IAV
  • Berlin, Germany
Recent publications
This work presents a costing and emissions analysis of long-haul battery electric trucks (BETs) with overnight charging for the U.S. market. First, we compute the energy requirements of a long-haul truck for a 600-mile (966 km) real-world driving range and perform battery sizing. The battery sizes are used along with a fleet-replacement model and the U.S. payload distribution to compute payload losses for two different chemistries, Nickel-Manganese-Cobalt (NMC) and Lithium-Iron-Phosphate (LFP). Given present battery energy densities, BET fleets will require 1.06 and 1.27 times the trucks of a diesel fleet to provide the same cargo capacity. Next, we perform electricity pricing analysis for high-power applications. Our baseline scenario estimates a price of 0.32 USD/kWh, and it only decreases to 0.15 USD/kWh for the optimistic scenario. Currently, we compute the total cost of ownership for BETs to be more than twice (>2x) that of diesel trucks, however, the price premium is projected to decrease significantly to 1.2x in the long term. BETs could become economically competitive with diesel if the delivered cost of electricity for high-power applications drops below 0.1 USD/kWh, and if we realize projected improvements in battery energy density and cost. Our emissions analysis shows negligible present-day greenhouse gas (GHG) benefits from switching to BETs, primarily due to the carbon intensity of electricity generation. In the long term, we project BETs to have 40% less GHG emissions than diesel. Today, BETs are not well-suited for the long-haul trucking sector. However, our sensitivity analysis shows that operating with battery swapping and short-haul applications could potentially benefit from electrification, hence we encourage further investigation. Our analysis framework is provided as a Google Colab Notebook that can be modified to assist these needed future studies.
Autonomous driving is no longer science fiction but a rapidly developing reality. In this article, we will take a look at Level 4 autonomy, a significant milestone on the path to fully autonomous vehicles. We will examine what is necessary to achieve this level, why a digital twin is currently indispensable, and how it is integrated into development and implementation. We will also discuss the current challenges and provide an outlook on the future of autonomous driving.
This paper introduces ALICE (Automated Logic for Identifying Contradictions in Engineering), a novel automated contradiction detection system tailored for formal requirements expressed in controlled natural language. By integrating formal logic with advanced large language models (LLMs), ALICE represents a significant leap forward in identifying and classifying contradictions within requirements documents. Our methodology, grounded on an expanded taxonomy of contradictions, employs a decision tree model addressing seven critical questions to ascertain the presence and type of contradictions. A pivotal achievement of our research is demonstrated through a comparative study, where ALICE’s performance markedly surpasses that of an LLM-only approach by detecting 60% of all contradictions. ALICE achieves a higher accuracy and recall rate, showcasing its efficacy in processing real-world, complex requirement datasets. Furthermore, the successful application of ALICE to real-world datasets validates its practical applicability and scalability. This work not only advances the automated detection of contradictions in formal requirements but also sets a precedent for the application of AI in enhancing reasoning systems within product development. We advocate for ALICE’s scalability and adaptability, presenting it as a cornerstone for future endeavors in model customization and dataset labeling, thereby contributing a substantial foundation to requirements engineering.
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71 members
Fabian Utesch
  • Lighting Functions
Rafika Hajji
  • College of Geomatic Sciences and Surveying Engineering
Marco Moser
  • Powertrain Research
Sebastian Hentzelt
  • Control Engineering Excellence Cluster
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Berlin, Germany