• GINI
  "Robot for flexible automatic charging of electric vehicles"
• FOR 2401
  "Optimization-Based Multiscale Control of Low-Temperature Combustion Engines"
• 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"
• 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"

• ACOSAR
  "Advanced Co-Simulation Open System Architecture"
• ConneCDT
  "Co-Simulation Platform Connecting Chemistry and Powertrain Dynamics to Traffic Simulation "
• CREST
  "Collaborative Embedded Systems"
• 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"

Federal Ministry for Economic Affairs and Climate Action

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.

01/2022 - 12/2024

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.

10/2020 - 09/2023

Indo-German Science & Technology Centre (IGSTC)

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.

04/2020 - 03/2023

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.

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

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

Within the framework of ALADIN, active learning is to be used exemplarily in the context of traffic prediction during data acquisition in the vehicle. For this purpose, a test vehicle will be equipped with environmental sensors and a programmable data logger where the prediction models as well as the algorithms for Active Learning will be implemented. For the final evaluation, data sets selected both manually and by Active Learning will be used to train the prediction model and the performance of the resulting models will be compared.

01/2022 - 06/2022

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.

Automated driving functions open up a high potential for energy savings, especially in dynamic urban traffic, through optimized longitudinal vehicle guidance, which has already been demonstrated in various simulation studies. The aim of this research project is to evaluate and transfer optimization-based driving functions from simulation to reality. For this purpose, a test vehicle will be equipped with a prototype control unit in order to test the driving functions for evaluating the real energy saving potential on the test track in exemplary inner city scenarios.

To achieve global climate targets and to reduce local air pollutants, the transport sector has to make significant contributions. Modern drives evolve to electrified, networked, software-intensive overall systems with a large number of degrees of freedom. Innovations to minimize emissions and raise efficiency are increasingly made possible just by software. The effort required to develop, validate and calibrate these algorithms is increasing exponentially, while simultaneously available development times and resources are shortening. Within HELENE, a novel approach to hardware-in-the-loop based machine learning is demonstrated. Reinforcement learning is used to train specific powertrain functions in a realistic, heterogeneous virtual-real simulation scenario.