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National Engineering Laboratory for Electric Vehicles of Beijing Institute of Technology received the Best Paper Award in the ISEV 2017

In the International Symposium on Electric Vehicles (ISEV2017) held in Stockholm, the capital of Sweden, on July 26th29th 2017, the group led by Prof. Hongwen He from the National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering of our university received the Best Paper Award, which was awarded by the Conf. Chair, Prof. Suleiman M Sharkhwith, with a paper entitled “Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries”.

ISEV2017 was a big international academic conference with a theme of advanced technologies and engineering applications of electric vehicles, which was held by Lunds University in Sweden. The conference papers can be recommend to be published on international journals including Journal of Cleaner Production(IF=5.715)Energy, Ecology and Environment and Energies (IF=2.262). The theme of the conference covered power electronics and electric motor drives; battery and management, charging systems and infrastructures; structure optimization, modeling and simulation; connected and automated vehicles, smart mobility, and vehicle security; and codes, standards, policies and regulations for transportation electrification, etc. Two best papers were finally selected from more than 240 conference papers accepted by the conference.

In the paper, the Ph.D. student Yongzhi Zhang, who was supervised by Prof. Hongwen He, developed a fusion technique based on the relevance vector machine and particle filter techniques to reduce the required training data and improve the method practicality of the traditional remaining useful life (RUL) prediction methods of lithium-ion batteries. The developed method in the paper reduced 70% of the required training data for an accurate RUL prediction. The Monte Carlo method was used to validate the developed fusion technique. The experimental results showed that the method can predict an accurate battery failure at an early point in the battery’s life.

Yongzhi Zhang graduated from Chongqing University in 2013 as an undergraduate. He is currently a Ph.D. student in the School of Mechanical Engineering of our university. In 9th 2016, he went to the University of Maryland in USA to study as a joint training Ph.D. student and the training will last for 18 months. His research focuses on the prognostics and health management of lithium-ion batteries used in electric vehicles.