TY - JOUR A1 - Azzam, Mohamed A1 - Sauer, Dirk Uwe A1 - Endisch, Christian A1 - Lewerenz, Meinert T1 - Comprehensive Analysis of Float Current Behavior and Calendar Aging Mechanisms in Lithium‐Ion Batteries JF - Batteries & Supercaps N2 - Aiming to quantify degradation currents from solid electrolyte interphase formation (ISEIgrowth) and gain of active lithium due to cathode lithiation (ICL), resulting from electrolyte decomposition, the float current behavior of lithium-ion batteries is investigated with different cathode materials. The float current, IFloat , represents the recharge current required to maintain the cell at a fixed potential during calendar aging. This current arises as lithium is irreversibly consumed at the anode or inserted into the cathode, shifting the electrode potentials. To account for the asymmetric response of the electrodes, a voltage-dependent scaling factor, SF, is introduced, derived from the slopes of the electrode-specific voltage curves. Using this factor in combination with measured float currents and capacity loss rates from check-up tests, ISEIgrowth and ICL is quantified at 30 °C across various float voltages. Although the SF and capacity data are limited to 30 °C, the model is extended to a range of 5–50 °C using only float current measurements. The results show that using capacity loss rates alone underestimate ISEIgrowth and that ICL, contributes significantly to the observed float current at elevated voltages, indicating that cathode lithiation plays an increasingly important role in high-voltage calendar aging. UR - https://doi.org/10.1002/batt.202500349 Y1 - 2025 UR - https://doi.org/10.1002/batt.202500349 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-68054 SN - 2566-6223 VL - 9 IS - 1 PB - Wiley CY - Weinheim ER - TY - CHAP A1 - Horn, Alexander A1 - Adam, Philip-Roman A1 - Schmidtner, Stefanie T1 - A Benchmark Dataset for Bus Travel and Dwell Time Prediction T2 - 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC60802.2025.11423733 Y1 - 2026 UR - https://doi.org/10.1109/ITSC60802.2025.11423733 SN - 979-8-3315-2418-0 SP - 2047 EP - 2054 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Langer, Robin A1 - Tentrup, Thomas A1 - Schweiger, Hans-Georg T1 - A Vehicle-in-the-Loop Approach for Front Camera Verification Using Adaptive High Beam JF - IEEE Open Journal of Intelligent Transportation Systems N2 - As automated driving functions based on environmental sensors become increasingly deployed, ensuring reliable performance over the vehicle lifetime is essential. Currently, verification is carried out through internal self-diagnostics, which do not always operate correctly, and periodic technical inspection, which assesses only the test criteria installation and condition. Test criteria for function and efficiency of environmental sensors are neither standardized nor routinely assessed, creating the need for new testing approaches. Previous low-cost research approaches defined a method and conducted experiments to verify a vehicle’s front camera by displaying visual stimuli and evaluating the high beam assist response. Whereas the camera’s function could be verified through a basic qualitative check, the approach did not enable a quantitative evaluation of its performance. The aim of this work was therefore to advance this approach and investigate the added value of a Vehicle-in-the-Loop test bench for front camera verification. Three tests were conducted. A supporting method was introduced to reproducibly detect and define the position of the headlight cutoff line, enabling consistent evaluation of the vehicle’s reaction. With static camera stimuli (Test I), the function of the front camera could be verified, and the influence of the vehicle geometry on the reaction was assessed. Dynamic stimuli (Test II) additionally enabled an efficiency evaluation, allowing quantitative comparison between vehicles. However, transferring the stimuli into a reproducible virtual simulation (Test III) remained challenging, as the vehicles under test did not respond consistently. Further research is required to refine and simplify the method toward a standardized periodic technical inspection procedure. UR - https://doi.org/10.1109/OJITS.2026.3672438 Y1 - 2026 UR - https://doi.org/10.1109/OJITS.2026.3672438 SN - 2687-7813 PB - IEEE CY - New York ER - TY - JOUR A1 - Rappsilber, Tim A1 - Krüger, Simone A1 - Raspe, Tina A1 - Reclo, Rudolf A1 - Schweiger, Hans-Georg T1 - Toxic gas emission in electric vehicles: What a battery fire means for occupant safety JF - Fire Safety Journal N2 - This work investigates the ability of occupants to escape from a battery electric vehicle during a thermal runaway of the traction battery initiated by nail penetration. Such events generate intense fires and large amounts of toxic gases, rapidly reducing the time available for safe evacuation. In controlled full-scale outdoor experiments on two identical mid-range battery electric vehicles, the smoke gas composition inside the cabin is examined. Using FTIR spectrometers and an oxygen analyzer, temporal and spatial concentrations of organic carbonates, hydrocarbons, hydrogen fluoride, hydrogen cyanide, acetylene, and oxygen are measured. Sampling locations include the driver's breathing zone, the right rear passenger's breathing zone, and the exterior right rear wheel housing. The study further evaluates occupant's escape capability using the fractional effective dose (FED) model in accordance with ISO 13571. Results show that toxic gases can reach harmful concentrations within minutes after smoke enters the cabin, though smoke entry times vary widely with fire progression. Differences in fire development and smoke dispersion strongly affect FED values and thus the time available for self-rescue. Overall, the findings provide an important basis for assessing occupant safety during battery electric vehicle fires and highlight the need for improved mitigation strategies. UR - https://doi.org/10.1016/j.firesaf.2026.104717 Y1 - 2026 UR - https://doi.org/10.1016/j.firesaf.2026.104717 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-67774 SN - 1873-7226 VL - 2026 IS - 162 PB - Elsevier CY - New York ER - TY - JOUR A1 - Pandey, Amit A1 - Mohd, Zubair Akhtar A1 - Veettil, Nandana Kappuva A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric JF - Scientific Reports N2 - Sharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of an automotive camera, which could be the first step toward state monitoring of automotive cameras. To achieve this, Principal Component Analysis (PCA) was performed, using synthetic kernels generated by Zemax. The PCA model was built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images were created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data were utilized for algorithm development, and later on, validation was performed on real-life data. The algorithm extracts two pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel was used to blur the reference ROIs, and the resulting SFR was compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel was evaluated against the true kernel for accuracy. The structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.99, and mean squared error (MSE) from to . Validation on real-life camera images showed that the SSIM between the estimated and blurred ROI was >0.82, showing promising accuracy in kernel estimation, which could be used towards in-field monitoring of camera sharpness degradation. UR - https://doi.org/10.1038/s41598-026-40556-w Y1 - 2026 UR - https://doi.org/10.1038/s41598-026-40556-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66915 SN - 2045-2322 VL - 16 IS - 1 PB - Springer Nature CY - London ER - TY - CHAP A1 - Dönmez, Ömer A1 - Tejero de la Piedra, Ricardo A1 - Klose, Simona A1 - Riolet, Matthieu A1 - Rozek, Lukas A1 - Vaculin, Ondrej A1 - Hach, Christian T1 - Approach for Passive Safety Assessment of Rearward-Sitting Occupants T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376179 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376179 SN - 978-1-6654-7778-9 SP - 445 EP - 452 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Funk Drechsler, Maikol A1 - Sell, Christoph Dominic A1 - Poledna, Yuri A1 - Huber, Werner T1 - Accelerating the Approval of Automated Driving Vehicles through standardized XiL test environments T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376566 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376566 SN - 978-1-6654-7778-9 SP - 183 EP - 188 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Ulreich, Fabian A1 - Funk Drechsler, Maikol A1 - Poledna, Yuri A1 - Chan, Pak Hung A1 - Herraren, Tuomas A1 - Ebert, Martin A1 - Kaup, André A1 - Huber, Werner T1 - Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving T2 - 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES65691.2025.11376043 Y1 - 2026 UR - https://doi.org/10.1109/ICVES65691.2025.11376043 SN - 978-1-6654-7778-9 SP - 57 EP - 64 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Da Silva Junior, Amauri A1 - Müller, Steffen A1 - Birkner, Christian A1 - Jazar, Reza A1 - Marzbani, Hormoz T1 - Fault Tolerant Control With Reinforcement Learning for Evasive Maneuvers Using a Scaled Vehicle JF - IEEE Access N2 - Autonomous vehicle controllers are responsible for handling the vehicle at all times in any situation, including emergency conditions. An emergency might arise from, e.g., adverse weather conditions, short-time detection, and system faults. In this paper, we develop a fault-tolerant controller to handle actuator faults for an over-actuated autonomous vehicle based on reinforcement learning. A worst-case scenario is selected for the controller development, involving short-time detection of the preceding objects, high velocity, and dry to wet road conditions. The design of the vehicle controller is performed in three steps. First, a robust controller based on sliding mode control with lateral and longitudinal coupled strategy was built to ensure stability in emergency scenarios. Secondly, a strategy was proposed to identify the most critical vehicle faults that might lead to a crash. Building on these foundations, this study extends the vehicle controller to handle vehicle faults with a reinforcement learning strategy, enabling adaptive and robust fault handling in complex fault scenarios. The vehicle controller is designed and optimized in IPG-Carmaker®, and proof of concept is carried out in a scaled 1:3.33 test vehicle. The results demonstrate the robustness of the proposed controller in an emergency single-lane change with a velocity of up to 130 km/h. Tests in the scaled vehicle demonstrate the vehicle controller’s accuracy against simulation, with the application of reinforcement learning strategy in real-case scenarios. UR - https://doi.org/10.1109/ACCESS.2026.3661179 Y1 - 2026 UR - https://doi.org/10.1109/ACCESS.2026.3661179 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66839 SN - 2169-3536 VL - 14 SP - 21353 EP - 21383 PB - IEEE CY - New York ER - TY - JOUR A1 - Kreilinger, Laurens A1 - Zott, Stefan A1 - Hemmert, Werner A1 - Karg, Sonja T1 - Dry electrode impedance: a new approach towards improved characterization JF - Biomedical Physics & Engineering Express N2 - Electrode-skin impedance plays a crucial role in electrophysiological signal acquisition, influencing signal quality and measurement reliability. We designed a reproducibility measurement setup, using a membrane with a saline solution and a three-electrode Electrochemical Impedance Spectroscopy measurement setup (range 1 Hz–20 kHz), to mimic the electrode-skin impedance. The system allowed controlled application of pressure to the working electrode (WE) and measurement of all setup parameters. With this setup, reproducible results were achieved, with a standard deviation of 5.5% of the mean impedance across three builds. Potentiostatic and impedance analyzer measurements with six types of dry electrodes produced comparable results, with an average error of 10%. The six dry electrode types exhibited impedance variations of up to a factor of 10,000 at low frequencies, depending on material and geometry. Ag/AgCl-coated electrodes exhibited an impedance reduction by a factor of 100 at 1 Hz compared to their uncoated counterparts. The proposed setup provides a standardized and reproducible approach for characterizing electrode impedance across different materials, coatings, and geometries. UR - https://doi.org/10.1088/2057-1976/ae3e9c Y1 - 2026 UR - https://doi.org/10.1088/2057-1976/ae3e9c UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66800 SN - 2057-1976 VL - 12 IS - 2 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Agrawal, Atharva Mangeshkumar A1 - Chaudhary, Arvind Kumar A1 - Gandamalla, Viswa A1 - Sheru, Aravind Reddy A1 - Chakraborty, Ashmita A1 - Dhirwani, Bhavesh Arjan A1 - Agarwal, Priyank T1 - Author-Oriented Semantic Plagiarism Detection Using Transformer Architectures T2 - 2025 IEEE International Conference on Advanced Computing Technologies (ICACT) UR - https://doi.org/10.1109/ICACT67549.2025.11351398 Y1 - 2026 UR - https://doi.org/10.1109/ICACT67549.2025.11351398 SN - 979-8-3315-9002-4 SP - 401 EP - 406 PB - IEEE CY - Piscataway ER -