AICE zero emission scenario for 2050 with industry-academia collaboration
Expert Leader/NISSAN MOTOR Co., Ltd.
Chairperson of Steering Committee/AICE
Senior Engineer/NISSAN Motor Co., Ltd.
Abstract: The realization of a carbon-free society is now a common goal all over the world, and its realization is an urgent task. It is generally understood that carbon-neutral is also the goal of realizing a sustainable society for the future of automobiles. Along with this, the number of cities banning the sale of gasoline and diesel vehicles will increase after 2025, and it is expected that battery electric vehicles will become mainstream in the future. And it is said that there is no future for internal combustion engines that cannot achieve carbon-neutral. It follows from these that the key to the survival of an internal combustion engine towards the 2050 is whether it can achieve carbon-neutral.
AICE (The Research Association of Automotive Internal Combustion Engines) believes that Well-to-Wheel Zero CO2 for internal combustion engines requires an electrified powertrain that means a concept of a new generation internal combustion engine that combines electrification rather than an extension of traditional technology. Technical scenario for achieving Well-to-Wheel Zero CO2 with this new electrified powertrain in 2050 was considered by AICE. In order to realize Well-to-Wheel Zero CO2, it is necessary to create innovative technologies that are not bound by conventional technologies. AICE considers one of the key drivers for creating innovative technologies is industry-academia collaboration.
This presentation deals with the technical scenario of AICE for Well-to-Wheel Zero CO2 in 2050 with electrified powertrains and the activities of Industry-Academia Collaboration.
Bio: Manabu Hasegawa started his career at Nissan Motor Corporation in 1990 as an engine researcher. Since then he has led several researches, advanced engineering and product projects for diesel engines. From 2016 to the present, he is Senior Engineer for researching future promising technologies for powertrains. From 2016, he is the member of AICE (The Research Association of Automotive Internal Combustion Engine).
A uncertainty rejection-based path-following controller for unmanned rollers and its industrial application
Professor, School of Mechanical Engineering, Tianjin University, China
Vice director in State Key Laboratory of Engines, Tianjin University
Director of Autonomous Driving Cross-research Platform, Tianjin University
Abstract: The drum roller, as a widely used engineering vehicle, has higher degree of freedom in motion relative to conventional passenger vehicles. The special operating condition that has large rocks on road for compaction introduces severe disturbances in path-following. In this talk, a composite disturbance rejection-based framework, for the path-following control of rollers, will be presented. The external disturbances, caused by rocks on road, are rejected by correcting the coordinates of rollers from Global Position System (GPS) using measured attitude information. The nonlinearities from the complex articulation structure are compensated using a kinematic model-based feedforward control. All other uncertainties, internal and external, are lumped as an augmented state - “total disturbance”, estimated hence rejected in real-time via the extended state observer (ESO). As compliment to ESO with the limited performance due to low sampling rate of GPS, a model parameters self-learning algorithm is added. The proposed solution is validated both in simulation and experiments, showing satisfactory performance. The maximum lateral error is ~0.1m for unmanned rollers, out-performing the average level of human driven rollers when working on road with maximum diameter of rocks up to 1m. The industrial application of 15-roller fleet in Sichuan province will be introduced.
Bio: Prof. Hui Xie received his PhD in propulsion machine and engineering at Tianjin University in 1998, and now he holds a position as professor and vice director in State Key Laboratory of Engines at Tianjin University, also as director of Autonomous Driving Cross-research Platform at Tianjin University. His research interests include intelligent control of engine, powertrain and vehicle, autonomous driving vehicle and big data analysis. His research achievements include advanced intelligent control algorithms of engines, multi-core hardware control architecture, self-optimization energy management methods and autonomous driving algorithm. He published 80+ papers and 30+ authorized invention patents. He got 2014 National Educating Achievement Award, 2018 China machinery industry science and technology award and 2019 Tianjin government science and technology.
Leveraging connectivity and machine learning for progressing powertrain modeling and control problems
Ford Research, USA
Abstract: Recent developments in multidomain connectivity and Machine Learning are providing new opportunities for modeling and control of dynamical systems. It is now possible to consider new sensing modalities for control. Vision is the new and powerful sensor that, along with connectivity, afford several benefits such as extending the forward-looking control horizon for better utilization of preview. ML techniques also allow powerful ways to analyze data for better insights without always relying explicitly on hand crafted models. In this talk we show case on example that leverages these techniques and applies them to the rather challenging problem of the real time optimal control of Diesel Particulate Filters. We also discuss new, data-driven, opportunities that the control community may begin to consider in order to create robust realizations of these methods.
Bio: Dr. Devesh Upadhyay is a technical leader in Ford’s Research and Advanced organization (formerly known as Scientific Research Labs). In this role Devesh leads the research effort to progress the state of the art in AI/ML and Quantum Computing as they relate to the broad domain of engineering problems.
Start your impossible
~challenge for digitalization of powertrain development process~
General Manager, Measurement Instrumentation & Digital Development Innovation Div.
Toyota Motor Corporation, Japan
Abstract: The market demand for electrified vehicles is expanding significantly. However, the needs of our worldwide customers are quite different. It means that we have to deliver the diversified products. From the viewpoint of the vehicle and the powertrain development, it’s extremely challenging. The development must be more efficient and smarter than ever before. To meet this challenge, we at Toyota are focusing on digitalize our development process.
Bio: Junichi Matsudaira started his career at Toyota in 1990 as an engine evaluation engineer. Since then, he has lead the development of engine components and new engine project based on TNGA, Toyota New Global Architecture. From 2016, he is leading the digitalization of the vehicle and powertrain development process.
Physical-based model and AI technology for control, communication and collaboration
Professor, Department of Mechanical Engineering
The University of Tokyo, Japan
Abstract: Internal combustion engines are basically complicated systems, and they are still getting more complicated due to the combination of electrical components. Furthermore, we must consider driver’s behavior and use a huge amount of information from the connected devices to decrease fuel consumption and emissions in real world operation. As the next stage of CO2 reduction phase, it is also facing the carbon-free society. Requirements for powertrain control are becoming more and more sophisticated, and various knowledge, technologies and researchers in different disciplines are to be cooperatively introduced. Then, models will play more important roles to design control system, combine different things, disciplines, and researchers. In this presentation, taking “model” as a keyword, I will introduce a developed model-based control system for the advanced combustion engine, an on-line calibration algorithm, a driver model for engine control and discuss the aspect of the model as a communication tool.
Bio: Yudai Yamasaki is an Associate Professor in Department of Mechanical Engineering at The University of Tokyo. He received his PhD degree from Keio University 2003, PhD thesis was “Study on ignition and combustion mechanism of HCCI engine”. Oct. 2003, He joined as a researcher, Dept. of Mechanical Engineering, The University of Tokyo, where he engaged in developing engine control systems using biomass resources. His research interests include engine combustion and its control, alternative fuel, chemical reaction in ICE, combustion analysis and diagnostic, and distributed energy systems. Recently he also challenges applying AI technology to power train systems. He was also responsible for developing a control oriented–model and also managing a control group in a national project SIP (Cross-ministerial Strategic Innovative Promotion Program) from 2014-2019. Now, he is also promoting several collaborative works related to powertrain control systems with AICE (The Research association of Automotive Internal Combustion Engines).
The future of automotive powertrains in the age of electrification, connectivity and automation – some thoughts about how our community will evolve in the next decades
The Ford Motor Company Chair in Electromechanical Systems
Director and Senior Fellow, Center for Automotive Research
Professor, Department of Mechanical and Aerospace Engineering
Professor, Department of Electrical and Computer Engineering
Abstract: The evolution of the automotive industry towards increased electrification, connectivity and automation will have a profound impact on research and development of future automotive powertrains. In addition to the more immediately evident changes – focusing on electric propulsion, energy storage, charging infrastructure, there is growing interest in the impact of connectivity ad automation on the opportunity to significantly improve powertrain efficiency by optimizing the use of powertrains based on external information, including vehicle-to-vehicle and vehicle-to-infrastructure connectivity, as well as varying degrees of vehicle automation.
This presentation will review some of these advances in the context of the expertise that characterizes the IFAC ECOSM community. In summary, the depth of expertise that is represented in the ECOSM community is well positioned to continue to make a positive impact on the industry and more broadly on society leveraging its expertise in modeling, control and optimization of powertrain systems
Bio: Prof. Giorgio Rizzoni, the Ford Motor Company Chair in ElectroMechanical Systems, is a Professor of Mechanical and Aerospace Engineering and of Electrical and Computer Engineering at The Ohio State University (OSU). He received his B.S. (ECE) in 1980, his M.S. (ECE) in 1982, his Ph.D. (ECE) in 1986, all from the University of Michigan. Since 1999 he has been the director of the Ohio State University Center for Automotive Research (CAR), an interdisciplinary university research center in the OSU College of Engineering. His research activities are related to modeling, control and diagnosis of advanced propulsion systems, vehicle fault diagnosis and prognosis, electrified powertrains and energy storage systems, vehicle safety and intelligence, and sustainable mobility. He has contributed to the development of graduate curricula in these areas, and has served as the director of three U.S. Department of Energy Graduate Automotive Technology Education Centers of Excellence: Hybrid Drivetrains and Control Systems (1998-2004), Advanced Propulsion Systems (2005-2011, and Energy Efficient Vehicles for Sustainable Mobility (2011-2016). Between 2011 and 2016 he served as the OSU Site Director for the U.S. Department of Energy China-USA Clean Energy Research Center - Clean Vehicles. He is currently leading an ARPA-E project in the NEXTCAR program. During his career at Ohio State, Prof. Rizzoni has directed externally sponsored research projects funded by major government agencies and by the automotive industry in approximately equal proportion. Prof. Rizzoni is a Fellow of SAE (2005), a Fellow of IEEE (2004), a recipient of the 1991 National Science Foundation Presidential Young Investigator Award, and of many other technical and teaching awards.
The OSU Center for Automotive Research, CAR, is an interdisciplinary research center in the OSU College of Engineering founded in 1991 and located in a 60,000 ft2 building complex on the west campus of OSU. CAR conducts interdisciplinary research in collaboration with the OSU colleges of Engineering, Medicine, Business, and Arts and Sciences, and with industry and government partners. CAR research aims to: develop efficient vehicle propulsion and energy storage systems; develop new sustainable mobility concepts; reduce the impact of vehicles on the environment; improve vehicle safety and reduce occupant and pedestrian injuries; increase vehicle autonomy and intelligence; and create quieter and more comfortable automobiles. A team of 45 administrative and research staff supports some 50 faculty, 120 graduate and 300 undergraduate students, and maintains and makes use of advanced experimental facilities. Dr. Rizzoni has led CAR for over a decade, growing its annual research expenditures from $1M per year to over $14M today, and engaging CAR in a broad range of technology commercialization activities, start-up company incubation and spin-out as well as providing a broad range of engineering services to the automotive industry. CAR is also the home of the OSU Motorsport program, which supports the activities of 7 student vehicle competition programs: the Buckeye Bullet (holder of all current electric vehicle land speed records), EcoCAR hybrid-electric vehicle team, FSAE, Baja SAE, Buckeye Electric Motorcycle Racing Team, Supermileage SAE, and Underwater Robotics Team.
From OBD to connected diagnostics: a game changer at fleet, vehicle and component level
Professor, Departamento de Máquinas y Motores Térmicos
Universitat Politècnica de València, Spain
Abstract: Early on-board diagnostics (OBD) standards were enforced in 1988 and, by the beginning of the XXI century, all major automotive markets require some sort of OBD. Over the years, the diagnostics software layer has grown in complexity, yet robust fault detection remains a challenging task: insufficient memory and computation power, suboptimal calibration, and the lack of sufficient real-life operation data for model development are some of the limiting factors. The connected vehicle paradigm allows a complete reshaping of the vehicle diagnostics: real-life data feeds provide operation data of the vehicle fleet; artificial intelligence assists data clustering and model development; and over-the-air update tools allow the deployment of new software components and optimized calibration. A smart combination of embedded and cloud components seems to be a major step forward for the next generation of vehicles, allowing the determination of the in-service emissions at vehicle and fleet level. While many challenges are still to be solved, connectivity offers a giant leap in the area of vehicular diagnostics.
Bio: Carlos Guardiola received the MSc degree in Mechanical Engineering and the Ph.D. degree from the Universitat Politècnica de València, Spain, in 2000 and 2005, respectively. He is professor at the same university, where he leads research on automotive control and diagnostics. He keeps an active collaboration with the industry, and some of the software components he has developed are currently in production, notably in diesel and SI aftertreatment diagnostics. He is coauthor of more than 100 papers at journal and conferences.
Dr. Guardiola is member of Technical Committee on Automotive Control of the International Federation of Automatic Control, of the Editorial Board of the Proceeding of the Institution of Mechanical Engineering, Part D: Journal of Automobile Engineering, and Co-Editor of Springer Tracts on Mechanical Engineering. He is a recipient of the 2014 Ralph R. Teetor Educational Award by SAE International, and of the 2017 Betancourt y Molina Gold Medal by the Spanish Royal Academy of Engineering.