机械工业出版社: 动力电池管理系统核心算法(第一版)
发表时间:2018-11-01     阅读次数:     字体:【

简介:《动力电池管理系统核心算法》结合作者十多年来的研究实践,阐述了动力电池管理系统的特点与技术难题,针对新能源汽车应用,详细阐述了动力电池系统实验设计、动态建模、荷电状态估计、健康状态估计、峰值功率预测、剩余寿命预测、低温快速加热与优化充电以及相应核心算法的工程应用和实践问题,并配有详细的算法实践步骤和开发流程,可作为相关领域技术人员的参考用书,也可以作为汽车专业的高年级本科生和研究生的专业课教科书。

主要内容如下

第一章剖析了国家“十三五”新能源汽车发展规划以及对动力电池管理系统的技术指标,系统地阐述了动力电池系统及管理的设计与实现要点。

第二章阐述了动力电池测试平台搭建、实验方法设计与特性分析,系统分析了动力电池在不同老化、温度和充放电倍率等因素下的工作特性,为动力电池管理系统核心算法开发提供了方向性指引。

第三章至第七章系统深入地论述了动力电池系统建模、荷电状态与健康状态双估计、峰值功率预测、剩余寿命预测、低温快速加热与优化充电等动力电池管理系统核心算法的基础理论、算法构建与实施细节。

最后,从核心算法的软硬件在环仿真验证、台架测试和实车验证等角度论述了动力电池管理系统算法的“V”开发流程。本书力求做到文字准确、精练,插图清晰,内容系统、详实、先进,力求站在前沿领域帮助读者掌握新能源汽车动力电池管理系统的核心算法。


专著资源分享

1. 模型:电池等效模型(Thevenin模型.zip, 46.5kB);电化学模型(电化学模型.zip, 54.9kB);分数阶模型(分数阶模型.zip, 243kB);(详细内容查看)

2. SOX算法:EKF-SOC算法模型(EKF_SOC.zip, 162kB);SOH算法模型(SOH.zip, 102kB);SOP算法模型(SOP.zip, 2.19MB);(详细内容查看)

3. 寿命预测算法(寿命预测算法.zip, 3.57kB);(详细内容查看)

4. 故障诊断方法(故障诊断方法.zip,3.45kB);(详细内容查看)

5. 优化充电方法(优化充电方法.zip,3.33kB);(详细内容查看)

6. 低温加热算法(低温加热算法.zip, 9.22kB);(详细内容查看)

7. 测试数据:电池单体数据,电池组数据,实车运行数据 (点击查看)

8. 试读 http://www.aesa.net.cn/upload/image/icon_pdf.gif部分章节.pdf

印刷次数

第1版第1次印刷第1版第2次印刷第1版第3次印刷


完整出版信息:熊瑞. 动力电池管理系统核心算法[M]. 北京:机械工业出版社,2018. (京东购书)


相关参考论文

1. R. Xiong, S. Ma, H. Li, F. Sun and J.Li, “Towards a Safer Battery Management System: A Critical Review on Diagnosis and Prognosis of Battery Short Circuit”, iScience, vol. 23, no. 4, pp. 101010, April 2020. (下载链接)

2. R. Xiong, Q. Yu, W. Shen, C.Lin and F. Sun, "A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles", IEEE Transactions on Power Electronics, 2019, vol. 34, no. 10, pp. 9709-9718, OCT 2019. (下载链接)

3. R. Xiong, Y. Zhang, H. He, X. Zhou, Michael Pecht, “A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries,” IEEE Transactions on Industrial Electronics, vol.65, no.2, pp.1526-1538, Feb 2018. (下载链接)

4. R. Xiong, JP Tian, H Mu, C. Wang, “A systematic model-based degradation behavior recognition and health monitor method of lithium-ion batteries,” Appl Energy, vol. 207, pp. 367-378, DEC 2017. (下载链接)

5. R. Xiong, Q.Q Yu, LY Wang, C Lin, “A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter,” Appl Energy, vol. 207, pp. 341-348, DEC 2017. (下载链接)

6. F. Sun; R. Xiong and H. He, “Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions,” J. Power Sources, vol.259, pp.166–176, Aug. 2014. (下载链接)

7. R. Xiong; F. Sun; X. Gong and C. Gao, “A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles,” Appl Energy, vol. 113, pp. 1421–1433, Jan. 2014. (下载链接)

8. R. Xiong; F. Sun; Z. Chen and H. He, “A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles,” Appl Energy, vol. 113, pp. 463-476, Jan. 2014. (下载链接)

9. R. Xiong; F. Sun; H. He and T. Nguyen, “A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles,” Energy, vol. 63, pp. 295–308, Dec. 2013. (下载链接)

10. R. Xiong; F. Sun; X. Gong and H. He, “Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles,” J. Power Sources, vol. 242, pp. 699–713, Nov., 2013. (下载链接)

11. R. Xiong; X. Gong and C. C. Mi, “A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter,” J. Power Sources, vol. 243, pp. 805–816, Jun. 2013. (下载链接)

12. R. Xiong; H. He; F. Sun; X. Liu and Z.Liu, “Model-based State of Charge and peak power capability joint estimation of Lithium-Ion battery in plug-in hybrid electric vehicles,” J. Power Sources, vol. 229, pp. 159–169, May 2012. (下载链接)

13. R. Xiong; H. He; F. Sun and K. Zhao, “Evaluation on State of Charge Estimation of Batteries with Adaptive Extended Kalman Filter by Experiment Approach,” IEEE T VEH TECHNOL. Vol. 62, no.1, pp. 108–117, Jan. 2013. (下载链接)

14. R. Xiong; F. Sun and H. He, “Data-driven State-of-charge Estimator for Electric Vehicles Battery using Robust Extended Kalman Filter,” INT J AUTOMOT TECHN., vol. 15, no. 1, pp. 89–96, Feb. 2014. (下载链接)

15. R. Xiong; H. He; F. Sun and K. Zhao, “Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach,” Energies, vol. 5, no. 5, pp. 1455-1469, May 2012. (下载链接)

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