Springer出版社:Battery Management Algorithm for Electric Vehicles
发表时间:2020-01-01     阅读次数:     字体:【

简介:This book mainly introduces the technical details of the algorithm development of new energy vehicle power battery management system.


Chapter 1 analyzes the development plan of China's new energy vehicles in the 13th five year plan and the technical indicators of the power battery management system, and systematically expounds the design and implementation points of the power battery system and its management;

In Chapter 2, the construction, experimental design and characteristic analysis of power battery test platform are described. The working characteristics of power battery under different aging, temperature and charge discharge rate are analyzed systematically, which provides direction guidance for the development of core algorithm of power battery management system;

From Chapter 3 to Chapter 7, the basic theory, algorithm construction and implementation details of the core algorithms of power battery management system, such as power battery system modeling, state of charge and health collaborative estimation, peak power prediction, residual life prediction, low temperature rapid heating and optimal charging, are discussed systematically and deeply. Finally, the "V" development process of power battery management system algorithm is discussed from the aspects of hardware and software in the loop simulation verification, bench test and real vehicle verification of the core algorithm.


1. 模型:电池等效模型(Thevenin model.zip, 46.7kB);电化学模型(Electrochemical model.zip, 54.2kB);分数阶模型(Fractional order model.zip, 244kB);

2. SOX算法:EKF-SOC算法模型(program01_EKF.zip, 162kB);SOH算法模型(SOH.zip, 56.9kB);SOP算法模型(SOP.zip, 2.19MB);

3. 优化充电算法(optimal_charge.zip, 2.56kB);

4. 寿命预测模型(runBoxCox.zip, 3.45kB);

5. 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)

6. 代码申请:资源申请表.pdf

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

完整出版信息:Xiong R. Battery Management Algorithm for Electric Vehicles[M]. Springer, 2020. (出版社网站)


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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