Semi-Plenary Lecture

Title: Model-based estimation approach for state of charge of lithium-ion battery

Panelist: Lin He (贺林)

Abstract: The state of charge is a key parameter of lithium-ion battery, whose estimation approaches may be divided into classical control and reinforce learning algorithm. For an accurate SOC of lithium-ion battery by classical control algorithm, we have met with two problems, i.e. the accuracy of model, and the practicability of algorithm. Respect to the practicability of algorithm, it is important that the algorithm should not only match with the characteristics of state of charge, but also be applied into practical engineering. As for the accuracy of model, there are lots of battery models proposed in some literatures. An accurate battery model, which can describe the dynamics characteristics of lithium-ion battery, is one of the most important factors affecting the final estimation result. In this lecture, we will discuss how to respond to the challenge of accurate estimation for the state of charge of lithium-ion battery.

Personal ProfileHe Lin was born in Nanchong, China, in 1977. He received the Ph.D. degree in automotive engineering from Tongji University, Shanghai, China, in 2010. After that, he entered into the State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China, as an assistant research fellow. He did some researches of advanced engine control technology for Toyota Motor Corporation from Apr. 2010 to Mar. 2011 in Sophia University, Tokyo, Japan. From Jan. 2012 to May 2016, He was a product engineering manager of Foton Motor Group to develop new energy vehicle. In the meantime, As a project chief engineer, He developed first fuel cell truck of China and first full platform of battery electric truck for Foton. Now, He is a professor of Hefei University of Technology. He has written two books of new energy vehicle, i.e. Electric Vehicle Design, Power Battery. His research interests mainly include vehicle dynamics & control, and automotive powertrain & control, lithium-ion battery management, electric motor control, steer-by-wire, brake-by-wire, intelligent vehicle. Email:


Title: The Development and Application of Automotive Fuel Cell Control Unit

Panelist: Weihai Jiang (江维海)

Abstract: The Proton Exchange Membrane Fuel Cell(PEMFC) are widely regarded as one of a very important alternative automotive powertrain for the next generation of vehicles, since it has the advantages of high efficiency, zero emission, low noise and short start-up time, etc. However, the electrochemical reaction process of fuel cell in practical application is extremely sensitive to the system conditions such as component variations within fuel cell, temperature, pressure and humidity, etc. What’s more, the system has strong nonlinearity and serious coupling among subsystems. It is a multi-disciplinary complex system, which brings great challenges to the system control design. A reliable Fuel Cell Control Unit (FCU) can improve the working efficiency and prolong the service life of fuel cell at the same time. This presentation will systematically introduce the development of Fuel Cell Control Unit(FCU) from the aspects of the development process of FCU, hardware development, software development, Hardware in the Loop(HIL) test, the modeling and calibration of fuel cell system , control algorithm development and validation, etc.

Personal ProfileWeihai Jiang, a senior engineering of Automotive Engineering Research Institute, China Automotive Technology and Research Center Co.,Ltd(CATARC). He received the M.S. degree in power engineering from Tianjin University, China, in 2016 and the Ph.D. degree in control engineering from Sophia University, Tokyo, Japan, in 2019. He has published more than 20 papers and 4 invention patents. He won the third prize of the Innovation and Entrepreneurship Competition from the Fifth young member of Jiangsu Association for science and technology, in 2020. He won the excellent solution Award for Hualing Xingma Autombile(grop)CO.,LTD. from Anhui Provincial Department of science and technology, in 2020. He received an excellent scientific and technological achievement award from CATARC and an Advanced individual in scientific research award from the Automotive Engineering Research Institute of CATARC, in 2020. His research interests include advanced control technology of new energy vehicles , Connected and Autonomous vehicles.



Title: Deep Learning based Object Detection and Semantic Segmentation for Autonomous Driving

Panelist: Ying Li (李颖)

Abstract: Accurate environmental perception is the key for reliable navigation, accurate decision-making, and safe driving of autonomous vehicles in complex environments. This task needs to extract reliable and accurate environmental information using the data acquired by on-board sensors. In recent years, with the breakthroughs of computing platforms and the publication of high-quality labeled perception datasets, deep learning has achieved a series of remarkable results in environment perception for autonomous driving. How to extract accurate information using deep learning algorithms in different driving scenarios plays an important role in decision-making and planning. Therefore, this report focuses on the research of deep learning based object detection and semantic segmentation in different driving scenarios.

Personal ProfileYing Li received the Ph.D. degree from the University of Waterloo, Canada, in 2021. She is currently an Assistant Professor with the School of Mechanical Engineering, Beijing Institute of Technology, China. Her current research focuses on autonomous driving, environmental perception, computer vision, mobile laser scanning, geometric and semantic modeling, and high definition mapping. She has published more than 20 SCI/EI papers. She is also the principal investigator of 4 grants, including the National Science Fund for Youth and the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program).



Title: Bridging the Theory to Practice: A Review of the IFAC ECOSM Benchmark Problem

Panelist: Fuguo Xu (徐福国)

Abstract: In the 6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling (ECOSM 2021), Tokyo 2021, we collaborated with scholars from Sweden, Spain, China and Japan and engineers from Toyota Motor Corporation, Japan to propose a benchmark problem for the challengers aiming to energy efficiency control of connected and intelligent hybrid electric vehicles (HEVs). The purpose of this benchmark problem is to provide a platform for the students and younger researchers to challenge the issues of the next generation powertrain control, and exchanging the frontier research results in automotive system control and optimization.

The targeted HEVs are under the connected environment with of real-time information of vehicle-to-everything, including geographic information, vehicle-to-infrastructure information and vehicle-to-vehicle information. The provided high-fidelity simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfil fuel economy improvement while satisfying the constraints of the driving safety and travel time.

This presentation will give an introduction of the benchmark challenging problem and our provided simulator, and discuss the fuel economy performances of the submitted controller results.

Personal ProfileFuguo Xu received the M.E. degree in control theory and control engineering from Yanshan University, Qinhuangdao, China, in 2016 and the Ph.D. degree in green science and engineering from Sophia University, Tokyo, Japan, in 2019. Since 2019, he has been a Postdoctoral Fellow with the Department of Engineering and Applied Sciences, Sophia University. He has served as the student activity chair of IFAC Conference on ECOSM 2021 and he is serving as the guest editor of Control Theory and Technology. His research interests include optimal control and applications in powertrain systems of hybrid electric vehicles and connected vehicles.