• KoopF
  "Validation of the feasibility and robustness of energy-efficient and cooperative driving functions"
• FOR 2401
  "Optimization-Based Multiscale Control of Low-Temperature Combustion Engines"
• Heuristic Search and Deep Learning
  "Fully automated learning of optimized control strategies"
• VISION
  "Virtual sensors from chemistry-based models for intelligent online virtual calibration"
• GINI
  "Robot for flexible automatic charging of electric vehicles"
• XL-Connect
  "Large scale system approach for advanced charging solutions"
• ESCALATE
  "Powering EU Net Zero Future by Escalating Zero Emission HDVs and Logistic Intelligence"

• ACOSAR
  "Advanced Co-Simulation Open System Architecture"
• ALADIN
  "Active Learning based Automated Data Processing for Energy-efficient Driving Functions"
• CEVOLVER
  "Connected Electric Vehicle Optimized for Life, Value, Efficiency and Range"
• 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"
• HELDA
  "Validation of a hybrid electric drive for light all-terrain vehicles"
• HELENE
  "Hardware-in-the-Loop basierte Funktionsentwicklung zur emissionsoptimierten Regelung von Fahrzeugantrieben mit Reinforcement Learning"
• HERCET
  "Development and Validation of a Cost Effective Hybrid Electric Drive Solution for Small Two Wheelers for Reducing CO2 emission "
• HIFI-ELEMENTS
  "High Fidelity Electric Modelling and Testing"
• Hy-Nets
  "Efficient hybrid powertrains by vehicle communication"
• Hy-Nets4all
  "Validation environment for the optimization of electrified driving in urban space"
• 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"
• KIVER
  "Data-driven combustion modeling of homogeneous charge compression ignition processes for real-time use in model predictive control"
• NET-ECU
  "Connected Engine Control"
• ROTAFEM
  "Validation of a Sensorless Rotor Temperature Measurement for Active Field Weakening to Increase the Energy Efficiency of Electrical Machines"
• STEP
  "Smart Traffic Eco Powertrain"
• ToRSteM
  "Investigation of different temperature-optimized control strategies for electrical machines in a virtual complete vehicle network"
• VOKAL
  "Validation of a hardware-in-the-loop driving simulator for the development of CO2-emission-optimal engine control units"

Within the scope of this project, a driving function for cooperative driving (at least two automated vehicles in a vehicle network) previously developed in the simulation will be validated by driving tests. With the help of the work carried out, the energy-saving potential of cooperative driving functions identified in the simulation will be confirmed in the real vehicle and solutions for real-time capability and robustness to sensor inaccuracies and other disturbance variables will be developed.

01/2023 - 06/2023

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.

10/2016 - 09/2023

Arbeitsgemeinschaft industrieller Forschungsvereinigungen (AiF)

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.

11/2020 - 10/2023

Virtual sensors from chemistry-based models for intelligent online virtual calibration

Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

07/2022 - 12/2023

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

European Commission, Horizon Europe

The strong increase of electric vehicles is a big challenge for the energy system in Europe, but at the same time a chance to use V1G/V2G/V2X-technologies. As vehicles are mainly parking, they can be used as energy storage in order to increase grid stability. The overall project objective is to optimize the entire charging chain - from energy provision to the end user - to create a clear benefit for all stakeholders.

01/2023 - 06/2026

European Commission, Horizon Europe

In line with the European 2050 goals ESCALATE aims to demonstrate high-efficiency zHDV powertrains (up to 10% increase) for long-haul applications that will provide a range of 800 km without refueling/recharging and cover at least 500 km average daily operation (6+ months) in real conditions. ESCALATE will achieve this by following modularity and scalability approach starting from the β-level of hardware and software innovations and aiming to reach the γ-level in the first sprint and eventually the δ-level at the project end through its 2 sprint-V-cycle.

01/2023 - 06/2026