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Circular Economy Enabled by Digitization: Digital networking in the procurement of manufacturing companies
No full textCurrent developments in digitalization and data economy, especially multilateral data sharing platforms, offer the potential to accelerate the implementation of circular economy practices in the manufacturing industry. This article systematically examines the extent to which digitalization could serve as a catalyst for circular economy in the procurement of such companies. As a basis for the following research, eight experts from five leading global manufacturers and suppliers in the automotive and aviation industries were interviewed. This article demonstrates practical hypotheses for the sustainable design of supply chains and proposes two specific use cases for circular economy practices that can proactively counteract the use of resources
Machine learning estimation of battery state of health in residential photovoltaic systems
No full textAs the global adoption of residential battery storage systems paired with local photovoltaic (PV) generation increases, prosumers are increasingly motivated to reduce both their electricity costs and dependence on the grid. This shift highlights the importance of accurately evaluating and predicting the battery's State of Health (SOH) and Remaining Useful Life (RUL). These factors are crucial for determining the operational costs and longevity of battery systems. Traditionally, SOH predictions have relied heavily on detailed measurement data and time-intensive simulations. In response, we introduce a new AI-based approach that simplifies SOH estimation. Our method, named "ML Battery Life Predictor (MLBatLife)," leverages forecasted or historical PV generation data and load consumption patterns to quickly forecast the SOH for various battery configurations. Tested on simulated data, this tool demonstrated a high accuracy, with a coefficient of determination of 0.986 for predictions one day ahead, and an impressively low average error of 0.1 % for projections five years into the future. This innovative AI-driven technique offers substantial benefits for evaluating the economic viability and warranty parameters of battery installations in different regions. It provides a valuable resource for both industry stakeholders and energy system planners aiming to assess and anticipate battery health outcomes efficiently
Path Planning for Autonomous Vehicles: Implementing an Occupancy Grid and Artificial Potential Fields in a Euro NCAP Simulation based on ASAM OSI
No full textThis article investigates path planning strategies for autonomous vehicles in critical pedestrian scenarios, using a digital presentation of a real scenario based on the ASAM Open Simulation Interface® (OSI) standard. We present a comparative study of two decision-making algorithms -an Occupancy Grid Method (OG) and an Artificial Potential Field Method (APF) -applied to the Euro NCAP CPNCO-50 scenario, a critical use case for autonomous emergency braking systems. Simulations are implemented using OSI to enable modular and standardized integration across simulation platforms.
The OG Method reacts preemptively to potential collisions by detecting obstacles within a discretized environment model, initiating early evasive maneuvers and offering conservative, safety-oriented responses. In contrast, the APF Method adapts dynamically by modeling repulsive risk potentials, resulting in behavior more similar to that of human drivers.
The framework allows parameter tuning to reflect different driving styles and can incorporate Predictive Potential Field Method (PPF) that anticipate future trajectories. This enables efficient algorithm comparison and iteration. Real-world scenarios can be resimulated using OSI trace file to validate virtual performance against physical tests
Customer satisfaction of driver assistance systems: challenges and opportunities in safety development for future driver assistance, automated and autonomous driving systems
No full textPerceived reliability, safety and comfort benefits are key factors of customer satisfaction with advanced driver assistance systems. Satisfaction again is linked to technology trust, acceptance and diffusion. Therefore, it plays a pivotal role for advancing road safety and for the future market penetration for advanced driver assistance systems of level 3 and beyond. By understanding the interplay between customer expectations, satisfaction, and behaviour, manufacturers can refine systems to address not just convenience but also critical safety concerns. Empirical results underscore that improving customer satisfaction directly contributes to higher trust, usage, and thus increased road safety, aligning with FAST-zero's mission of achieving zero accidents.
Customer satisfaction results from a subjective comparison of expectations and experiences. With the help of the disconfirmation paradigm (Confirmation/Disconfirmation paradigm), customer expectations can be identified and characterized. The matching process is subjective, as cognitive and affective factors influence the resulting satisfaction -the customer draws on existing experience, information and knowledge. Affectiveemotional driven factors in the context of driver assistance systems are for example, the perceived feeling of safety or the enjoyment of driving.
This conference paper is based on an extensive quantitative survey (sample: 609 participants in Germany), which was supplemented with qualitative interviews. It presents the survey results on automatic distance control and the lane keeping assistant
Einordnung von Führung in einer digitalen Welt - KI erweitert die Spielregeln in Zeiten von New Work
No full text
Robots as Coaches: Exploring User Expectations, Ethics, and Design Guidelines
No full textThis study explores the use of socially assistive robots (SARs) for behavioural coaching for healthy habit formation. We conducted four focus group discussions with nineteen end users to understand their needs and expectations for SAR coaches. We performed a thematic and narrative analysis of the data collected. Our findings emphasise the significance of SARs in assisting individuals and equipping them with the skills for independent health management after the intervention ends. The design requirements generated are centred around interaction, ethics, and environment and are justified by linking them with established behavioural theories. These requirements will help guide the development of robotic interventions that support long-term habit formation
A Methodology for Verifying and Validating the Functional Performance Evaluation of High-Fidelity RADAR Sensor Models at Raw Data and Detection Levels
No full textThis paper outlines a comprehensive methodology for verifying and validating the functional performance evaluation of high-fidelity radio detection and ranging (RADAR) sensor models at both the raw data level, specifically the range map (RM) and range-Doppler map (RDM), and the detection level. Three key performance parameters (KPPs) are defined at the raw data level: the target's received power, signal-to-noise ratio (SNR), and twoway beam pattern. Four KPPs are identified at the detection level: distance, relative radial velocity, azimuth angle, and elevation angle. This study also introduces three dynamic test scenarios directly applicable to the functional evaluation assessment of RADAR sensor models. Furthermore, a toolchain is presented to integrate real-world test scenarios into the virtual environment, allowing for frame-by-frame validation of the RADAR sensor models' functional evaluation. Quantitative analysis successfully assesses differences between simulations and real measurements, employing the mean absolute percentage error (MAPE) metric. This methodology has been successfully applied to a high-fidelity RADAR sensor model featuring a multiple input and multiple output (MIMO) 2D linear spacing virtual antenna and a complete signal processing toolchain that mimics real RADAR sensors. Results show a strong correlation between the simulation and real measurements, with MAPE values below 7 % for all defined KPPs at raw data and detection levels. Using this methodology, developers can assess whether virtual RADAR sensors are ready for environmental perception during scenario-based automated driving system (ADS) testing
Performance assessment of sCO2- and organic fluid based cycles integrated with LNG gasification plants
No full textIn recent years, the role of natural gas in the global energymix has significantly increased, contributing 24.7% to theoverall primary energy supply in 2020, due to its importance in the energy transition towards decarbonization. This rise in consumption has led to a substantial growth in interregional trade, with Liquefied Natural Gas (LNG) surpassing pipelines as the primary transportation method. In 2020, LNG accounted for 52% of global natural gas trade, up from 41% in 2010. Efficient operation of LNG regasification terminals is now crucial for both environmental and economic reasons, particularly regarding the recovery of cold energy typically wasted during regasification, where LNG is stored at approximately −160 °C and ambient pressure. This study investigates the integration of LNG regasification with thermodynamic cycles that exploit the available cold energy during the working fluid’s condensation. Two cycle categories are considered: a low-temperature cycle using seawater as a heat source, and a high-temperature cycle using exhaust gases from a gas turbine powered by a portion of the regasified natural gas. CO₂ and R125 are selected as working fluids, with CO₂ analyzed under both subcritical and supercritical conditions. The system’s performance is evaluated as a function of the regasification and distribution pressures, with a turbine installed to recover energy from the pressure difference. A medium-sized regasification terminal (50 kg/s) is analyzed, achieving an integrated cycle power output between 3 and 35MW, depending on the heat source and working fluid. The integrated gas turbine operates at around 70–80 MW. Dedicated models have been developed in the Matlab environment simulate the regasification process, topping cycles, and their energy integration
