| 基于电动汽车磷酸铁锂动力电池荷电状态
估计方法研究 |
| |
| 引用本文： | 徐国卿,李卫民,梁嘉宁,Taimoor Zahi,张伟华,余兵.基于电动汽车磷酸铁锂动力电池荷电状态
估计方法研究[J].集成技术,2016,5(1):24-32. |
| |
| 作者姓名： | 徐国卿 李卫民 梁嘉宁 Taimoor Zahi 张伟华 余兵 |
| |
| 作者单位： | 1. 中国科学院深圳先进技术研究院 深圳518055;香港中文大学 香港999077;2. 中国科学院深圳先进技术研究院 深圳518055;济宁中科先进技术研究院 济宁272073;3. 中国科学院深圳先进技术研究院 深圳518055 |
| |
| 基金项目： | 广东省引进创新团队计划资助(201001D0104648280)，国家863计划课题(2013BAG02B00)，国家自然基金(51107142)，深圳基础研究计划(JCYJ20120617121836364 |
| |
| 摘 要： | 近几年,磷酸铁锂动力电池逐渐成为电动汽车动力电池首选.但是由于材料本身特性,使得磷酸铁锂电池的荷电状态难以精确估算.当电动汽车处于复杂工作环境时,荷电状态估计在保证电动汽车电池操作中的安全性和可靠性方面起到了至关重要的作用.文章采用戴维宁等效电路模型,验证无迹卡尔曼滤波和粒子滤波两种方法的估算效果,并分别与扩展卡尔曼滤波方法作对比,结果证明无迹卡尔曼滤波和粒子滤波都具有更好的估算精度.
|
| 关 键 词： | 电池管理系统 荷电状态 粒子滤波 无迹卡尔曼滤波 扩展卡尔曼滤波 |
Analysis of State of Charge Estimation Method Based on Lithium Iron
Phosphate Power Battery for Electric Vehicle |
| |
| Authors: | XU Guoqing LI Weimin LIANG Jianing ZAHID Taimoor ZHANG Weihua and YU Bing |
| |
| Abstract: | In recent years, lithium iron phosphate (LiFePO4) power battery is widely used for electric vehicle. However, it is difficult to estimate the state of charge(SOC) of battery because of the characteristics of material itself. In complicated operation environments, SOC estimation plays a significant role in ensuring safety and reliability of battery operations for an electric vehicle. In this paper, both unscented Kalman filter and Particle Filter methods of a LiFePO4 battery for applications in electric vehicles were verified using Thevenin equivalent circuit model. Compared with the extended Kalman filter method, results show that both unscented Kalman filter and particle filter have a better estimation accurancy. |
| |
| Keywords: | battery management system state of charge particle filter unscented Kalman filter extended Kalman filter |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《集成技术》浏览原始摘要信息 |
|
点击此处可从《集成技术》下载全文 |
