基于卡尔曼滤波的动力电池组SOC精确估计
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摘要
随着石油能源的短缺和大气污染的加剧,开发节能环保型电动汽车已经成为现今汽车工业领域发展的主要趋势。作为电动汽车的动力来源和能量载体,电池自身制造工艺以及成组应用技术已成为推动电动汽车商业化的关键因数。因此,为了保证动力电池能够安全有效的工作,电动汽车必须配置特定的电池管理系统对动力电池组的状态进行控制和管理。电池剩余电量SOC估计一直是电池管理系统的核心,是反映电池运作状态的主要参数,为整车控制策略提供判断依据。本文以磷酸铁锂聚合物动力电池为研究对象,采用卡尔曼滤波修正算法对动力电池组进行SOC估计。
本文首先介绍了电动汽车的发展现状和车用动力电池的性能要求,以磷酸铁锂电池的电化学特性为出发点,分析了电池SOC的各种内外影响因素及在线估算难点。从SOC的定义出发,通过比较几种常用SOC估计方法,并结合车用动力电池的应用环境,本文提出了卡尔曼滤波修正算法。该算法充分发挥了开路电压法、安时计量法和扩展卡尔曼滤波法的优点,从而使得SOC的估算精度和实时性有了很大的提高。根据放电实验数据,系统建立了与算法相关的充放电倍率SOC模型、温度SOC模型、开路电压SOC模型和扩展卡尔曼滤波复合模型。
然后在此基础上,系统分别从软硬件角度建立了动力电池组SOC估计系统。该系统是由数据采样、算法执行、通讯管理、保护控制、信息存储及数据显示等模块组成,完成了电池电压、充放电电流、温度的在线检测、卡尔曼滤波修正算法执行、单片机SPI及串口通讯、LCD在线显示、电池状态诊断及保护等功能,从而在真正意义上实现了车用动力电池组剩余电量的实时在线精确估计。
最后制定电池组实验的充放电方案,使用汽车行驶工况HWFET、UDDS、FUDS来对电池组SOC估计算法进行检验和优化。经过工况测试和Matlab分析比较,本文提出的卡尔曼滤波修正算法具有很好的实际估计效果,完全符合电动汽车对电池组SOC估计的准确性要求。
Because of oil resources shortage and air quality degradation, electric vehicle with advantages of energy saving and environment protection has emerged as the main trend in automobile industry. As the major energy carrier and power source, battery’s manufacturing process and group application technology have been the key factors in promoting the commercial progress of electric vehicle. Therefore, in order to keep power battery work securely and effectively, electric vehicle must be equipped with a specific management system to control and supervise the function of battery. The state of charge SOC estimation has always been the core component in battery management system, which is one of the main parameters to reflect the battery working states and can provide judgment basis to vehicle control strategy. This paper takes the LiFeO4 polymer power battery as the research object, and uses Kalman filter correction algorithm for battery pack online SOC estimation.
Firstly, the paper describes the development of electric vehicle and the performance requirements of vehicular power battery, and it takes the electrochemical characteristics of LiFeO4 battery as a starting point to analyze the various SOC effect factors and study the difficulties of online accurate estimation. After comparing some commonly used methods and considering the electric vehicle environment, this paper proposes a new method named Kalman filter correction algorithm on the basis of SOC definition. The algorithm gives such full play to the advantages of open circuit voltage method, ampere hour counting method and extended Kalman fitering algorithm that makes the estimation accuracy and real-time ability improved significantly. According to discharging experimental data, the system has established discharging or charging rate model, temperature model, open circuit voltage model and extended Kalman filtering combined model.
Secondly, the paper builds up the power battery pack’s SOC estimation system in view of software and hardware design. This system is made up of data sampling, algorithm execution, communication management, protection control, information storage and display. It possesses so many functions that can take an online accurate estimation on vehicular battery pack in a real sense, including battery data online detection, Kalman filtering correction algorithm implementation, microcontroller communication, liquid crystal display, battery diagnosis and protection.
Finally, the paper designs the battery pack’s charging and discharge experiments. With the help of automobile driving cycle, such as HWFET, UDDS, FUDS, the performance of SOC estimation algorithm has been verified and improved. Consequently, with a good estimation effect, Kalman filtering correction algorithm can be completely in conformity with the SOC accuracy requirements in electric vehicle environment.
本文首先介绍了电动汽车的发展现状和车用动力电池的性能要求,以磷酸铁锂电池的电化学特性为出发点,分析了电池SOC的各种内外影响因素及在线估算难点。从SOC的定义出发,通过比较几种常用SOC估计方法,并结合车用动力电池的应用环境,本文提出了卡尔曼滤波修正算法。该算法充分发挥了开路电压法、安时计量法和扩展卡尔曼滤波法的优点,从而使得SOC的估算精度和实时性有了很大的提高。根据放电实验数据,系统建立了与算法相关的充放电倍率SOC模型、温度SOC模型、开路电压SOC模型和扩展卡尔曼滤波复合模型。
然后在此基础上,系统分别从软硬件角度建立了动力电池组SOC估计系统。该系统是由数据采样、算法执行、通讯管理、保护控制、信息存储及数据显示等模块组成,完成了电池电压、充放电电流、温度的在线检测、卡尔曼滤波修正算法执行、单片机SPI及串口通讯、LCD在线显示、电池状态诊断及保护等功能,从而在真正意义上实现了车用动力电池组剩余电量的实时在线精确估计。
最后制定电池组实验的充放电方案,使用汽车行驶工况HWFET、UDDS、FUDS来对电池组SOC估计算法进行检验和优化。经过工况测试和Matlab分析比较,本文提出的卡尔曼滤波修正算法具有很好的实际估计效果,完全符合电动汽车对电池组SOC估计的准确性要求。
Because of oil resources shortage and air quality degradation, electric vehicle with advantages of energy saving and environment protection has emerged as the main trend in automobile industry. As the major energy carrier and power source, battery’s manufacturing process and group application technology have been the key factors in promoting the commercial progress of electric vehicle. Therefore, in order to keep power battery work securely and effectively, electric vehicle must be equipped with a specific management system to control and supervise the function of battery. The state of charge SOC estimation has always been the core component in battery management system, which is one of the main parameters to reflect the battery working states and can provide judgment basis to vehicle control strategy. This paper takes the LiFeO4 polymer power battery as the research object, and uses Kalman filter correction algorithm for battery pack online SOC estimation.
Firstly, the paper describes the development of electric vehicle and the performance requirements of vehicular power battery, and it takes the electrochemical characteristics of LiFeO4 battery as a starting point to analyze the various SOC effect factors and study the difficulties of online accurate estimation. After comparing some commonly used methods and considering the electric vehicle environment, this paper proposes a new method named Kalman filter correction algorithm on the basis of SOC definition. The algorithm gives such full play to the advantages of open circuit voltage method, ampere hour counting method and extended Kalman fitering algorithm that makes the estimation accuracy and real-time ability improved significantly. According to discharging experimental data, the system has established discharging or charging rate model, temperature model, open circuit voltage model and extended Kalman filtering combined model.
Secondly, the paper builds up the power battery pack’s SOC estimation system in view of software and hardware design. This system is made up of data sampling, algorithm execution, communication management, protection control, information storage and display. It possesses so many functions that can take an online accurate estimation on vehicular battery pack in a real sense, including battery data online detection, Kalman filtering correction algorithm implementation, microcontroller communication, liquid crystal display, battery diagnosis and protection.
Finally, the paper designs the battery pack’s charging and discharge experiments. With the help of automobile driving cycle, such as HWFET, UDDS, FUDS, the performance of SOC estimation algorithm has been verified and improved. Consequently, with a good estimation effect, Kalman filtering correction algorithm can be completely in conformity with the SOC accuracy requirements in electric vehicle environment.
引文
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[6]肖婷.车用动力镍氢电池SOC建模与仿真[D] .武汉理工大学硕士学位论文,2008:13-21.
[7]焦慧敏.电动汽车动力电池剩余电量的预测方法及其实现[D] .湖南大学硕士学位论文,2006:24-45.
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