Wireless Data Transmission For The Battery Management System Of Electric And Hybrid Vehicles

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.

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.

Focusing on reducing emissions and improving fuel economy, automotive manufacturers are developing electric vehicles (EV) to replace fuel and diesel vehicles starting in 2030 onwards. The EVs, with their green power supplies maximize environmental benefits with zero emissions thereby lowering air pollution levels. There is now an increased demand for stable electric storage systems (ESS) that are part of the design of new electric vehicles. This timely reference gives an overview of modern electrical power systems applied in the current generation of electric vehicles which require an ESS, and how these can be utilized for simultaneous power and data communication. The book starts with an introduction to the topic, before giving a summary of the green power trend for the electric vehicle market. The book then delves into the theoretical and analytical framework required to understand adaptive compensation of the magnetic inductive system (ACMIS), based on zero voltage switch (ZVS). The chapters demonstrate how these systems are used for transmitting electric power from a single-end inverter combined with a compensated network of parallel to parallel (P-P) type and an auto-tuning impedance of LC tank. The book also covers the experimental method for a multifunctional contactless power flow of the G2V mode and bidirectional outer communication and inner communication with giant magnetoresistance (GMR) effect for car parking guidance. The experiment shows how to analyze data transferring performance including the current trimming method and how to evaluate data transmission quality according to the relevant parameters. Overall the book serves to familiarize automotive engineers and industry professionals involved in the electric vehicle market with the issues that surround wireless power charging and data transfer systems for electric vehicles, and introduces them to more coherent designs.

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.

Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.

This work presents model-based algorithmic approaches for interference-invariant time delay estimation, which are specifically suited for the estimation of small time delay differences with a necessary resolution well below the sampling time. Therefore, the methods can be applied particularly well for transit-time ultrasonic flow measurements, since the problem of interfering signals is especially prominent in this application.