This work introduces a novel wireless approach for the data transmission within automotive battery management systems. The main target is the reduction of the wiring harness deployed in a battery. The characteristics of the wireless in-battery channel are investigated by means of measurements and software-based electromagnetic simulations. Different types of antennas and frequency bands are analyzed. The performance of the proposed system is evaluated by means of simulations and prototypes.

This work introduces a novel wireless approach for the data transmission within automotive battery management systems. The main target is the reduction of the wiring harness deployed in a battery. The characteristics of the wireless in-battery channel are investigated by means of measurements and software-based electromagnetic simulations. Different types of antennas and frequency bands are analyzed. The performance of the proposed system is evaluated by means of simulations and prototypes. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components.

Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.

The increasing use of automated laser welding processes causes high demands on process monitoring. This work demonstrates methods that use a camera mounted on the focussing optics to perform pre-, in-, and post-process monitoring of welding processes. The implementation uses machine learning methods. All algorithms consider the integration into industrial processes. These challenges include a small database, limited industrial manufacturing inference hardware, and user acceptance.

The automatic retrieval of images according to the similarity of their content is a challenging task with many application fields. In this book the automatic retrieval of images according to human spontaneous perception without further effort or knowledge is considered. A system is therefore designed and analyzed. Methods for the detection and extraction of regions and for the extraction and comparison of color, shape, and texture features are also investigated.