Electromobility is also picking up speed in freight transport. This raises the question of the most versatile type of drive. Regardless of developments in the passenger car segment, this niche is often seen as a "safe haven" for hydrogen propulsion (H2). The reasons given for this are the insufficient range of battery trucks, charging times that are too long, and the reduced payload due to the heavy battery. While battery-electric vehicles have prevailed over H2 in private transport, there is still some debate in heavy-duty transport. But which type of drive will prevail in the long term? What role do range, infrastructure and, above all, economy play? We talk about this with Christopher Hecht from the Institute for Power Electronics and Electrical Drives at RWTH Aachen University and Member of the Project Hydrogen Compass.
Timeline:
The smarter E podcast is all about the current trends and developments in a renewable, decentralized and digital energy industry. Our moderators Tobias Bücklein and Zackes Brustik welcome and interview personalities who shape our industry and drive developments forward. A new episode is published every Thursday.
Click on the logo to listen to the podcast on the platform of your choice.
Christopher's research focuses on applying machine learning and Big Data algorithms to public charging infrastructure for electric vehicles. Key questions are how to best utilize the existing infrastructure, how to best organize future expansion, and how electric vehicles can interact with the power grid in a way that serves the system. Through the platform www.mobility-charts.de as well as through appearances at various conferences, he is a leading voice on the use of charging infrastructure in Germany.
The Institute for Power Electronics and Electrical Drives (ISEA) at RWTH Aachen University was founded in 1965 and has been headed by Prof. Dr. Rik W. De Doncker since 1996. Today, ISEA houses the chairs for Power Electronics and Electric Drives, for Electrochemical Energy Conversion and Storage Systems Technology, and for Battery Aging Processes and Lifetime Prediction. The aim is to optimize existing battery technologies for applications.