TY - CHAP A1 - Steinberger, Fabian A1 - Mohan, Nihesh A1 - Rämer, Olaf A1 - Elger, Gordon T1 - Low temperature die-attach bonding using copper particle free inks T2 - 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings UR - https://doi.org/10.1109/ESTC60143.2024.10712150 Y1 - 2024 UR - https://doi.org/10.1109/ESTC60143.2024.10712150 SN - 979-8-3503-9036-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Agrawal, Shiva A1 - Song, Rui A1 - Kohli, Akhil A1 - Korb, Andreas A1 - Andre, Maximilian A1 - Holzinger, Erik A1 - Elger, Gordon ED - Ploeg, Jeroen ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Concept of Smart Infrastructure for Connected Vehicle Assist and Traffic Flow Optimization T2 - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - The smart infrastructure units can play a vital role to develop smart cities of the future and in assisting automated vehicles on the road by providing extended perception and timely warnings to avoid accidents. This paper focuses on the development of such an infrastructure unit, that is specifically designed for a pedestrian crossing junction. It can control traffic lights at the junction by real-time environment perception through its sensors and can optimize the flow of vehicles and passing vulnerable road users (VRUs). Moreover, it can assist on-road vehicles by providing real-time information and critical warnings via a v2x module. This paper further describes different use-cases of the work, all major hardware components involved in the development of smart infrastructure unit, referred to as an edge, different sensor fusion approaches using the camera, radar, and lidar mounted on the edge for environment perception, various modes of communication including v2x, system design UR - https://doi.org/10.5220/0011068800003191 KW - Road Side Unit (RSU) KW - Smart Infrastructure KW - Radar KW - LiDAR KW - Camera KW - Perception KW - Sensor Data Fusion KW - Vehicle-to-everything (V2X) KW - Communication KW - Simulation KW - ITS Y1 - 2022 UR - https://doi.org/10.5220/0011068800003191 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30926 SN - 978-989-758-573-9 SN - 2184-495X SP - 360 EP - 367 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Streck, Egor A1 - Herschel, Reinhold A1 - Wallrath, Patrick A1 - Sunderam, M. A1 - Elger, Gordon ED - Prasad, Venkatesha ED - Pesch, Dirk ED - Ansari, Nirwan ED - Benavente-Peces, César T1 - Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops T2 - Proceedings of the 11th International Conference on Sensor Networks N2 - This paper presents the joint work from the “HORIS” project, with a focus on pedestrian detection at bus-stops by radar sensors mounted in the infrastructure to support future autonomous driving and protecting pedestrians in critical situations. Two sensor systems are investigated and evaluated. The first based on single radar sensor phase-sensitive raw data analysis and the second based on sensor data fusion of cluster data with two radar sensors using neural networks to predict the position of pedestrians. UR - https://doi.org/10.5220/0010777100003118 KW - Sensor Data Fusion KW - Radar Sensor KW - Multiple-Sensor Systems KW - Machine Learning Y1 - 2022 UR - https://doi.org/10.5220/0010777100003118 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30953 SN - 978-989-758-551-7 SN - 2184-4380 SP - 89 EP - 96 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Azzam, Mohamed A1 - Ehrensberger, Moritz A1 - Endisch, Christian A1 - Sauer, Dirk Uwe A1 - Lewerenz, Meinert T1 - Comparison of dU/dQ, Voltage Decay, and Float Currents via Temperature Ramps and Steps in Li‐ion Batteries JF - Batteries & Supercaps UR - https://doi.org/10.1002/batt.202400627 Y1 - 2024 UR - https://doi.org/10.1002/batt.202400627 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53655 SN - 2566-6223 VL - 8 IS - 1 PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Paarmann, Sabine A1 - Schreiber, Markus A1 - Chahbaz, Ahmed A1 - Hildenbrand, Felix A1 - Stahl, Gereon A1 - Rogge, Marcel A1 - Dechent, Philipp A1 - Queisser, Oliver A1 - Frankl, Sebastian Dominic A1 - Morales Torricos, Pablo A1 - Lu, Yao A1 - Nikolov, Nikolay I. A1 - Kateri, Maria A1 - Sauer, Dirk Uwe A1 - Danzer, Michael A. A1 - Wetzel, Thomas A1 - Endisch, Christian A1 - Lienkamp, Markus A1 - Jossen, Andreas A1 - Lewerenz, Meinert T1 - Short‐Term Tests, Long‐Term Predictions – Accelerating Ageing Characterisation of Lithium‐Ion Batteries JF - Batteries & Supercaps N2 - AbstractFor the battery industry, quick determination of the ageing behaviour of lithium‐ion batteries is important both for the evaluation of existing designs as well as for R&D on future technologies. However, the target battery lifetime is 8–10 years, which implies low ageing rates that lead to an unacceptably long ageing test duration under real operation conditions. Therefore, ageing characterisation tests need to be accelerated to obtain ageing patterns in a period ranging from a few weeks to a few months. Known strategies, such as increasing the severity of stress factors, for example, temperature, current, and taking measurements with particularly high precision, need care in application to achieve meaningful results. We observe that this challenge does not receive enough attention in typical ageing studies. Therefore, this review introduces the definition and challenge of accelerated ageing along existing methods to accelerate the characterisation of battery ageing and lifetime modelling. We systematically discuss approaches along the existing literature. In this context, several test conditions and feasible acceleration strategies are highlighted, and the underlying modelling and statistical perspective is provided. This makes the review valuable for all who set up ageing tests, interpret ageing data, or rely on ageing data to predict battery lifetime. UR - https://doi.org/10.1002/batt.202300594 Y1 - 2024 UR - https://doi.org/10.1002/batt.202300594 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51546 SN - 2566-6223 VL - 7 IS - 11 PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Kiran, Iqra A1 - Azzam, Mohamed A1 - Endisch, Christian A1 - Butt, Nauman Zafar A1 - Lewerenz, Meinert T1 - Evaluation of Calendar Aging in Cells with Graphite: Silicon Anode Using Float Current Analysis Under the Influence of Voltage Hysteresis JF - Journal of The Electrochemical Society N2 - This study investigates the calendar aging of lithium-ion batteries with graphite-silicon anodes using float current analysis. While float current analysis is already a proven method for assessing aging in cells with graphite-based anodes, the presence of silicon introduces additional complexities due to its voltage hysteresis. We address this by comparing the results for the scaling factor separately for charge and discharge. The scaling factor is initially derived from charge and discharge GITT measurements on fresh cells, including an aging-induced shift among both electrode curves. This approach enables quantification of SEI growth ISEI growth, and cathode lithiation current ICL bridging measured results for float currents with capacity loss rate. As a result, the scaling factor during charge delivered the most meaningful results regarding fitted aging currents. By extending the estimation method based on the Arrhenius equation across temperatures from 5 °C to 50 °C, our model is validated against measured float currents, improving the predictive accuracy of long-term aging trends in silicon-containing anodes. Electrochemical impedance spectroscopy provided further insights into degradation mechanisms, revealing a strong correlation between cathode lithiation by salt decomposition and resistance increase at high voltages (⩾4.15 V), confirmed by pulse tests at 100% SOC showing a sharp resistance increase at elevated voltages. UR - https://doi.org/10.1149/1945-7111/ae0fe8 Y1 - 2025 UR - https://doi.org/10.1149/1945-7111/ae0fe8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63053 SN - 1945-7111 VL - 172 IS - 10 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Conti, Fosca A1 - Lux, Kerstin A1 - Bhogaraju, Sri Krishna A1 - Liu, E A1 - Lenz, Christoph A1 - Seitz, Roland A1 - Elger, Gordon ED - Baldini, Francesco ED - Homola, Jiri ED - Lieberman, Robert A. T1 - Raman spectroscopy to investigate gallium nitride light emitting diodes after assembling onto copper substrates T2 - Optical Sensors 2021 UR - https://doi.org/10.1117/12.2591947 Y1 - 2021 UR - https://doi.org/10.1117/12.2591947 SN - 978-1-5106-4379-6 N1 - Poster + Paper PB - SPIE CY - Bellingham ER - TY - JOUR A1 - Kohler, Markus A1 - Mitsios, Dionysios A1 - Endisch, Christian T1 - Reconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity JF - Journal of Manufacturing Systems N2 - Manufacturing wound rotor synchronous machines (WRSMs) for electric vehicle traction systems necessitates rigorous quality inspection to ensure optimal product performance and efficiency. This paper presents a novel visual anomaly detection method for monitoring the needle winding process of WRSMs, utilizing unsupervised learning with convolutional autoencoders (CAEs) and the structural similarity index measure (SSIM). The method identifies deviations from the desired orthocyclic winding pattern during each stage of the winding process, enabling early detection of winding errors and preventing resource wastage and potential damage to the product or winding machinery. Trajectory-synchronized frame extraction aligns the visual inspection system with the winding trajectory, ensuring precise monitoring traceable to a specific point in the winding process. We present the comprehensive Winding Anomaly Dataset (WAD), which comprises images of WRSM rotor prototypes with and without winding faults recorded in different lighting conditions. The proposed reconstruction-based anomaly detection technique is trained on fault-free data only and utilizes the introduced masked mean structural dissimilarity index measure (MMSDIM) to focus on the relevant sections of the winding during inference. Comprehensive comparative analysis reveals that the CAE with unregularized latent space and the maximum mean discrepancy Wasserstein autoencoder (MMD-WAE) outperform the beta variational autoencoder (beta-VAE) in terms of anomaly detection performance, with the CAE and WAE delivering comparable results. Extensive testing confirms the approach’s effectiveness, achieving 95.6 % recall at 100 % precision, an AUROC of 99.9 %, and an average precision of 99.1 % on the challenging WAD, considerably outperforming state-of-the-art visual anomaly detection models. This work thus offers a robust solution for WRSM production quality monitoring and promotes the incorporation of visual inspection in electric drive manufacturing systems. UR - https://doi.org/10.1016/j.jmsy.2024.12.005 Y1 - 2024 UR - https://doi.org/10.1016/j.jmsy.2024.12.005 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-55371 SN - 1878-6642 VL - 2025 IS - 78 SP - 410 EP - 432 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection JF - Scientific Reports N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.1038/s41598-025-23019-6 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-23019-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63458 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection T2 - Research Square N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61996 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Schmid, Michael A1 - Vögele, Ulrich A1 - Endisch, Christian T1 - A novel matrix-vector-based framework for modeling and simulation of electric vehicle battery packs JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2020.101736 KW - Electric vehicle KW - Lithium-ion battery pack KW - Control-oriented modeling KW - Electro-thermal model KW - Fault diagnosis KW - Performance analysis Y1 - 2020 UR - https://doi.org/10.1016/j.est.2020.101736 SN - 2352-1538 VL - 2020 IS - 32 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Michael A1 - Endisch, Christian T1 - Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis JF - Journal of Energy Storage N2 - Safe operation of large battery storage systems requires advanced fault diagnosis that is able to detect faults and provide an early warning in the event of a fault. Since Internal Short Circuits (ISC) are the most common abuse condition leading to thermal runaway, this study addresses the early detection of incipient soft ISCs at the stage when the fault is still uncritical and does not lead to significant heat generation. The differences in cell voltages as measured by conventional battery management systems prove to be indicative features for ISC diagnosis. However, due to poor balancing and parameter variations, the cell voltage differences exhibit nonlinear variations. This work addresses this challenge with a nonlinear data model based on Kernel Principal Component Analysis (KPCA). To enable an online application in a vehicle, the present work reduces the computational complexity of the method by an optimal choice of training data. An analysis of the contribution of each cell to the fault statistics enables identification of the faulty cell. Since early-stage ISCs can exhibit a wide range of short-circuit resistances, experimental validation is performed with resistances from 10Ω to 10kΩ, which are correctly detected and isolated by the optimized cross-cell monitoring in all cases. UR - https://doi.org/10.1016/j.est.2022.104815 KW - lithium-ion battery KW - internal short circuit KW - fault diagnosis KW - fault isolation KW - kernel principal component analysis KW - battery safety Y1 - 2022 UR - https://doi.org/10.1016/j.est.2022.104815 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-25282 SN - 2352-1538 VL - 2022 IS - 53 PB - Elsevier CY - Amsterdam ER -