隨著世界能源消費(fèi)的快速增長(zhǎng),防止大氣污染和生態(tài)友好發(fā)展逐漸引起各國(guó)政府的關(guān)注。電動(dòng)汽車(chē)在減少環(huán)境污染和預(yù)防能源危機(jī)方面具有巨大的潛力,盡管電動(dòng)汽車(chē)行業(yè)得到了各國(guó)政府的支持,但其技術(shù)發(fā)展仍有一些亟待解決的問(wèn)題。目前,電池作為電動(dòng)汽車(chē)的關(guān)鍵部件也是其瓶頸。電動(dòng)汽車(chē)動(dòng)力電池由電池管理系（Battery management system,BMS）控制,為了保證電動(dòng)汽車(chē)的節(jié)能高效運(yùn)行,防止蓄電池深度放電或過(guò)充電,準(zhǔn)確估計(jì)剩余里程,延長(zhǎng)使用壽命,防止蓄電池的漸進(jìn)性永久性損壞,最大限度地提高蓄電池性能,BMS必須具有準(zhǔn)確的蓄電池荷電狀態(tài)值。此外,為了提高操作的可靠性,并警告駕駛員將來(lái)更換電池,BMS需要電池的健康狀態(tài)值。在實(shí)際應(yīng)用中,電池的工作狀態(tài)、溫度、老化等因素都將非線性引入狀態(tài)預(yù)測(cè)任務(wù)中,使得狀態(tài)預(yù)測(cè)的準(zhǔn)確性變得十分困難。首先,本文分析了鋰電池的工作原理、結(jié)構(gòu)和主要特點(diǎn)。然后,考慮到鋰電池的化學(xué)特性,在對(duì)現(xiàn)有電池模型進(jìn)行比較分析的基礎(chǔ)上,建立了電池2RC等效電路模型。采用Levenberg-Marquardt（LM）最小二乘誤差優(yōu)化算法對(duì)不同環(huán)境溫度下的等效電路模型參數(shù)進(jìn)行了估計(jì)。在放電脈沖... 

【文章來(lái)源】：蘭州交通大學(xué)甘肅省

【文章頁(yè)數(shù)】：64 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
摘要
Abstract
1 Introduction
    1.1 Background and significance of study
        1.1.1 Study background
        1.1.2 Study significance
    1.2 Topic research status in China and other contries
        1.2.1 Battery modeling research status
        1.2.2 SOC prediction research status
        1.2.3 SOH prediction research status
    1.3 The main contents of research
2 Battery modeling and parameter estimation
    2.1 Principles and characteristics of lithium batteries
        2.1.1 Battery technology comparison
        2.1.2 Battery structure and working principle
        2.1.3 Main technical parameters of battery
    2.2 Brief introduction to battery models
        2.2.1 Existing equivalent circuit models analysis
        2.2.2 Battery model selection
    2.3 Model parameters estimation and validation
        2.3.1 Levenberg-Marquardt parameter estimation method
        2.3.2 Parameter estimation results
    2.4 Chapter short summary
3 Battery SOC prediction based on AEKF
    3.1 The basic principles of Kalman filter
    3.2 SOC estimation algorithm based on EKF
    3.3 SOC estimation algorithm based on AEKF
        3.3.1 Application of AEKF
        3.3.2 Introduction of battery SOC testing
    3.4 Simulation analysis
    3.5 Chapter short summary
4 SOH prediction
    4.1 Battery State of Health definition and influencing factors
    4.2 SOH estimation based on Kalman filtering algorithm
        4.2.1 Ampere-hour integration method
        4.2.2 The Kalman filter design
    4.3 Simulation analysis
    4.4 Chapter short summary
Conclusion
Acknowledgements
References
The results of research during the obtaining degree



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