Repository der Technischen Hochschule Ingolstadt
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Interconnection of low-temperature metallization on silicon solar cells - The role of silver in tin-bismuth-based solder alloys
Get PDFThis study investigates the potential of tin-bismuth (SnBi) alloys micro-alloyed with silver (Ag) to enhance low-temperature (LT) soldering for photovoltaic (PV) modules. The primary focus is on addressing challenges such as intermetallic compound (IMC) formation, which can compromise mechanical strength and durability under thermal cycling, thereby improving the long-term stability of solder joints. A systematic investigation of the influence of the Ag amount is being conducted with the intention to find the optimal alloy composition for SnBi solder joints on LT metallization contacts. The objective is to provide a cost-effective, lead-free alternative for the interconnection of temperature-sensitive solar cells like silicon heterojunction (SHJ) cells. The findings illustrate that the addition of 0.4 %wt. Ag notably enhances the wettability and initial mechanical strength of the solder. An increase in the Ag content to 1.0 %wt. results in an excessive growth of IMC, which in turn leads to increased brittleness and potential long-term stability issues of the solder joint. Thermal cycling tests demonstrate that modules soldered with SnBiAg0.4 exhibited less than -5 % loss in power. This composition represents an optimal balance between performance and cost. These findings show the potential of SnBiAg0.4 as a suitable solder alloy regarding long-term stability and electrical performance for interconnection of LT metallization contacts of solar cells in PV module manufacturing
Radar in the Rain: Understanding and Simulating Environmental Effects on ADAS Radar Sensors
No full textParameterization of automated driving functions in virtual environments based on characteristic test scenarios
No full text
Monitoring Occupant Posture Using a Standardized Sensor Interface with a Vehicle Seat
Get PDFCar safety can be enhanced by enabling the Airbag Control Unit (ACU) to adaptively deploy different charges based on the occupant’s position once the crash occurs. In this context, monitoring the occupant’s position using a sensorized seat integrated with an Inertial Measurement Unit (IMU) offers a practical and cost-effective solution. However, certain challenges still need to be addressed. The adoption of sensorized seats in research and vehicle set-up is still under consideration. This study investigates an interface device that can be reconfigured to suit almost any seat model. This reconfigurability makes it easily adaptable to new vehicles under development and applicable to any passenger seat in the vehicle. This paper details the device’s design, including its programming using calibration and monitoring features, which significantly improves its reliability compared to earlier prototypes. Extensive testing through real driving experiments with multiple participants demonstrated an accuracy range of 45–100%. The testing involved both drivers and passengers, showcasing the device’s ability to effectively monitor various in-car scenarios
Fusion of Driving Behavior and Monitoring System in Scenarios of Driving Under the Influence: An Experimental Approach
Get PDFDriving under the influence of alcohol (DUI) remains a leading cause of accidents globally, with accident risk rising exponentially with blood alcohol concentration (BAC). This study aims to distinguish between sober and intoxicated drivers using driving behavior analysis and driver monitoring system (DMS), technologies that align with emerging EU regulations. In a driving simulator, twenty-three participants (average age: 32) completed five drives (one practice and two each while sober and intoxicated) on separate days across city, rural, and highway settings. Each 30-minute drive was analyzed using eye-tracking and driving behavior data. We applied significance testing and classification models to assess the data. Our study goes beyond the state of the art by a) combining data from various sensors and b) not only examining the effects of alcohol on driving behavior but also using these data to classify driver impairment. Fusing gaze and driving behavior data improved classification accuracy, with models achieving over 70% accuracy in city and rural conditions and a Long Short-Term Memory (LSTM) network reaching up to 80% on rural roads. Although the detection rate is, of course, still far too low for a productive system, the results nevertheless provide valuable insights for improving DUI detection technologies and enhancing road safety
