電動(dòng)汽車以其零溫室氣體排放和高效率的優(yōu)點(diǎn),越來(lái)越受到人們的關(guān)注和興趣。電池組是電動(dòng)汽車的主要儲(chǔ)能方式。嚴(yán)格的電池組管理是保證電池組在各種負(fù)載和行駛狀態(tài)下的安全和性能的關(guān)鍵。因此,需要一個(gè)有效的電池管理系統(tǒng),該系統(tǒng)能夠進(jìn)行電池荷電狀態(tài)(SOC)的估計(jì)、電池剩余使用壽命(RUL)的預(yù)測(cè)、電池單元的平衡和溫度的控制。電動(dòng)汽車用鋰離子電池(LIBs)受多種因素的影響,電池不平衡是其中的關(guān)鍵問(wèn)題。當(dāng)電池組中的電池出現(xiàn)不平衡時(shí),單個(gè)電池的電壓會(huì)隨著時(shí)間的推移而不同,這會(huì)導(dǎo)致電池快速老化,進(jìn)而引起電動(dòng)汽車最終失效,并可能導(dǎo)致災(zāi)難發(fā)生。適當(dāng)?shù)碾姵仄胶夥椒▽?duì)電池壽命的保持起著重要的作用,并適當(dāng)?shù)匮娱L(zhǎng)電池的使用壽命,使鋰離子電池在電動(dòng)汽車中的使用效率更高。本論文主要從三個(gè)不同的角度研究電池平衡優(yōu)化,以提高電池的效率和安全性。首先,針對(duì)電動(dòng)汽車鋰離子電池健康管理系統(tǒng)(BMS)中存在的問(wèn)題,從優(yōu)化電池性能和電池壽命周期,提高電池安全性的角度,提出了基于粒子濾波的電池剩余使用壽命(RUL)的精確預(yù)測(cè)方法,對(duì)于BMS的預(yù)測(cè)和健康管理具有重要意義。其次,分析了基于MATLAB/Simulink的各種電池平衡方案和拓... 

【文章來(lái)源】：北京科技大學(xué)北京市 211工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：195 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
ACKNOWLEDGEMENT
摘要
Abstract
縮寫和符號(hào)清單
術(shù)語(yǔ)表
1 Introduction
    1.1 Background and motivation
    1.2 Problem statement
    1.3 Objective
    1.4 Research questions
    1.5 Significance of study
    1.6 Thesis organization
2 Literature review
    2.1 Health management systems for batteries
        2.1.1 Battery Terminologies
        2.1.2 BMS architecture
    2.2 Stages of performing battery management
        2.2.1 Condition monitoring
        2.2.2 Hazard protection
        2.2.3 Charge and discharge management
        2.2.4 Diagnosis
        2.2.5 Data management and assessment
    2.3 Issues of BMS
        2.3.1 Diversity of battery management applications
        2.3.2 Handling of potential, but unprecedented hazards
        2.3.3 Lack of safe operating areas for specific battery cells
        2.3.4 Ensuring an efficient operational state of the peripheral control unitsand the power converters
    2.4 Prognostic methods
        2.4.1 Physical methods
        2.4.2 Data-based methods
        2.4.3 Hybrid methods
    2.5 Battery management system framework
    2.6 Opportunities and challenges on prognosis of LIB health
        2.6.1 Technological aspects
        2.6.2 Cost aspects
        2.6.3 Security aspects
        2.6.4 Environmental aspects
        2.6.5 Future research agenda
    2.7 Chapter summary
3 Remaining useful life prediction of electric vehicle lithium-ion battery based onthe particle filter method
    3.1 Battery prognostics
    3.2 Particle filtering
    3.3 Experimental data
    3.4 Prediction based on particle filter method
    3.5 Chapter summary
4 Battery cell balancing methodologies for optimizing battery pack performance inelectric vehicles
    4.1 Battery model
    4.2 Battery cell balancing
        4.2.1 Cell balancing schemes
        4.2.2 Types of battery cell imbalance that affect charge/discharge voltage
        4.2.3 Effects of battery cell imbalance on performance
        4.2.4 Importance of cell balancing
    4.3 LIBs cell balancing model/algorithm
        4.3.1 Model assumptions
        4.3.2 Model requirements
        4.3.3 Model validation
        4.3.4 Research framework
    4.4 Experimental results for battery pack health analysis
    4.5 Chapter summary
5 Parameter identification and state estimation of lithium-ion batteries for electricvehicles with vibration and temperature dynamics
    5.1 Lithium-ion battery
        5.1.1 Modeling
        5.1.2 Problem statement
    5.2 Parameter identification
    5.3 Effects of vibration and temperature on battery state
        5.3.1 Vibration
        5.3.2 Temperature
        5.3.3 SOC estimation
        5.3.4 SOH estimation
        5.3.5 State estimation based on double extended Kalman filter
    5.4 Experimental test system
        5.4.1 Experimental set-up
        5.4.2 Experimental procedures
        5.4.3 Results and discussions
        5.4.4 Future application of DEKF algorithm to address challenges ofbattery state estimation
    5.5 Future research directions and discussions
    5.6 Chapter summary
6 Lithium-ion battery's SOC estimation for Electric vehicles based on comparisonsof KF and EKF algorithms
    6.1 Battery model
        6.1.1 LIB modeling
        6.1.2 State-of-Charge
        6.1.3 Sensors bias modeling
    6.2 Kalman-based filtering algorithms for SOC estimation
        6.2.1 KF algorithm
        6.2.2 EKF algorithm
    6.3 Results and analysis
        6.3.1 Results
        6.3.2 Experimental validation and results
    6.4 Chapter summary
7 Research conclusion and recommendations for future research
    7.1 Conclusions
    7.2 Research contributions and novelty
    7.3 Recommendations for future research
參考文獻(xiàn)
作者簡(jiǎn)歷及在學(xué)研究成果
學(xué)位論文數(shù)據(jù)集



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