Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 785-791.doi: 10.23919/JSEE.2022.000078

• CLOUD CONTROL SYSTEMS • Previous Articles     Next Articles

Intelligent decision support platform of new energy vehicles

Zhenpo WANG1,2(), Zhenyu SUN1(), Peng LIU1,2(), Shuo WANG1,*(), Zhaosheng ZHANG1()   

  1. 1 Collaborative Innovation Center for Electric Vehicles & National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    2 Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401135, China
  • Received:2022-03-01 Online:2022-08-30 Published:2022-08-30
  • Contact: Shuo WANG E-mail:wangzhenpo@bit.edu.cn;bitzhenyu@163.com;bitliupeng@bit.edu.cn;shuo.wang@bit.edu.cn;zhangzhaosheng@bit.edu.cn
  • About author:|WANG Zhenpo was born in 1976. He received his B.S. degree in automotive engineering from Tongji University, Shanghai, China, in 2000, and Ph.D. degree in automotive engineering from Beijing Institute of Technology, Beijing, China, in 2005. He is currently a professor with Beijing Institute of Technology and the director of the National Engineering Research Center of Electric Vehicles. His current research interests include pure electric vehicle integration, packaging and energy management of battery systems, and charging station design. E-mail: wangzhenpo@bit.edu.cn||SUN Zhenyu was born in 1990. He received his B.S. degree in vehicle engineering from Beijing Forestry University, Beijing, China, in 2015, and M.S. degree in mechanical engineering from Beijing Institute of Technology, Beijing, China, in 2018, where he is currently working toward his Ph.D. degree with the mechanical engineering. He is a visiting student with the Delft University of Technology, Delft, the Netherlands, from 2021 to 2022. His research interests include electric vehicle big data analysis, fault diagnosis and safety management of lithium-ion battery system. E-mail: bitzhenyu@163.com||LIU Peng was born in 1983. He received his B.S. and M.S. degrees from Chang’an University, Xi’an, China, in 2005 and 2008, respectively, and Ph.D. degree from Beijing Institute of Technology, Beijing, China, in 2011. He is currently an associate professor with the School of Mechanical Engineering, Beijing Institute of Technology, China. His research interests include big data analysis and safety management of electric vehicles. E-mail: bitliupeng@bit.edu.cn||WANG Shuo was born in 1987. He received his B.S. degree from Shandong University of Sci-ence and Technology, Qingdao, China, M.S. degree from China University of Mining and Technology, Beijing, China, and Ph.D degree in engineering from the Faculty of Engineering and Information Technology, University of Technology Sydney in 2017. He was a postdoctoral research fellow with Beijing Institute of Technology from 2017 to 2021. He is an assistant professor at Beijing Institute of Technology, Beijing. His research interests include wireless charging, electric vehicle and its big data analysis. E-mail: shuo.wang@bit.edu.cn||ZHANG Zhaosheng was born in 1984. He received his Ph.D. degree in automotive engineering from Tsinghua University, Beijing, China, in 2013. He is currently an assistant professor with the School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China. He has authored or co-authored two monographs and more than 20 technical papers. He also holds more than 10 patents. His current research interests include intelligent transportation and big data analysis. E-mail: zhangzhaosheng@bit.edu.cn
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2019YFB1600800).

Abstract:

New energy vehicles (NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability, the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

Key words: new energy vehicle (NEV), intelligent decision support platform, software system, data platform application