Volkswagen Group (Germany)

Control elements with active haptic feedback have become established in modern automotive user interfaces, but numerous media reports and studies indicate customer dissatisfaction with the current design. To investigate the customer preference that has not been well-characterized so far and to deduce whether differences in preference can be linked to a driving task or customer attributes, a Preliminary-Study with adjective rating, an Expert-Study with pairwise comparison, and a Customer-Study with an additional real driving task were conducted. The results show different customer preference groups, with the vast majority favoring short haptic feedback within 13.6 ms and 20.6 ms length. A driving task does not influence preference, nor is the preference affected by attributes such as gender, age, or thumb size. These findings can be used to optimize active haptic feedback according to customer preferences. As a result, well-designed haptics can increase customer satisfaction and the perceived value of automotive controls.

The evolution of Industry 4.0 standards and trends introduces new challenges for traditional Manufacturing Execution Systems, necessitating their transformation into modular, interconnected components with standardized communication interfaces. Although these trends provide a conceptual framework, a distributed architecture for monitoring and aggregating production data remains underdeveloped. This paper addresses this gap by proposing a system of distributed monitoring components that interact hierarchically and heterarchically using Industry 4.0 technologies. The approach integrates Asset Administration Shells for passive data representation with Multi-Agent Systems and investigates its applicability for different control system architectures in different case studies.

This paper presents the first-ever analysis of the switching performance of Gallium Nitride (GaN) Current Aperture Vertical Transistors (CAVET). The effect of current aperture length in CAVETs on intrinsic device capacitances was investigated to reduce switching losses and make the device more efficient for fast-switching power applications. The results demonstrate that as the aperture length increases from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \mu \mathrm{m}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16 \mu \mathrm{m}$</tex>, the gate-to-source capacitance (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{\text{G S}}$</tex>) and drain-to-source capacitance (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{\text{D S}}$</tex>) reduce by 93 % and 82 %, respectively, while the gate-drain capacitance (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{\text{G D}}$</tex>) increases <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{3 0}$</tex> times which compensating for the switching frequencies. In addition, the switching characteristics illustrate that the peak reverse recovery current of the CAVET decreases from 0.92 A to 0.16 A when the aperture expands from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \mu \mathrm{m}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16 \mu \mathrm{m}$</tex>. As a result, the switching loss for CAVET with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16 \mu \mathrm{m}$</tex> aperture is reduced to 131.3 nJ, which is half of that for the device with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \mu \mathrm{m}$</tex> aperture. To further evaluate performance, the highest switching figure of merit (FOMsw) of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.79 \text{GHz} / \text{cm}^{2}$</tex> is observed for the CAVET with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16 \mu \mathrm{m}$</tex> aperture length, while the lowest value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.74 \text{GHz} / \text{cm}^{2}$</tex> is found for the CAVET with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 \mu \mathrm{m}$</tex> aperture length.

Electromagnetic interference (EMI) from high-voltage electric drive systems in electric vehicles poses significant challenges for electromagnetic compatibility (EMC). To ensure meaningful results, EMC testing should replicate real vehicle configurations and load conditions, offering early insights into compliance tests with legal and internal standards. This paper introduces a novel time-domain measurement method that captures 16 simultaneous signals to analyze current and voltage paths, including common-mode (CM) and differential-mode (DM) behavior. Supporting frequencies up to 100 MHz, the method provides a comprehensive snapshot of the system’s electromagnetic behavior under defined load conditions. This enables detailed analysis of EMI coupling mechanisms, supports simulation validation, and enhances early-stage development. Examples of applications demonstrate their effectiveness in identifying EMI sources and improving model accuracy. As a feasibility study, the results are experimental but promising, aligning with the growing trend of using time-domain methods to complement traditional frequency-domain EMC analysis [1].

Abstract We present two novel contributions for achieving and assessing quantum advantage in solving difficult optimisation problems, both in theory and foreseeable practice. (1) We introduce the “Quantum Tree Generator” to generate in superposition all feasible solutions of a given 0-1 knapsack instance; combined with amplitude amplification, this identifies optimal solutions. Assuming fully connected logical qubits and comparable quantum clock speed, QTG offers perspectives for runtimes competitive to classical state-of-the-art knapsack solvers for instances with only 100 variables. (2) By introducing a new technique that exploits logging data from a classical solver, we can predict the runtime of our method way beyond the range of existing quantum platforms and simulators, for benchmark instances with up to 600 variables. Under the given assumptions, we demonstrate the QTG’s potential practical quantum advantage for such instances, indicating the promise of an effective approach for hard combinatorial optimisation problems.

The exponential growth of global electric vehicle deployment has precipitated a critical need for the sustainable recycling of end-of-life lithium-ion batteries (LIBs), particularly nickel–cobalt–manganese (NCM) ternary cathodes, which dominate the retired battery stream. This study establishes an integrated Aspen Plus-based hydrometallurgical process model, focusing on “acid dissolution–LiOH precipitation–electrolysis” for closed-loop NCM recycling. Gibbs reactor-based dissolution kinetics is used for selective metal leaching (achieving &gt; 99% efficiency at 185 kg/h acid flow), the thermodynamic prioritization of sequential hydroxide precipitation (Co → Ni → Mn at 10–60 kg/h LiOH), and the electrochemical regeneration of LiOH/H2SO4 from Li2SO4 (70.01 kg/h LiOH at 0.8 conversion). Material balance analysis confirms a net production of 10.01 kg LiOH per 100 kg of NCM feedstock with 41.87 kg of acid consumption, while the energy of electrolysis power is 452.96 kW at 6 V/1360 A/m2. This work provides a techno-economic framework for industrial-scale battery recycling.

Due to the shift in the automotive industry towards lower‐emission production, the use of green steels is constantly expanding. Today, green steels can be produced from recycled steel. However, the increase in tramp elements resulting from the recycling of steel in the electric arc furnace (EAF) route is problematic. These tramp elements can lead to a negative influence on the mechanical properties, particularly relevant in the case of resistance spot welding sheets. To understand the influence, the welding properties of three different green steels used in the automotive industry are characterized. Thereby, the potential limits of the use of recycled steels are shown.

This paper presents an approach for feedforward disturbance rejection for open or closed loop controlled plants combining the Machine Learning (ML) perspective on supervised learning with basic paradigms of the active noise cancelling (ANC) framework. In contrast to a broad amount of the proposed methodology in this area, neither a linear primary path nor a time invariant secondary path are assumed. Moreover, the disturbance source has a multi-channel structure, increasing complexity to the considered setup. The proposed methodology applies a Neural Network (NN) based feedforward controller which can be optimized iteratively or in closed form depending on the network structure. A two-staged approach is presented, transforming the control problem into a supervised learning setting and applying well-known methods of system identification and Machine Learning. This simple but effective procedure bridges the gap between supervised learning and ANC methods and highlights new fields of application for the subsequent combined approach. An experimental evaluation on the Air-Fuel-Ratio (AFR) control of a spark ignited engine is provided to demonstrate the practicability and effectiveness of the proposed methodology.