According to government officials, automakers, and academics, vehicular ad hoc networks (VANET) may be an effective tool for improving safety and efficiency on the road. For safety-related information to be disseminated, VANET uses cars and infrastructure nodes to interact with each other. Over the years, interest in vehicular communications has developed and is now acknowledged as a pillar of the intelligent transportation systems (ITSs). Nodes in vehicular networks have a lot of electricity and computational power (storage and processing) as a requirement. Electrification and renewable energy initiatives are relocating workforces. Controlling and regulating power flow from several sources and converters to various vehicle loads is critical in electric vehicle technology (EVT) and VANET. In this chapter, the authors put forward an extensive study over the power controllers and the use of artificial intelligence and machine learning in this field. Neural network systems for power optimization are explored. Intelligent power management systems developed are also a part of the focus.
TopIntroduction
A vehicular ad hoc network (VANET) is a kind of wireless ad hoc network that connects automobiles and roadside devices (Laberteaux & Hartenstein, 2009). The integration of next-generation wireless networking capabilities into automobiles is developing as new technology. Virtual ad hoc networks (VANETs) are designed to offer mobile users access to the Internet while on the road, as well as efficient vehicle-to-vehicle communications that support Intelligent Transportation Systems (ITS). These include cooperative traffic monitoring, traffic flow management (including blind crossings), accident avoidance, nearby information services (such as traffic information systems), and real-time diversion route calculation. At the same time, automobiles and other gadgets become nodes in a tiny network. All nodes get the information that the nodes have available to them. Similarly, each node receives the data sent by other nodes after sending its own set of data. When the data has been gathered, the nodes labour to turn it into valuable information, which they subsequently communicate to other devices. Devices may freely join and leave the network as the network grows, resulting in an “open” system of communication. Onboard sensors are now standard on new cars, making it easier for the vehicle to join and integrate into the network and benefit from VANET (Anwer & Guy, 2014).
Future transportation networks are expected to be dominated by electric vehicles (EVs). There are several advantages to electric vehicle systems, such as reduced fossil fuel dependency, reduced emissions of carbon dioxide and greenhouse gases, and the ability to recharge cars from renewable energy sources. The use of traditional energy resources may be decreased by using electric cars to go about. In the event of an energy crisis, this may extend the life of other sources of energy and assist alleviate it. Vehicles are also a significant source of global warming gas emissions. Cleaner air will benefit greatly from the use of electric cars. Furthermore, research has shown that the electric car engine is more fuel-efficient (Lopes, Soares, and Almeida, 2010). The problem of reliance on a single fuel source may be addressed by using electric cars in combination with a wide range of renewable energy sources.
The design of EV charging and energy management systems is one of the most important aspects of EV deployment (Clement-Nyns, Haesen, & Driesen, 2010). Because an electric car may take longer to recharge than a gasoline-powered vehicle, it is necessary to have a city-wide or area-based charging infrastructure management system. Because of this, an information system for mobile electric vehicle fleets and charging stations with varying occupancy levels must be developed. If charging station providers use a variety of sources to generate energy, the cost and availability may vary based on the time of day or the region (Kalakanti & Rao, 2022).
Energy storage is an option for EVs when peak/average generating capacity exceeds consumption demand in particular places (Yadav & Maurya, 2022). Electric vehicle information systems (EVIS) face the fundamental difficulty of effectively and quickly providing cars with the needed service information, such as a nearby charging station's location, a cheaper cost station, a speedier availability to charge. When it comes to controlling an Electric Vehicle (EV), things aren't quite as straightforward as they seem. For one thing, the functioning of an EV is time-dependent (e.g., the operating parameters of the vehicle and road conditions are always changing) (Zhang, Chen, & Liao, 2022). Therefore, the controller should be developed to make the system resilient and adaptable, enhancing the system's dynamic and steady-state performance. Additionally, EVs are “energy management machines,” which makes the control of EVs
unique. The low operating distance per battery charge is now the key limiting factor for the widespread deployment of electric vehicles (EVs). As a result, in addition to managing the vehicle's performance (e.g., smooth driving for a pleasant ride), substantial attention must be devoted to the battery's energy management.