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 Battery Management System (BMS) is in charge of monitoring and ensuring the correct operation of the Electric Vehicle (EV) battery. The BMS uses a master slave architecture with a wired daisy chain link. Lately, the industry has seen interest in replacing the wired bus with a wireless network. Wireless BMS (WBMS) brings multiple advantages but at the same time several challenges. In this thesis, we present a complete solution to enable robust wireless communications inside the battery pack of EVs. We propose an enhanced version of the IEEE Std 802.15.4 Time Slotted Channel Hopping (TSCH) Medium Access Control (MAC) mode running over the physical layer of Bluetooth Low Energy (BLE). The network is managed by a reliable and predictable scheduling function based on the Group Acknowledgement (GACK) method. For the parking state of the vehicle, we propose a WBMS sleep mode that reconfigures the network architecture allowing the central node to switch off, preventing the discharge of the small 12 V battery of the EV. We also discuss the new opportunities that the inclusion of a computing capacity in the slaves brings to BMS software development. The laboratory and vehicle test results show that our system provides high reliability, bounded latency, and low power consumption.

The book provides thorough coverage of energy systems for electric and hybrid vehicles with a focus on the three main energy system technologies - energy sources, battery charging and vehicle-to-grid systems. Energy sources includes electrochemical energy sources, electromechanical energy storage, hybrid energy sources, on-board solar energy harvesting, on-board thermoelectric energy recovery, and battery management. Battery charging technology ranges from the existing charging strategies to the latest wireless charging techniques for park-and-charge and move-and-charge. Vehicle-to-grid technology covers interdisciplinary topics which link electric vehicles, information technology and power systems for management of energy systems, power interfaces and service scheduling. Researchers and advanced students developing electric/hybrid vehicles and intelligent transport systems in industry and academia will find this book invaluable. As will researchers and advanced students working on automotive engineering and battery/power engineering.

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.

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.

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.