Electromobility is a research area with a strong interdisciplinary character. Research thus requires a systemic approach integrating vehicular systems, power systems, and transportation systems. Our research activities follow this systemic approach. Our main research areas are the grid integration of and the energy management in electric and hybrid vehicles.
Grid Integration of Electric and Hybrid Vehicles
Power systems are currently undergoing substantial changes since power generation gradually shifts from few large centralized conventional generators to many small decentralized renewable generators. The volatile and decentralized character of renewable energy sources necessitates completely new methods and components for power system operation and control. Electric and hybrid vehicles as mobile energy storages can considerably contribute to handling the changes. For example, ancillary services for frequency and voltage control can be provided by coordinated charging and discharging of vehicle batteries. Furthermore, the charging infrastructure for electric and hybrid vehicles can be utilized for reactive power compensation and harmonic filtering, substantially contributing to grid stabilization and conditioning particularly in low-voltage grids. Overall, electric and hybrid vehicles have an important role for an efficient and reliable power supply.
A major challenge associated with grid integration of electric and hybrid vehicles as well as other decentralized generators and loads is the system complexity. The enormous number, the high volatility, and the fast dynamics of the components necessitate fundamentally new coordination mechanisms. This is where our research starts. Our research focus is on distributed and hierarchical methods for power system operation and control integrating electric and hybrid vehicles as well as further decentralized generators and loads. Particularly methods for
networked frequency and voltage control,
coordinated reactive power compensation and harmonic filtering, and
distributed optimal power flow
are investigated. To this end distributed and hierarchical model predictive control methods are developed based on distributed optimization methods and game theory and evaluated experimentally. Such methods have received considerable attention in recent years, particularly in the context of cyber-physical systems. Additionally, methods for planning grids to charge electric vehicles while regarding energy and transportation aspects as well as methods for analyzing the impact of charging electric vehicles on the grid are investigated.
Energy Management in Electric and Hybrid Vehicles
A sustainable mobility requires an efficient utilization of the energy. To this end a holistic energy management is a key technology. The energy management coordinates the power flow between energy storages, drive units, and auxiliary units such that the energy demand is minimized while regarding driver demands, comfort desires, and safety requirements as well as considering geographic, traffic, and weather conditions. While research on battery and drive technology has been intensely studied during the last years both in industry and academia, the energy management has not been in focus. This is where our research starts. Our research objective is the development of innovative energy management systems for electric and hybrid vehicles. Main research areas are the
development of energy management systems for
plug-in hybrid electric vehicles (PHEVs),
development of driver assistance systems for eco-driving
(based on optic, acoustic, and haptic feedback),
integration of predictions based on geographic, traffic, and weather data,
integration of a thermal management,
integration of novel energy storages
(mechanical, hydraulic, and pneumatic storages, supercaps), and
integration of novel regeneration components
(regenerative suspension components, regenerative power lifts in commercial vehicles, thermoelectric generators).
Cornerstone of the energy management systems are model predictive control methods. Particularly, robust and stochastic methods are developed to handle uncertainties. Furthermore, explicit and fast methods are investigated to enable a cost-efficient real-time implementation.
A special focus is on energy management systems for electric and hybrid commercial vehicles. Due to well-defined operation profiles, e.g. operation schedules for agricultural and construction machines, time tables for urban buses, and route plans for special vehicles like waste trucks, commercial vehicles have a high potential for a predictive energy management.
Our main research areas will be gradually extended during the next years. We are always open for project ideas. Please contact us.