Research Projects

 
Ongoing Research Projects
  • GINI
    "Robot for flexible automatic charging of electric vehicles"
  • FOR 2401
    "Optimization-Based Multiscale Control of Low-Temperature Combustion Engines"
  • Heuristic Search and Deep Learning
    "Fully automated learning of optimized control strategies"
  • HERCET
    "Development and Validation of a Cost Effective Hybrid Electric Drive Solution for Small Two Wheelers for Reducing CO2 emission "
  • Hy-Nets4all
    "Validation environment for the optimization of electrified driving in urban space"
  • ALADIN
    "Active Learning based Automated Data Processing for Energy-efficient Driving Functions"
  • CEVOLVER
    "Connected Electric Vehicle Optimized for Life, Value, Efficiency and Range"

 
Completed Research Projects
  • ACOSAR
    "Advanced Co-Simulation Open System Architecture"
  • ConneCDT
    "Co-Simulation Platform Connecting Chemistry and Powertrain Dynamics to Traffic Simulation "
  • CREST
    "Collaborative Embedded Systems"
  • EVOLVE
    "Validation of an Energy-efficient longitudinal vehicle motion control based on model predictive control"
  • HELENE
    "Hardware-in-the-Loop basierte Funktionsentwicklung zur emissionsoptimierten Regelung von Fahrzeugantrieben mit Reinforcement Learning"
  • HIFI-ELEMENTS
    "High Fidelity Electric Modelling and Testing"
  • Hy-Nets
    "Efficient hybrid powertrains by vehicle communication"
  • ICCC
    "Ion-Current Sensor based Closed-Loop Control of Lean Gasoline Combustion with High Compression Ratio"
  • IMPERIUM
    "Implementation of Powertrain Control for Economic, Low Real Driving Emissions and Fuel Consumption"
  • NET-ECU
    "Connected Engine Control"
  • STEP
    "Smart Traffic Eco Powertrain"
  GINI Logo
Title Robot for flexible automatic charging of electric vehicles
Acronym GINI
Funding

Federal Ministry for Economic Affairs and Climate Action

Description

GINI pursues the goal of developing a smart, semi-autonomous, mobile charging robot with fast charging technology and an inductive charging interface. In addition to the function of charging electrified vehicles in urban areas, the charging of e-bike sharing stations as well as data acquisition, pre-processing and analysis in a networked environment will be enabled. Thus, a significant contribution can be made to the urgently needed expansion of cost-efficient, powerful and flexible charging infrastructure for electrified mobility solutions.

Term

01/2022 - 12/2024

  Logo FOR 2401
Title Research Unit 2401 – Optimization-Based Multiscale Control of Low-Temperature Combustion Engines
Acronym FOR 2401
Funding German Research Foundation (DFG)
Description

A state-of-the-art approach for closed-loop control of low temperature combustion processes are cycle-based control algorithms. However, these approaches allow only a stable operation in a very limited engine-map. Cycle-based controllers act such that only the system dynamics and disturbances which occur at a cycle-averaged time scale can be controlled. The relevant physico-chemical processes determining the stability and emissions characteristics of low temperature combustion, which proceed on a inner-cyclic time-level, can’t be controlled. For this reason TP1 investigates multiscale control algorithms, to also control the smaller time scales. It is expected that a successful control of these critical time scales allows for distinct enlarging of the operating range, increase of efficiency and reduction of pollutant emissions. The multiscale control is a novel approach.

Term

10/2020 - 09/2023

 
Title Heuristic Search and Deep Learning
Acronym Heuristic Search and Deep Learning
Funding Bundesministerium für Wirtschaft und Klimaschutz (BMWK),

Arbeitsgemeinschaft industrieller Forschungsvereinigungen (AiF)

Description

The development of transient control functions represents a major development effort, especially for highly complex, strongly non-linear systems such as that of a combustion engine. The need to consider many independent parameters also complicates the optimization process, making methodological approaches in addition to pure domain expertise a useful support. Reinforcement learning is a promising approach from the field of machine learning. In this approach, an agent independently learns a strategy that maximizes the reward it receives. Using this methodology, optimized control strategies can be learned fully automatically.

Term

11/2020 - 05/2023

  Logo HERCET
Title Development and Validation of a Cost Effective Hybrid Electric Drive Solution for Small Two Wheelers for Reducing CO2 emission
Acronym HERCET
Funding Federal Ministry of Education and Research,

Indo-German Science & Technology Centre (IGSTC)

Description

Achieving the CO2 targets requires reduced fuel consumption through the use of advanced hybrid engine technologies, which must also have the advantage of good vehicle range. To achieve this goal, it is necessary to think beyond the automotive sector. Predictions have shown that even with two-wheelers, the significant reduction of CO2 emissions is possible through the use of hybrid technologies. For example, fuel savings were demonstrated in a prototype of a plug-in hybrid two-wheeler, where a wheel hub motor was used on the front wheel, while the rear wheel was driven by an internal combustion engine. Although hybrid technology is already mature for four-wheelers, the two-wheeler segment is still relatively new due to the high costs and complexity involved.

Term

04/2020 - 03/2023

  Hy-Nets4all
Title Validation environment for the optimization of electrified driving in urban space
Acronym Hy-Nets4all
Funding European Union (European Regional Development Fund, ERDF)
Description

In Hy-Nets4all, a development and validation environment is being set up that will enable automated driving functions for electrified vehicles to be developed holistically and efficiently secured. The aim is to reduce the energy requirements and emissions of electrified vehicles in urban areas, to further develop electrical components in a targeted manner, to design driving concepts in line with available and future charging infrastructure, to optimise cooperative driving scenarios and to liquefy traffic flows.

Term 08/2019 - 07/2022
  Aladin Logo
Title

Active Learning based Automated Data Processing for Energy-efficient Driving Functions

Acronym ALADIN
Funding

Ministerium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen

Description

-

Term

01/2022 - 07/2022

  CEVOLVER Logo
Title Connected Electric Vehicle Optimised for Life, Value, Efficiency and Range
Acronym CEVOVLER
Funding European Commission, Horizon 2020
Description

The current generation of electric vehicles is still generally too expensive and limited in range. For this reason, the CEVOLVER project takes a user-centric approach to create battery-electric vehicles that are usable for comfortable long day trips while the installed battery is dimensioned for affordability. Furthermore, the vehicles will be designed to take advantage of future improvements in the fast-charging infrastructure that many countries are now planning.

CEVOLVER tackles the challenge by improving the vehicle itself to reduce energy consumption. Moreover, the usage of connectivity is maximized for further optimizations of both component and system design as well as control and operating strategies. Within the project, it will be demonstrated that long-trips are achievable even without any further increases in battery size, thus avoiding higher cost. The efficient transferability of the results to further vehicles is ensured by adopting a methodology that proves the benefit with an early assessment approach before an implementation in OEM demonstrator vehicles.

Term 11/2018–10/2022