Machine Learning in Electric Vehicles and Environment Impacts (MLEV)

Electricity is a domestically produced transportation fuel that will transform the whole transportation sector. It helps the entire human community to use pollution free clean transportation and reduce its dependence on oil. EVs lacked sufficient range and power delivery when compared with internal combustion engines. It also requires an efficient energy storing system which is one of the major concerns of today's EV technology. More number of batteries are required to assure a desired level of performance, which leads to an increase in the vehicle weight, cost and the degradation of vehicle performance. Lithium-ion batteries (LIBs) are currently used in the majority of electric vehicles, as they provide sufficient energy storage and performance; Its safety concerns, weight-to-performance conundrum, and usage of cobalt as cathode still remains a significant limitation to lithium-ion batteries.

Machine learning accelerates the automation of electric vehicle driving, and that machine learning it provides costeffective solutions to the complex real-life problems for which conventional computing solution does not exist. A revolution is transforming the automotive industry. Powerful digital technologies are driving demand for autonomous transport solutions, and they are disrupting existing approaches to car building. These smart and connected vehicles powered by machine learning represent a considerable challenge and opportunity for automakers. Developing future vehicles comes with a great increase in complexity, so the right tools and technology will be needed.

Several researchers are currently underway to develop and commercialize a promising energy storage device. Batteries that are smaller, less costly and have higher energy density forms the prime focus in the development and research sector. Solid-state battery, Proton batteries, Graphene-based energy storage devices, hydrogen fuel cell based electric mobility solutions are the emerging technologies in the EV sector. This Special Issue spans over the latest topics in EV transportation system, energy storage and management system, development of machine learning in energy markets. We welcome contributions from many disciplines.