电池管理系统中电池平衡性能优化建模方法
发布时间：2020-12-26 23:00
电动汽车以其零温室气体排放和高效率的优点,越来越受到人们的关注和兴趣。电池组是电动汽车的主要储能方式。严格的电池组管理是保证电池组在各种负载和行驶状态下的安全和性能的关键。因此,需要一个有效的电池管理系统,该系统能够进行电池荷电状态(SOC)的估计、电池剩余使用寿命(RUL)的预测、电池单元的平衡和温度的控制。电动汽车用锂离子电池(LIBs)受多种因素的影响,电池不平衡是其中的关键问题。当电池组中的电池出现不平衡时,单个电池的电压会随着时间的推移而不同,这会导致电池快速老化,进而引起电动汽车最终失效,并可能导致灾难发生。适当的电池平衡方法对电池寿命的保持起着重要的作用,并适当地延长电池的使用寿命,使锂离子电池在电动汽车中的使用效率更高。本论文主要从三个不同的角度研究电池平衡优化,以提高电池的效率和安全性。首先,针对电动汽车锂离子电池健康管理系统(BMS)中存在的问题,从优化电池性能和电池寿命周期,提高电池安全性的角度,提出了基于粒子滤波的电池剩余使用寿命(RUL)的精确预测方法,对于BMS的预测和健康管理具有重要意义。其次,分析了基于MATLAB/Simulink的各种电池平衡方案和拓...
【文章来源】：北京科技大学北京市 211工程院校 教育部直属院校
【文章页数】：195 页
【学位级别】：博士
【文章目录】：
ACKNOWLEDGEMENT
摘要
Abstract
缩写和符号清单
术语表
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
参考文献
作者简历及在学研究成果
学位论文数据集
本文编号：2940599
【文章来源】：北京科技大学北京市 211工程院校 教育部直属院校
【文章页数】：195 页
【学位级别】：博士
【文章目录】：
ACKNOWLEDGEMENT
摘要
Abstract
缩写和符号清单
术语表
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
参考文献
作者简历及在学研究成果
学位论文数据集
本文编号：2940599
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