• ToRSteM
  "Investigation of different temperature-optimized control strategies for electrical machines in a virtual complete vehicle network"
• HERCET
  "Development and Validation of a Cost Effective Hybrid Electric Drive Solution for Small Two Wheelers for Reducing CO2 emission "
• HELDA
  "Validation of a hybrid electric drive for light all-terrain vehicles"
• KIVER
  "Data-driven combustion modeling of homogeneous charge compression ignition processes for real-time use in model predictive control"
• Heuristic Search and Deep Learning
  "Fully automated learning of optimized control strategies"
• 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"
• 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"
• 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"
• 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"
• 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"
• VOKAL
  "Validation of a hardware-in-the-loop driving simulator for the development of CO2-emission-optimal engine control units"

Investigation of different temperature-optimized control strategies for electrical machines in a virtual complete vehicle network

Ministry of Economic Affairs, Industry, Climate Action and Energy of the State of North Rhine-Westphalia

The objective of this project is to validate the energy-saving potential through targeted thermal conditioning of electric drive machines. An existing test bench will be enabled to determine the efficiency of two test objects in thermal comparative tests. This will not only consider the electric machine and the inverter, but also takes into account the entire vehicle and its operating strategy with the aid of a hardware-in-the-loop approach.

08/2022 - 03/2023

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

11/2022 - 04/2023

Data-driven combustion modeling of homogeneous charge compression ignition processes for real-time use in model predictive control

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11/2022 - 04/2023

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 - 05/2023

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

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