Electrified Powertrains

  Copyright: © RWTH Aachen | CMP Figure 1: Different topologies of electrified powertrains. The figure shows the powertrain topology of a conventional vehicle, a parallel hybrid vehicle, a power split hybrid vehicle and a series hybrid vehicle.

Introduction

The need for mobility and the opportunities to be mobile have never been greater than today. New and alternative mobility scenarios must be continuously researched and developed in order to optimize powertrain technologies for passenger and freight transport both in urban areas and over longer distances under economically and environmentally sound boundary conditions.

A priority goal for mobility in the coming years is to further reduce CO₂ emissions in order to achieve the global climate targets. In addition, NO_x and particulate matter emissions must also be further reduced significantly so that local air quality, especially in cities, can be sustainably improved. In addition to the further development of internal combustion engines and new clean fuels, the focus is also on electrified and partially electrified powertrain systems.

  Copyright: © RWTH Aachen | CMP Figure 2: Advantages and disadvantages of the different powertrain topologies. Shown are the impacts of the topology on the mass of the storage medium, battery capacity, power, emissions and the expected growth of the energy density.

The electrification of vehicle drives means that large electrical components, such as high-voltage batteries, inverters and electrical machines, are being brought into the vehicles. The development, production and integration of these systems pose many challenges for our scientific staff. In basic research, a wide variety of powertrain topologies are analyzed and optimized, see Figure1.

Of particular importance are the CO₂ reduction potential, the extension of the driving range and the optimization of the batteries with respect to size, weight and cost, see Figure 2.

  Copyright: © RWTH Aachen | CMP Figure 3: Schematic representation of the model predictive control of a hybrid vehicle. The model-predictive control loop contains the vehicle model, the energy management and the optimal gear selection and power distribution.

Function development

In order to develop innovative and sustainable solutions for the reduction of emissions and energy consumption, the I nstitute develops elaborate environmental, charging infrastructure, fleet, traffic, vehicle, component, ECU, 2D and 3D models, which enable market-strategic real-time simulations. Thus, the latest requirements for faster implementations with standardized safegua rding of the development results are met. Function development for electrified powertrains can be very multifaceted , including, above all, the development of state-of-the-art model-predictive and self-learning control systems, see Figures 3 and 4.

  Copyright: © RWTH Aachen | CMP Figure 4: Impacts of Real-Time optimization on velocity profile, gear selection, energy and power.   Copyright: © RWTH Aachen | CMP Figure 5: Design of a 48V mild hybrid prototype Mercedes Benz AMG A45 with air, fuel and an electrical path.

Implementation

The evaluation and validation under real environmental and driving conditions is an important task for powertrain development. With the introduction of new development methods, the Center for Mobile Propulsion and the Fuel Science Center of RWTH Aachen University offer ideal conditions for this. Examples include a parallel hybrid model with a real truck engine for predictive, energy-optimized drive train control for commercial vehicles over long distances in the IMPERIUM research project, the complete simulation of the drive components in a closed control loop with real-time control in the DUETT research project, the development of a fuel cell range extender in the BREEZE! research program and the development of a biogas-powered range extender in the GreenREX research project. The described competences are bundled in various research projects and implemented on test benches or in demonstration vehicles, see Figure 5.