of the algorithm for estimating battery SOC is analyzed through the in-depth analysis of principal component analysis theory. The estimation model of battery SOC based on principal component analysis (PCA) algorithm and least square regression method is proposed. The model is verified by simulation and experiment. Because the PCA algorithm can not extract the nonlinear factor in the parameters, the estimation error of SOC is caused. Furthermore, the core principal component analysis (KPCA) algorithm is used to establish the battery SOC estimation model, and the model is verified by simulation experiments. The SOC estimation model of nuclear principal component analysis battery can not adapt to the situation of variable temperature or different degradation degree of the cell. The model is improved based on the operating temperature of the battery and the degradation degree of the battery. The improved SOC estimation model of the battery is simulated and verified by experiments. The simulation results show that, The improved model can adapt to more complex environment and meet the requirements of real-time and reliability. The estimation accuracy is improved, and the average estimation error is 1.46, which is superior to the amperometric battery SOC estimation model and has better effect of battery SOC estimation.
【學位授予單位】：哈爾濱理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM912

【參考文獻】

相關期刊論文 前10條

1 王惠文;王R

本文編號：1684192