Joint Laboratory for Advanced Energy Storage and Applications, AESA, Beijing Institute of Technology

Books-CN

Fundamentals of Integrated Management of Hybrid Energy Storage System for EVs

Description：Relying on the relevant accumulation of the National Engineering Laboratory for Electric Vehicles of Beijing Institute of Technology in the design and development of new energy vehicles over the years, combined with the successful demonstation and application experience of electric vehicles in the 2008 Beijing Olympic Games, 2010 Shanghai World Expo and 2010 Guangzhou Asian Games, detailed theories and technologies related to the integrated management of electric vehicle compound power system. Adhering to the concept of combining theory with practice, and striving to be better understood by readers, this book uses many concrete examples when explaining the theoretical content.

More details (Chinese)：http://www.aesa.net.cn/NewsDetail.aspx?ID=733

Core Algorithms of Battery Management System (First Edition)

Description："Core Algorithm of Power Battery Management System" combines the author's research and practice for more than ten years, expounds the characteristics and technical problems of power battery management system, and elaborates the experimental design and dynamic modeling of power battery system for new energy vehicle applications. , State of charge estimation, state of health estimation, peak power prediction, remaining life prediction, low-temperature rapid heating and optimized charging, as well as engineering application and practical problems of corresponding core algorithms, with detailed algorithm practice steps and development processes, which can be used as a reference book for those skilled in the relevant fields.It can also be used as a professional course textbook for senior undergraduates and graduate students majoring in automobiles.

More details (Chinese)：http://www.aesa.net.cn/NewsDetail.aspx?ID=732

Principles and Structure of Electric Vehicles

Description：This book focuses on the system analysis and structural examples of electric vehicle principles. The content covers pure electric vehicles, hybrid electric vehicles, fuel cell electric vehicles and other vehicles, motor drive systems, power battery pack systems, and electrification assistance System and other components, as well as the infrastructure and applications of electric vehicles. Each independent chapter expands the description from three aspects: function definition, principle analysis and typical structure examples. The content is novel, with pictures and texts. This book can be used as a professional basic course textbook for the research direction of new energy vehicles for vehicle engineering majors in colleges and universities. It can also be used as a professional elective course textbook for vehicle engineering majors, a reference textbook for graduate students, and a reference book for engineering and technical personnel engaged in new energy vehicle technology research, production management, technical services, etc. A reference book for engineering and technical personnel.

More details (Chinese)：http://www.aesa.net.cn/NewsDetail.aspx?ID=736

Core Algorithms of Battery Management System (Second Edition)

Description："Core Algorithm of Power Battery Management System(Second Edition）" combines the author's research and Practice for many years, this paper expounds the characteristics of electric vehicle power battery management system and the key technical problems of its core algorithm development, and introduces in detail the power battery test, modeling, state estimation, remaining life prediction, fault diagnosis, low temperature heating, optimized charging, algorithm development Evaluation and testing, and the new generation of power battery management system, and with detailed algorithm practice steps and development process, focus on analyzing the key issues of the new energy field and the development of power battery. From the two aspects of advanced technology and common basic theory, help readers grasp the core algorithm of the new energy vehicle power battery management system.

More details (Chinese)：http://www.aesa.net.cn/NewsDetail.aspx?ID=994

Reference

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

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. (Download)

Available resources (data and code)

Please refer to the introduction and link of the Chinese book:
● http://www.aesa.net.cn/NewsDetail.aspx?ID=733

● http://www.aesa.net.cn/NewsDetail.aspx?ID=732

Errata

You are welcome to provide suggestions and amendments. Please email (rxiong@ieee.org) or let us know through here. Your name will also appear in the revised version.