The Impact of AI on the Widespread Use of EV’s: A Review

Energy production on a worldwide scale is now dependent on nonrenewable fossil fuels such as coal, oil, and gas. These fuels are known to contribute to climate change, escalate geopolitical conflicts, deplete already scarce resources, and have negative impacts on human health. So, the Paris Agreement is only one example of how major international environmental organizations and nations have responded to the threat of climate change by establishing programs and policies to curb emissions of carbon. In the last ten years, there have been tremendous advancements in both academic study and commercialization related to renewable power production and vehicle electrification. Electric cars currently only account for a fraction of the market, even though they provide several benefits to the environment and drivers’ performance compared to conventional vehicles. The increased initial investment, shorter range, and absence of supporting infrastructure are the key reasons why electric cars (EVs) do not attract the same level of customer interest as traditional internal combustion vehicles. In order to address the significant initial investment, it is crucial to reduce the cost of the electric vehicle’s battery pack. To improve battery production efficiency and find inexpensive, high-performing battery materials, machine learning offers a time- and moneysaving solution. With the help of AI algorithms and controls, we can maximize energy saving, get a more accurate estimate of the vehicle’s driving range, and alleviate “range anxiety” among consumers. Electric cars might be more appealing to consumers if charging stations are conveniently located and the smart grid is in place. Smart grid technology known as vehicle-to-grid (V2G) allows electric vehicle owners to generate income by supplying electricity to the grid. Recent developments in electric vehicles (EVs) and associated infrastructure, driven largely by advancements in artificial intelligence (AI), have increased the allure of EVs to consumers. Using AI to enhance electric vehicles, make smart grid integration easier, and improve electric vehicle charging stations is examined and evaluated critically. We conclude by looking forward to potential trends and developments in the field.

I. Introduction

Coal, oil, and gas are now the most used nonrenewable fossil fuels for transportation and energy production across the world. There are many challenges with fossil fuels, including a finite supply, climate change, geopolitical tensions, and health concerns. Governments have suggested programs to reduce the use of fossil fuels for energy production and transportation in light of the negative impacts of these fuels. One such program is the Paris Agreement [1]. Worldwide sales of electric cars (EVs) reached 2.1 million in 2019, continuing a trend that began in the previous decade. An increase in the number of companies producing EVs has also increased the variety of EVs available to buyers [2]. A number of factors have contributed to the meteoric rise in the popularity of electric vehicles (EVs), including improvements in charging infrastructure, more public understanding of the environmental advantages of EVs, tighter regulations around EV design and performance, and cheaper battery packs. Nevertheless, there are still obstacles to the broad adoption of EVs, even if the future of EV integration in society is bright. Electric vehicles have a number of obstacles, such as user range anxiety due to their short range, an inadequate charging infrastructure, a hefty initial investment compared to cars powered by internal combustion engines, and worries about their safety. So, to get over such obstacles, we need to come up with new, relevant approaches and provide practical ways. All artificial intelligence (AI) algorithms, including those in machine learning and AI, are taken into account within the framework of this assessment [3]. Because of their superior trend-finding capabilities and easier implementation, these AI algorithms may, depending on the situation, surpass conventional rule-based systems, which rely on human expertise to establish rules inside a system (also known as expert systems).the number of One appealing use of AI is in the electric vehicle industry, where it may help reduce costs by facilitating the design and production of batteries using the best possible materials. Precise range assessment to allay fears about short electric vehicle battery lives caused by the unpredictability of future road conditions, The use of artificial intelligence (AI) controls for electric vehicle (EV) auxiliary systems improved energy usage compared to conventional methods, The possibility of enhanced traffic flow and safer roads with linked and autonomous vehicles. Figure 1 summarizes and divides the AI approaches typically employed in EVs and associated infrastructures into two categories: computational intelligence (CI) and machine learning (ML). CI algorithms are frequently employed for addressing search, optimization, and other complicated problems, in addition to ML. When it comes to electric vehicles, CI algorithms are lifesavers when it comes to optimizing control systems, determining where to put EVCSs, and integrating EV infrastructure with the smart grid, among other difficult and dynamic optimization challenges. Academic papers, patent applications, and manufacturing scale have all expanded dramatically over the last decade, even if the present EV market has not quite adopted AI [4]. Rapid advancements in research and development, as well as widespread industrial application and commercialization, depend on comprehensive overviews of the roles played by artificial intelligence (AI) in electric vehicles (EVs) and their underlying infrastructure. Where AI has the potential to affect the widespread adoption of electric vehicles is the primary emphasis of this analysis [5].