The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li₄Ti₅O₁₂/LiCoO₂) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety.

The complexity of crash scenarios in the context of vehicle safety is steadily increasing. This is especially the case on the way to mixed traffic challenges with non-automated and automated driving vehicles. The number of simulations required to design a robust restraint system is thus also increasing. The vast range of possible scenarios here is causing a huge parameter space. Simultaneously biofidelic simulation models are resulting in very high computational costs and therefore the number of simulations should be limited to a feasible operational range. In this study, a machine-learning based design of experiments algorithm is developed, which specifically addresses the issues when designing a safety system with a limited number of simulation samples taking diversity of the occupant and accident scenario into account. In contrast to an optimization task, where the aim is to meet a target function, our job has been to find the critical load case combinations to make sure that these are addressed and not missed. A combination of a space-filling approach and a metamodel has been established to find the critical scenarios in order to improve the system for those cases. It focuses specifically on the areas that are difficult to predict by the metamodel. The developed method was applied to iteratively generate a simulation matrix of a total of 208 simulations with a generic interior model and a detailed FE human body model. Kinematic and strain-based injury metrics were used as simulation output. These were used to train the metamodels after each iteration and derive the simulation matrix for the next iteration. In this paper we present a method that allows the training of a robust metamodel for the prediction of injury criteria, considering both varying load cases and varying restraint system parameters for individual anthropometries and seating postures. Based on that, restraint systems or metamodels can be optimized to achieve the best overall performance for a huge variety of possible scenarios with a specific focus on critical scenarios.

Image sensors in TFA (Thin Film on ASIC) technology are composed of amorphous silicon (a-Si:H) thin film detectors on top of crystalline ASICs. A TFA prototype for automotive vision systems is presented which allows each pixel to adapt its individual sensitivity to the local illumination. By this means, a dynamic range of 120 dB minimum along with high local contrast is achieved. The ASICs were fabricated in a 0.7 /spl mu/m CMOS technology.

Distraction is a major contributor to road accidents, especially among children who are easily distracted and may not be fully aware of the traffic situation. It is crucial to understand that children up to a certain age may struggle to halt their movement once initiated. This study conclusively demonstrates that the stopping distance, time, and deceleration of children aged six to ten years after a specific stop signal at different speeds are strongly influenced by the speed of movement and the age of the children. The results show that in the “walking” test configuration, the children were able to stop within a range of 0.47 m to 0.63 m, with a shorter distance for older children. The stopping time ranges from 0.84 s to 1.21 s and correlates positively with age. The stopping time and distance of children were measured in both “running” and “walking” test configurations across different age groups. However, in the “running” test configuration, stopping distance is almost the same across all age groups, with children requiring between 1.72 m and 1.84 m and a stopping time ranging from 1.17 s to 1.28 s. In the “walking” test configuration, children are able to decelerate between 0.91 m/s² and 1.57 m/s², while in the “running” test configuration, they are able to decelerate between 2.24 m/s² and 3.19 m/s².