@article{SchmidEndisch2022, author = {Schmid, Michael and Endisch, Christian}, title = {Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis}, volume = {2022}, pages = {104815}, journal = {Journal of Energy Storage}, number = {53}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2022.104815}, year = {2022}, abstract = {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.}, language = {en} } @article{LewerenzRaheFuchsetal.2020, author = {Lewerenz, Meinert and Rahe, Christiane and Fuchs, Georg and Endisch, Christian and Sauer, Dirk Uwe}, title = {Evaluation of shallow cycling on two types of uncompressed automotive Li(Ni1/3Mn1/3Co1/3)O2-Graphite pouch cells}, volume = {2020}, pages = {101529}, journal = {Journal of Energy Storage}, number = {30}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2020.101529}, year = {2020}, language = {en} } @article{KleinerStuckenbergerKomsiyskaetal.2021, author = {Kleiner, Jan and Stuckenberger, Magdalena and Komsiyska, Lidiya and Endisch, Christian}, title = {Real-time core temperature prediction of prismatic automotive lithium-ion battery cells based on artificial neural networks}, volume = {2021}, pages = {102588}, journal = {Journal of Energy Storage}, number = {39}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2021.102588}, year = {2021}, language = {en} } @article{KleinerHeiderKomsiyskaetal.2021, author = {Kleiner, Jan and Heider, Alexander and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Thermal behavior of intelligent automotive lithium-ion batteries: Experimental study with switchable cells and reconfigurable modules}, volume = {2021}, pages = {103274}, journal = {Journal of Energy Storage}, number = {44, Part A}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2021.103274}, year = {2021}, language = {en} } @article{SchmidKleinerEndisch2022, author = {Schmid, Michael and Kleiner, Jan and Endisch, Christian}, title = {Early detection of Internal Short Circuits in series-connected battery packs based on nonlinear process monitoring}, volume = {2022}, pages = {103732}, journal = {Journal of Energy Storage}, number = {48}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2021.103732}, year = {2022}, language = {en} } @article{KleinerLechermannKomsiyskaetal.2021, author = {Kleiner, Jan and Lechermann, Lorenz and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Thermal behavior of intelligent automotive lithium-ion batteries}, volume = {2021}, pages = {102686}, journal = {Journal of energy storage}, subtitle = {operating strategies for adaptive thermal balancing by reconfiguration}, number = {40}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2021.102686}, year = {2021}, language = {en} } @article{SchneiderLiebhartEndisch2021, author = {Schneider, Dominik and Liebhart, Bernhard and Endisch, Christian}, title = {Active state and parameter estimation as part of intelligent battery systems}, volume = {2021}, pages = {102638}, journal = {Journal of Energy Storage}, number = {39}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2021.102638}, year = {2021}, language = {en} } @article{SchmidZippeliusHanssetal.2023, author = {Schmid, Maximilian and Zippelius, Andreas and Hanß, Alexander and B{\"o}ckhorst, Stephan and Elger, Gordon}, title = {Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part II - Reliability}, volume = {23}, journal = {IEEE Transactions on Device and Materials Reliability}, number = {3}, publisher = {IEEE}, address = {New York}, issn = {1558-2574}, doi = {https://doi.org/10.1109/TDMR.2023.3300355}, pages = {419 -- 429}, year = {2023}, language = {en} } @article{SchmidZippeliusHanssetal.2022, author = {Schmid, Maximilian and Zippelius, Andreas and Hanss, Alexander and B{\"o}ckhorst, Stephan and Elger, Gordon}, title = {Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part I - Initial Characterization}, volume = {22}, journal = {IEEE Transactions on Device and Materials Reliability}, number = {2}, publisher = {IEEE}, address = {New York}, issn = {1530-4388}, doi = {https://doi.org/10.1109/TDMR.2022.3152590}, pages = {175 -- 186}, year = {2022}, abstract = {Thermo-mechanical reliability is one major issue in solid-state lighting. Mismatches in the coefficients of thermal expansion (CTE) between high-power LED packages and substrates paired with temperature changes induce mechanical stress. This leads to a thermal degradation of LED modules by crack formation in the solder interconnect and/or delamination in the substrate, which in turn increases junction temperature and thus decreases light output and reduces lifetime. To investigate degradation and understand influence of LED package design and solder material, a reliability study with a total of 1800 samples - segmented in nine LED types and five solder pastes - is performed. First of all, in this paper a state-of-the-art review of high-power LED packages is performed by analyzing and categorizing the packaging technologies. Second, the quality inspection after assembly is realized by transient thermal analysis (TTA), scanning acoustic microscopy (SAM) and X-ray. For TTA, a new method is introduced to separate the thermal resistance of the LED package from solder interconnect and substrate by applying the transient dual interface method (TDI) on samples with different solder interconnect void ratios. Further measurement effort is not required. The datasheet values for thermal resistance are verified and the different LED package types are benchmarked. The void ratio of the solder interconnects is determined by X-ray inspection combined with an algorithm to suppress disruptive internal LED package structures. TTA and TDI revealed that initial thermal performance is independent of solder paste type and that voiding is more critical to smaller LED packages. In addition, lower silver proportion in the paste is found to increase voiding. SAM is less sensitive for initial void detection than X-ray, but it's applied to monitor crack propagation while aging in combination with TTA. The results of the reliability study, i.e., the crack growth under temperature shock test for the different SAC solders, will be presented in a second independent paper.}, language = {en} } @article{MohdSchmidZippeliusetal.2024, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Zippelius, Andreas and Elger, Gordon}, title = {Solder joint lifetime model using AI framework operating on FEA data}, volume = {2025}, pages = {109032}, journal = {Engineering Failure Analysis}, number = {167, Part B}, publisher = {Elsevier}, address = {Oxford}, issn = {1350-6307}, doi = {https://doi.org/10.1016/j.engfailanal.2024.109032}, year = {2024}, abstract = {The thermo-mechanical reliability of electronic systems is often limited by the crack growth within the solder joints. Addressing this issue requires careful consideration of the design of the package and solder pads. Finite Element Analysis (FEA) is widely used to predict crack growth and to model their lifetime. Traditionally, FEA post-processing methods rely on human expertise to select appropriate regions for evaluating plastic and creep strain at critical locations and correlating these values with experimental data using the Coffin-Manson equation, which predicts fatigue lifetime based on cyclic plastic strain. This study introduces a novel method for FEA post-processing of surface-mounted devices (SMD) on printed circuit boards (PCB) using artificial intelligence. The method transforms the FEA data into a 2D grid map of creep strain values and employs a Convolutional Neural Network (CNN) for automatic feature extraction. Afterwards, a fully connected layer correlates the extracted features with the experimental measured solder joint lifetime, effectively capturing nonlinear relationships. The study focuses on the development of the concept of crack formation in the solder interconnects of ceramic based high-power LED packages used in the automotive industry for headlights. The validated FEA model is based on an extensive data set of 1800 LED packages including seven different ceramic-based LED packages and five different solders. The design of the ceramic LED package covers two-pad and three-pad footprint for soldering and thin film and thick film metallized ceramic carriers. Results show a strong agreement (R2 Score is 99.867 \%) between simulations and experimental data for ceramic LED packages. This automatic feature extraction from FEA data sets a new benchmark for improving solder reliability predictions, and it has proved to be better than established methods for lifetime prediction of solder joints.}, language = {en} } @inproceedings{MohdKreinerSchmidetal.2024, author = {Mohd, Zubair Akhtar and Kreiner, Christian and Schmid, Maximilian and Zippelius, Andreas and Tetzlaff, Ulrich and Elger, Gordon}, title = {Fully Connected Neural Network (FCNN) Based Validation Framework for FEA Post Processing to Improve SAC Solder Reliability Analysis}, booktitle = {2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9036-0}, doi = {https://doi.org/10.1109/ESTC60143.2024.10712023}, year = {2024}, language = {en} } @inproceedings{MohdSchmidZippeliusetal.2024, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Zippelius, Andreas and Elger, Gordon}, title = {LEDs Lifetime Prediction Modeling: Thermomechanical Simulation for SAC305 and SAC105}, booktitle = {2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9363-7}, doi = {https://doi.org/10.1109/EuroSimE60745.2024.10491530}, year = {2024}, language = {en} } @article{MohdSchmidElger2025, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Elger, Gordon}, title = {AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data}, volume = {15}, pages = {24340}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-025-06902-0}, year = {2025}, abstract = {Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R2 = 99.91\%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method.}, language = {en} } @unpublished{MohdSchmidElger2025, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Elger, Gordon}, title = {AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-6173485/v1}, year = {2025}, abstract = {Crack propagation in solder joints remains a critical challenge affecting the thermo-mechanical reliability of electronic devices, emphasizing the need for optimized package and solder pad designs. Traditional Finite Element Analysis (FEA) methods for predicting solder joint lifespan rely heavily on manual post-processing, where high-risk regions for plastic strain accumulation are identified. However, these approaches often overlook intricate failure mechanisms, as they primarily average creep strain and correlate it with experimental lifetime data using the Coffin-Manson equation, limiting their predictive accuracy. To overcome these limitations, this study introduces a novel AI-driven framework that automates 3D FEA post-processing for surface-mounted devices (SMDs) connected to printed circuit boards (PCBs). Unlike traditional methods, this framework leverages deep learning architectures—specifically, 3D Convolutional Neural Networks (CNNs) and PointNet—to extract complex spatial features directly from 3D FEA data, eliminating the need for manual interpretation. These learned features are then mapped to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture nonlinear failure behaviours that conventional methods fail to recognize. The research focuses on crack propagation in ceramic-based high-power LED packages used in automotive lighting systems, incorporating variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates with validated FEA models. Comparative analysis shows that PointNet significantly outperforms 3D CNNs, achieving an exceptionally high correlation with experimental data (R² = 99.99\%). This AI-driven automated feature extraction and lifetime prediction approach marks a major advancement over traditional FEA-based methods, offering superior accuracy, reliability, and scalability for predicting solder joint reliability in microelectronics.}, language = {en} } @inproceedings{LiuMohdSteinbergeretal.2024, author = {Liu, E and Mohd, Zubair Akhtar and Steinberger, Fabian and Wunderle, Bernhard and Elger, Gordon}, title = {Using µ-RAMAN Spectroscopy to Inspect Sintered Interconnects}, booktitle = {2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9036-0}, doi = {https://doi.org10.1109/ESTC60143.2024.10712149}, year = {2024}, language = {en} } @article{ZippeliusMohdSchmidetal.2025, author = {Zippelius, Andreas and Mohd, Zubair Akhtar and Schmid, Maximilian and Elger, Gordon}, title = {Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks}, journal = {IEEE Transactions on Device and Materials Reliability}, publisher = {IEEE}, address = {New York}, issn = {1558-2574}, doi = {https://doi.org/10.1109/TDMR.2025.3633876}, year = {2025}, abstract = {Scarcity of raw data is a major issue for applying data driven methods to reliability prediction, so making the best use of what is available is critical. This paper studies how different aspects of measurement data can be used best. Specifically, the reliability of the solder joint of LED packages is predicted based on Transient Thermal Analysis and Scanning Acoustic Microscopy data from a large measurement campaign. The impact of using full temporal information vs measurements at individual datapoints is investigated as well as the benefit of including the SAM data, and different ways of presenting the TTA information, either as a full curve or as expert-selected features. The impact of formatting categorical information of solder and LED package type as one-hot encoding or using embeddings is considered. Finally, the performance for Pass/Fail predictions of the best identified model with a model architecture developed on a similar dataset is compared. We identified the most relevant sources of information for predicting the behavior and the best format for the data, which helps guide the choice for future model architectures.}, language = {en} } @article{MeessGernerHeinetal.2024, author = {Meess, Henri and Gerner, Jeremias and Hein, Daniel and Schmidtner, Stefanie and Elger, Gordon and Bogenberger, Klaus}, title = {First steps towards real-world traffic signal control optimisation by reinforcement learning}, volume = {18}, journal = {Journal of Simulation}, number = {6}, publisher = {Taylor \& Francis}, address = {London}, issn = {1747-7778}, doi = {https://doi.org/10.1080/17477778.2024.2364715}, pages = {957 -- 972}, year = {2024}, abstract = {Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20\% in traffic flow compared to the baseline - the corresponding pre-optimised real-world control.}, language = {en} } @inproceedings{RauscherBraunHiemeretal.2025, author = {Rauscher, Andreas and Braun, Julian and Hiemer, Rainer and Heldwein, Marcelo Lobo and Endisch, Christian}, title = {Convolutional Neural Networks and Thresholding Approaches for Single and Multi-Sensor Detection of Partial Discharges in Electrical Machine Stators}, booktitle = {Proceedings of the 15th International 2025 IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-8820-6}, doi = {https://doi.org/10.1109/SDEMPED53223.2025.11153974}, year = {2025}, language = {en} } @article{HartmannMargull2019, author = {Hartmann, Christoph and Margull, Ulrich}, title = {GPUart - An application-based limited preemptive GPU real-time scheduler for embedded systems}, volume = {2019}, journal = {Journal of Systems Architecture}, number = {97}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1383-7621}, doi = {https://doi.org/10.1016/j.sysarc.2018.10.005}, pages = {304 -- 319}, year = {2019}, language = {en} } @unpublished{KettelgerdesHillmannHirmeretal.2023, author = {Kettelgerdes, Marcel and Hillmann, Tjorven and Hirmer, Thomas and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2312.04229}, year = {2023}, abstract = {In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.}, language = {en} } @article{VoegeleEndisch2016, author = {V{\"o}gele, Ulrich and Endisch, Christian}, title = {Predictive Vehicle Velocity Control Using Dynamic Traffic Information}, pages = {2016-01-0121}, journal = {SAE Technical Paper}, publisher = {SAE}, address = {Warrendale}, issn = {0148-7191}, doi = {https://doi.org/10.4271/2016-01-0121}, year = {2016}, language = {en} }