TY - JOUR A1 - Fayyazi, Mojgan A1 - Sardar, Paramjotsingh A1 - Thomas, Sumit Infent A1 - Daghigh, Roonak A1 - Jamali, Ali A1 - Esch, Thomas A1 - Kemper, Hans A1 - Langari, Reza A1 - Khayyam, Hamid T1 - Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles N2 - Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed. KW - optimization system KW - intelligent control KW - fuel cell vehicle KW - machine learning KW - artificial intelligence KW - intelligent energy management Y1 - 2023 U6 - https://doi.org/10.3390/su15065249 N1 - This article belongs to the Special Issue "Circular Economy and Artificial Intelligence" VL - 15 IS - 6 SP - 38 PB - MDPI CY - Basel ER - TY - JOUR A1 - Henn, Gudrun A1 - Polaczek, Christa T1 - Studienerfolg in den Ingenieurwissenschaften JF - Das Hochschulwesen : HSW ; Forum für Hochschulforschung, -praxis und -politik Y1 - 2007 SN - 0018-2974 VL - 55 IS - 5 SP - 144 EP - 147 ER - TY - JOUR A1 - Esch, Thomas T1 - Trends in commercial vehicle powertrains JF - ATZautotechnology N2 - Low emission zones and truck bans, the rising price of diesel and increases in road tolls: all of these factors are putting serious pressure on the transport industry. Commercial vehicle manufacturers and their suppliers are in the process of identifying new solutions to these challenges as part of their efforts to meet the EEV (enhanced environmentally friendly vehicle) limits, which are currently the most robust European exhaust and emissions standards for trucks and buses. KW - European Transient Cycle KW - Common Rail Injection System KW - Commercial Vehicle KW - Selective Catalytic Reduction KW - Diesel Engine Y1 - 2010 U6 - https://doi.org/10.1007/BF03247185 SN - 2192-886X VL - 2010 IS - 10 SP - 26 EP - 31 PB - Vieweg & Sohn CY - Wiesbaden ER - TY - JOUR A1 - Genz, M. A1 - King, H. A1 - Wahle, Michael T1 - Mikrozellige Polyurethan-Elastomere als Federelement in Automobilanwendungen JF - Automobiltechnische Zeitschrift ; ATZ Y1 - 1992 SN - 0001-2785 VL - 94 IS - 10 SP - 512 EP - 520 ER - TY - JOUR A1 - Funke, Harald A1 - Esch, Thomas A1 - Roosen, Petra T1 - Powertrain Adaptions for LPG Usage in General Aviation JF - MTZ worldwide N2 - In general aviation, too, it is desirable to be able to operate existing internal combustion engines with fuels that produce less CO₂ than Avgas 100LL being widely used today It can be assumed that, in comparison, the fuels CNG, LPG or LNG, which are gaseous under normal conditions, produce significantly lower emissions. Necessary propulsion system adaptations were investigated as part of a research project at Aachen University of Applied Sciences. Y1 - 2022 U6 - https://doi.org/10.1007/s38313-021-0756-6 VL - 2022 IS - 83 SP - 58 EP - 62 PB - Springer Nature CY - Basel ER - TY - JOUR A1 - Dachwald, Bernd A1 - Tsinas, L. T1 - A combined neural and genetic learning algorithm / Tsinas, L. ; Dachwald, B. JF - Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence. Y1 - 1994 SN - 0-7803-1899-4 SP - 770 EP - 774 CY - Orlando, Fl ER - TY - JOUR A1 - Dahmann, Peter T1 - Funktionsweise von Hydraulikfiltern JF - Ölhydraulik und Pneumatik. 37 (1993), H. 2 Y1 - 1993 SN - 0341-2660 SP - 118 EP - 122 ER - TY - JOUR A1 - Polaczek, Christa A1 - Henn, Gudrun T1 - Gute Vorkenntnisse verkürzen die Studienzeit JF - Mathematikinformation : eine Zeitschrift von Begabtenförderung Mathematik e.V. Y1 - 2008 SN - 1612-9156 VL - 2008 IS - 49 SP - 46 EP - 50 PB - Begabtenförderung Mathematik CY - Neubiberg ER - TY - JOUR A1 - Schopen, Oliver A1 - Shah, Neel A1 - Esch, Thomas A1 - Shabani, Bahman T1 - Critical quantitative evaluation of integrated health management methods for fuel cell applications JF - International Journal of Hydrogen Energy N2 - Online fault diagnostics is a crucial consideration for fuel cell systems, particularly in mobile applications, to limit downtime and degradation, and to increase lifetime. Guided by a critical literature review, in this paper an overview of Health management systems classified in a scheme is presented, introducing commonly utilised methods to diagnose FCs in various applications. In this novel scheme, various Health management system methods are summarised and structured to provide an overview of existing systems including their associated tools. These systems are classified into four categories mainly focused on model-based and non-model-based systems. The individual methods are critically discussed when used individually or combined aimed at further understanding their functionality and suitability in different applications. Additionally, a tool is introduced to evaluate methods from each category based on the scheme presented. This tool applies the technique of matrix evaluation utilising several key parameters to identify the most appropriate methods for a given application. Based on this evaluation, the most suitable methods for each specific application are combined to build an integrated Health management system. KW - Fuel cell KW - Health management system KW - Online diagnostic KW - Fault detection KW - Non-model-based Evaluation Y1 - 2024 U6 - https://doi.org/10.1016/j.ijhydene.2024.05.156 SN - 0360-3199 VL - 70 SP - 370 EP - 388 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Stiemer, Luc Nicolas A1 - Thoma, Andreas A1 - Braun, Carsten T1 - MBT3D: Deep learning based multi-object tracker for bumblebee 3D flight path estimation JF - PLoS ONE N2 - This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker’s appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired. Y1 - 2023 U6 - https://doi.org/10.1371/journal.pone.0291415 SN - 1932-6203 N1 - Corresponding author: Luc Nicolas Stiemer VL - 18 IS - 9 PB - PLOS CY - San Fancisco ER -