基于模糊卡尔曼滤波的锂电池荷电状态和健康状态预测
发布时间：2021-07-12 00:47
随着世界能源消费的快速增长,防止大气污染和生态友好发展逐渐引起各国政府的关注。电动汽车在减少环境污染和预防能源危机方面具有巨大的潜力,尽管电动汽车行业得到了各国政府的支持,但其技术发展仍有一些亟待解决的问题。目前,电池作为电动汽车的关键部件也是其瓶颈。电动汽车动力电池由电池管理系（Battery management system,BMS）控制,为了保证电动汽车的节能高效运行,防止蓄电池深度放电或过充电,准确估计剩余里程,延长使用寿命,防止蓄电池的渐进性永久性损坏,最大限度地提高蓄电池性能,BMS必须具有准确的蓄电池荷电状态值。此外,为了提高操作的可靠性,并警告驾驶员将来更换电池,BMS需要电池的健康状态值。在实际应用中,电池的工作状态、温度、老化等因素都将非线性引入状态预测任务中,使得状态预测的准确性变得十分困难。首先,本文分析了锂电池的工作原理、结构和主要特点。然后,考虑到锂电池的化学特性,在对现有电池模型进行比较分析的基础上,建立了电池2RC等效电路模型。采用Levenberg-Marquardt（LM）最小二乘误差优化算法对不同环境温度下的等效电路模型参数进行了估计。在放电脉冲...
【文章来源】：兰州交通大学甘肃省
【文章页数】：64 页
【学位级别】：硕士
【文章目录】：
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
本文编号：3278844
【文章来源】：兰州交通大学甘肃省
【文章页数】：64 页
【学位级别】：硕士
【文章目录】：
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
本文编号：3278844
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