果冻传媒

Focus is on novel system architecture generation of complex systems (e.g., powertrains, machinery equipment, high tech systems, etc.) using platform-based design ontology that allows reusability of components and facilitates co-design of active dynamical systems (e.g., powertrain and its control) is defined as a 鈥榯op-down鈥� and 鈥榖ottom-up鈥� design process, where new optimized system platforms are generated 鈥榠n the middle鈥� of this process. The system functional and performance requirements in the specification space are mapped by (i) formalization to (e.g., architectural, spatial, etc.) constraints and objectives in the constraint space. Thereby, this process of 鈥榤apping鈥� from top-to-down can be interpreted as moving from a higher level of abstraction to a lower level.

Development of novel powertrain system design methods for optimizing the key powertrain components (electric drive systems, motors, fuel cell systems, engines, transmissions, and battery packs) incorporating physics- and/or data-based control design approaches (energy and thermal management strategies). Current research applications: cars, trucks, buses, drones, planes, ships.

Development of self-learning powertrains is crucial to deal with complexity and diversity of future ultra-clean and efficient vehicles and to minimize development time and costs. Self-learning powertrains automatically adapt control settings online to minimize overall powertrain efficiency using sensor and preview information. This approach not only offers solutions to deal with system complexity of future ultra-clean and efficient vehicles, but also to minimize the calibration effort by on-road optimization and dealing with unforeseen environmental conditions and system uncertainty.

Developing high-energy and power-dense battery packs cost-effectively and sustainably is crucial for enhancing battery systems' performance, longevity, and safety while reducing environmental impact. There is no universal battery topology; the layout and shape significantly affect overall performance. Research focuses on automated creation of simulation models for various modular pack topologies, including thermal subsystems like cooling plate connections, to analyse electrical and thermal responses, essential for fast charging. This research includes single and multi-cell chemistries and considers thermal, safety, operational, and other system constraints to optimize cell-to-vehicle integration and electric vehicle performance. Applications range from cars, trucks, and buses to drones, planes, and ships, and is conducted by the Control Systems Technology group of the Department of Mechanical Engineering.

Increasing levels of automation are beneficial for safety and efficiency of systems such as cooperative and automated vehicles, robotics, or powertrains. To enable this increased automation in complex environments, this research will focus on the intersection of classical control theory and artificial intelligence (in particular, machine learning and optimization). With increasingly more sensors and data recorded in all existing devices, new opportunities are created to improve how we design and control newly developed systems.

Focus is on application-driven design of battery packs and its thermal management design aiming at creating high-energy / power-dense battery packs in a cost-effective manner. At this moment no universal battery topology exists and functional layout and shape influences largely the overall battery performances and specification. Moreover, automated creation of simulation models that match desired modular pack topology and include (thermal subsystems), e.g. cooling plate connections allowing electrical and thermal response analysis (for fast charging) is researched. Thermal, safety operational and other concurrent system architecture constraints are considered in the design tradeoffs aiming at optimal cell-to-vehicle integration. Current research applications: cars, trucks, buses, drones, planes, ships.