**About the Editors**

**Stephan Rinderknecht** has served as Full Professor of Mechatronic Systems in Mechanical Engineering at the Technical University of Darmstadt since 2009. His research is focused on mechatronic vehicle systems, energy systems, general vibration systems and, recently, also on robotic systems. In some of his research fields, he works in very close cooperation with the industry, such as for example innovative drivetrains and propulsion systems for e-mobility as well as highly integrated designs for kinetic energy storage systems. There, recent key topics are sectoral integration and AI methods. For other research fields like active piezoelectric systems to reduce bending vibrations of flexible rotors, the projects approach more fundamental problems. Before becoming university professor, Stephan worked in the automotive transmission industry for more than 13 years and left the position of Senior Vice President of Research and Development. He graduated and received his doctoral degree at University of Stuttgart in the faculty of Aerospace Engineering.

**Philippe Jardin** has served as Research Associate of Mechatronic Systems in Mechanical Engineering at the Technical University of Darmstadt since 2017. His research is focused on mechatronic vehicle systems and driving comfort, where he applies machine learning methods jointly together with conventional development methods. Philippe graduated in Mechanical Engineering at TU Darmstadt and studied at University of California, Berkeley. During his studies, he focused on the control of mechatronic systems in practical applications.

**Arved Esser** has served as Research Associate for Mechatronic Systems in Mechanical Engineering at the Technical University of Darmstadt since 2016. The main focus of his research is on comparative evaluation of the ecological potential of vehicle powertrain concepts based on real fleet driving data using a big data approach. Arved studied Mechanical Engineering at TU Darmstadt and Institut National Polytechnique in Grenoble. During his studies, he focused on structural dynamics and vibration measurement technologies with applications in automotive engineering.

### *Editorial* **Special Issue on Future Powertrain Technologies**

**Philippe Jardin \*, Arved Esser \* and Stephan Rinderknecht \***

Department of Mechanical Engineering, Institute for Mechatronic Systems in Mechanical Engineering, Technical University of Darmstadt, 64287 Darmstadt, Germany

**\*** Correspondence: jardin@ims.tu-darmstadt.de (P.J.); esser@ims.tu-darmstadt.de (A.E.); rinderknecht@ims.tu-darmstadt.de (S.R.)

Received: 25 September 2020; Accepted: 28 September 2020; Published: 30 September 2020

Beside others, climate change and digitalization are trends of huge public interest, which highly influence the development process of future powertrain technologies. To handle these trends, new disruptive technologies are integrated into the development process. They open up space for diverse research, which is distributed over the entire vehicle design process. Recent research on this topic incorporates results for selecting and designing the powertrain topology [1–7] with their vehicle operating strategy [8,9], as well as results for handling the reliability of new powertrain components [10–12].

In [7], the optimal passenger car vehicle fleet transformation is developed based on lifecycle assessment. The results differ for short-range and long-range requirements, where battery electric vehicles are best for short-range. For longer distances, and at least until 2040, plug-in hybrid electric vehicles (PHEV) offer the greatest potential. In accordance with these results, ref. [6] optimizes an aftermarket hybridization kit, which can convert a combustion engine vehicle into a PHEV. Such kits may accelerate the passenger car vehicle fleet transition. With [1], a method for measuring non-volatile particle emissions is given. This method is highly relevant in the context of upcoming vehicle regulations within the transformation process.

In case of battery electric vehicles (BEV), ref. [5] investigates the influence of increasing the speed of the electric machine. Whilst this increases the system complexity through a higher gear ratio, a better power density of the overall powertrain system may be achieved. Different powertrain complexities in case of vehicles with dedicated hybrid transmissions are compared in [4]. The results show that a high complexity in the transmission may lead to a higher potential in reducing the vehicle's CO2 emissions. However, an increased powertrain complexity also increases the cost. In [2], a low-end multipurpose vehicle is proposed. Here, the combustion engine is designed only to meet the constant driving power requirements. On short distances, this vehicle drives purely electric. For longer distances, the constant driving power is delivered by the combustion engine.

In [3], the environmental and ecological benefits of a dual-battery BEV are considered. The authors propose to decrease the size of the Lithium-Ion battery and use a Zinc-Air battery pack as range extender. The results show that the vehicle cost can be reduced significantly by introducing a second, less costly battery.

PHEV offer new possibilities for intelligent operating strategies. A user-specific operating strategy that adapts during operating is proposed in [9]. The strategy uses supervised machine learning methods, and achieves lower consumption compared to a reference. Furthermore, new vehicle concepts, such as fuel cell vehicles, may introduce additional requirements regarding their operating strategy. In [8], an operating strategy for a fuel cell bus with a supercapacitor is developed based on real life data from London, UK. Here, it has to be assured that the capacitor is never over- or undercharged, and that the power requirements can be met.

Future powertrain technologies must be equipped with new and reliable system components. For example, in [12], condition monitoring and remaining useful lifetime estimation methods for

switching devices in the electric powertrain are developed. The authors validate their results with a 100 kW traction inverter. The overall system reliability also depends on the mechanical components. With [10], a systematic study of machine learning based reliability analysis methods is given. The authors integrate ensemble learning strategies into mechanical reliability estimations and compare the results that show the high potential of these methods. A crucial powertrain component is given by the clutch system. In [11], a novel method for clutch sensor fault diagnosis is developed and tested.

**Author Contributions:** All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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