Название: Big Data and Electric Mobility
Автор: Quan Zhou, Haoran Zhang
Издательство: CRC Press
Год: 2025
Страниц: 306
Язык: английский
Формат: pdf (true), epub
Размер: 34.7 MB

This book details how to assess electric mobility characteristics within electric vehicles, discussing energy management methods, automated systems, and the enormous potential of data resources mined from software, navigation systems, and connectivity.

Big Data and Electric Mobility presents methods to mine data specifically for electric vehicles, to comprehend their performance and to present opportunities to develop data-driven technological advancements. Including contributions from experts across the world, the book will look at topics such as human mobile behavior, battery charging and health, powertrain simulation, energy management, and multiphysics-constrained optimal charging.

Big Data and Artificial Intelligence (AI) will play key roles in the investigation and integration of renewable energy sources, electrified powertrains, intelligent transport systems, and shared mobility models, crucial in advancing eMobility systems. Artificial Intelligence will exhibit capabilities in enhancement of the efficiency and performance of eMobility systems at four levels (unit, powertrain, vehicle, and platoon), whereas the conventional design-of-experiments (DoE) method is insufficient to meet the growing demands for cost-effective vehicle product development. Digitalization in new vehicle design is rapidly evolving, with the technological development of digital twins, the Internet of Things (IoT), and artificial intelligence (AI), for example, in the ability to update vehicle control software through over-the-air (OTA) services. Automotive original equipment manufacturers (OEMs) now have the opportunity to use vast amounts of data to develop digital twin models in vehicle design. The deep integration of digital twin technology and Artificial Intelligence in eMobility systems will drive further advancements in the transport sector.

Big Data is made up of larger, complex datasets that originate from increasingly new sources of data. These datasets are so massive that the abilities of traditional data processing software are insufficient to manage them. Nevertheless, these massive volumes of data can be used to address business problems that would not have been tackled before. A simple definition of Big Data organized around Big Data’s three Vs could be large volumes of data that contain immense variety and are generated at increasing velocity. Recently, six other Vs and one C have been added to define the truthfulness and meaningfulness of data as veracity, infrequency as validity, extracting value from the collected data, replicability in the form of visualizations, processability in a virtual cloud platform, data variability, and complexity, purely in computation form.

The book will be key reading for researchers and engineers in the fields of automotive engineering, electrical engineering, and data mining.