As we know, with the great development in the sector of Smart Grids, the adoption of Electric Vehicles is proved out to be great boon in the environment sector, as it is seen that the amount of carbon-di-oxide emission is drastically reduced as the people nowadays started using electric vehicle. In EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy source in variety of location. On the other hand useful measure has to be taken so we can avoid maximum peaks at grid which may result in high electricity prices and overloads on grids. So in this paper, our main focus is to understand the utilization of the artificial intelligence techniques to render EV and the system that can manage EVs smartly. This can help us to develop a classification of key technique and benchmarks that can be used to start of this work space.
We know that , increase in fossil fuels have done a great negative impact on the climate, society and now several countries have instigated plans to reduce carbon emission. In particular, the electriﬁcation of transport is seen as one of the main pathways to achieve signiﬁcant reductions in CO2 emissions. In the last few years EVs have gained ground, and, to date, more than 180 thousand of them have been deployed worldwide. Despite this number only 0.02% of all vehicles on the roads, an ambitious target of having more than 20 million EVs on the roads by 2020 has been set by the International Energy Agency. To insure that large use of EVs results in significant reduction in CO2 emissions, it is necessary they should charge using energy from renewable sources such as (e.g., wind, solar).
On the other hand, given that large numbers of EVs need to charge on a daily basis, (40% of EV owners in California travel daily further than the range of their fully charged battery if EVs charge as and when needed, they may overload the network. For this reason, new mechanisms are required to manage the charging of EVs –Grid-to-Vehicle (G2V) – in real time while considering the constraints of the distribution networks within which EVs need to charge.
Energy Efficient EV Routing And Range Maximisation
Due to the limited range and the long charging times, various numbers of techniques to optimise the battery usage and to maximise the range of an EV have been developed. Two key research challenges are considered:
1) Energy efficient EV routing (considering or not recharging), where established search algorithms are adapted to the characteristics of EVs so as to calculate routes that utilise the EVs’ energy recuperation ability in order to maximise driving range.
2) Battery efficiency maximisation where techniques to maximise the utilisation of the energy stored by an EV are considered.
Energy Efficient EV Routing
The algorithms used by these modules are mostly based on Dijkstra or Dijkstra-like algorithms. Dijkstra's algorithm was created by the Dutch computer scientist, Edsger Dijkstra, in 1959. Dijkstra's algorithm is a greedy like search technique that solves for the shortest path problem on a graph. This makes it ideal for routing problems. A key requirement for Dijkstra's algorithm to be effective is to have all positive edge costs (one exception is when the negative nodes are only connected to the source or start point of the routing problem). Negative edge costs will prevent the algorithm from generating the optimal solution. This map is a directed graph from left to right. The numbers on the graph represent the amount of energy consumption for an EV. The negative routes mean that the EV is going downhill and thus generating energy. Dijkstra's algorithm is then applied to this simple routing problem.
Battery efficiency maximisation
The trend in energy storage technology for EVs (to maximise lifetime and allow for fast charging) is to use a chemical battery in conjunction with super capacitors. In a super capacitor, energy is stored electrostatically on the surface of the material, and does not involve chemical reactions. Super capacitors can be charged quickly, and they can last for millions of charge-discharge cycles, but they have a relatively low energy density Super capacitors can discharge a large current at short notice (e.g., when accelerating), thus reducing the stress on the chemical battery. When no current is drawn from the super capacitor, it may then recharge, at a slower rate, from the attached battery.
There are many ways so that the battery can be efficiently maximize, so there are few things drivers can do to improve the efficiency.
1. Accessories such as heating, air conditioning, and entertainment systems affect fuel economy on all vehicles, but can have a greater effect on EVs. However, using seat warmers instead of the cabin heater can save energy and extend range.
2. Avoid hard braking and anticipate braking
3. Observe the speed limit: Efficiency usually decreases at a speed above 50mph.
Data Fetched Which AI Used To Function Of Vehicle
In this section, we present the algorithm run by each EV agent to compute the best charging point to go to based on the estimated travel time (using the shortest-path) to all charging points, its current battery level, and the cost to charge at each charging point. While the source and the destination of the journey, as well as the current battery level are provided by the user, the cost of the electricity is provided by the charging points. Different cost policies are presented in the next section.
Development And Research
In this paper, we have analyzed the application of Artiﬁcial Intelligence techniques to address the major challenges that arise in the deployment and management of Electric Vehicles. In particular, we have studied AI techniques for energy- efﬁcient EV routing and charging point selection, as well as for the integration of EVs into the smart grid.
In this paper we have use the Artificial Intelligence process to analyze the problem which can occur in the management of the Electric Vehicle (EVs). Also, we have studied AI technique for EV routing and charging of the Electric Vehicle.
In late May, Honda Motor Company and General Motors announced a partnership to conduct a research project on electric vehicles (EVs) and smart grid interoperability. The goal is to determine whether EVs are capable of stabilizing the power supply in next-generation smart grids.
Honda and GM plan to develop ways to facilitate data retrieval of information exchanged between power grids and electric vehicles. Because smart grids rely on unstable renewable energy sources, such as solar and wind power, EVs could serve as more reliable power supply. If the project becomes successful, EV owners could generate revenue by exchanging the power stored in their electric car batteries with the grid
Evolution Of Smart Grid In Electric Vehicle
Smart Residential Charging: It helps in charging times of the vehicle shifted as per the grid loads available and also depends on the owner needs.
Vehicle-to-Grid (V2G):It is a technology that uses connectivity of an electric vehicle with the distribution grid to provide demand response services by two ways First is by returning electric power to the grid and Second by decreasing their charging rate. The fiscal
Incentives for V2G will need to be developed so that the customers are motivated to organise this service and because of that utilities can able to achieve the level of interaction that helps with grid management.
Vehicle-to-Home (V2H): It involves the connection between owner car to his/her house so to provide additional source to home. The vision here is that this connection can provide load flake service during peak hours, as well as a source of back-up energy during outages.
Renewables and Storage Integration: If you want to charge your vehicle at night, when electricity is cheaper with your own home battery or your rooftop solar depending upon your needs so it will find an algorithm that will use the pattern depending upon usage and needs.
From this we can understand the use and importance of smart grid which is use for various purpose like to supply in cities and offices and solar panel and now for Electric Vehicle also which has drastically reduced the used of non renewable sources and because of this we can control the pollution and we can save the environment also.
Conclusion And Result
It has been found that electric vehicles can provide auxiliary services to the grid such as voltage and frequency regulation, peak power leveraging support to enhance the operational efficiency and secure the electric grid and reduce power system operating cost. The study has shown that the deployment of the EVs into the smart grid system would be possible with the advanced communication, control and metering technologies. In this case the smart grid will foster the interoperability of the EVs for the grid support. Following that note, a correlation between the smart grid and the EVs has been extensively investigated in this paper. However, more research and analysis are required to justify the adoption of the V2G framework over other energy storage systems. To realize a near-real time communication and power measurement, an advanced technology in these areas has to be enforced to identify the challenges and limitations. Few researches have been reported but the issues like communication delays, routing protocols and cyber security are very critical for the reliable and efﬁcient adoption of the V2G transactions framework in the smart grid context.
Moreover, the feasibility of the smart grid with the V2G schemes has been explored with the insight of the recent projects. The low penetration of the electric vehicles embedded with the V2G functionalities is one of the challenges which hinder to a large extent the EVs adoption in the energy market. The side effects of the EV technologies like low cost and high efficient power converters(for EV charger) are among the other factors manifest at the automotive manufacturers' perspectives. For the effective V2G operation with the current battery technology, the challenge still remains to be battery wearing under frequent charging and discharging cycles. The researches have shown some promising results for lithium ion (LFP) battery.