Data analytics techniques are used to process the massive amounts of data produced by smart grid and electric vehicle technologies. Electric vehicles collect data from various sources, including sensors and trip records. Big data techniques can be used to analyze this immense quantity of data, which can then be used to implement rules for charging station placing, creating smart charging algorithms, addressing energy efficiency issues, evaluating the competence of power distribution systems to handle supplementary charging loads, and finally, forecasting the market effectiveness of the assets supplied by powered mobility. This chapter's data analytics landscape assessment addresses the incorporation of electric vehicles with green, smart urban environments. It serves as a blueprint for the objectives and alternatives for the incorporation of electric vehicles into smart future cities.
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Data analysis is the science of analyzing raw data and making inferences from it (Aggarwal, C. C., 2011). Many data analysis techniques and procedures have been automated into mechanical workflows and algorithms in order to transform raw data for human understanding. There are five different types of analytics: descriptive, diagnostic, prescriptive, predictive, and cognitive (Amin, B. R. et al., 2021; Cao, Y. et al., 2018; Kambatla, K. et al., 2014).
Today, data analysts can gather data, store data, analyze data, and publish results using a range of software tools (LaValle, S. et al., 2011). Data collection, analysis, and presentation are all carried out with the help of data visualization and analysis tools such as Tableau and Power BI (Lee, J. et al., 2014; Runkler, T. A., 2020).
For the benefit of data analysts, more tools are being developed. Data analytics has been used by many businesses, including those with frequent quick turnaround periods like airlines and hotels. Creating a more economical method of conducting business and adopting it into the business model might aid in cost reduction. Numerous quality control techniques, including the well-known Six Sigma systems, are supported by data analysis. A wide range of analytical techniques and methodologies can be employed by data analysts to extract information from huge volumes of unstructured, unprocessed data. Additionally, data analysts employ simple programming languages to update and alter databases (Russom, P., 2011, Wang, K. et al., 2017, Tsai, C. W. et al., 2015).
The acceptance of electric vehicles has grown steadily over time. The vehicle industry is using cutting-edge technologies to improve current prototypes and gain a deeper understanding of its consumers' preferences. At every stage of the product lifecycle, data science, artificial intelligence, and big data tools can help you enhance the performance of your final product and increase ROI (return on investment) (Yu, N. et al., 2015). A system-level knowledge of how these businesses operate can be unlocked by utilizing data. Stakeholders' decision-making is improved and risk is decreased via analysis and interpretation of EV market volatility and data. Today, data science combining manufacturing processes, marketing tactics, and charging point optimization is essential due to the electric vehicle industry's digital revolution and shifting consumer preferences (Elgendy, N. et al., 2014).
The availability of fossil fuels is already dwindling. Vehicles powered by internal combustion engines, on the other hand, only have an efficiency of 18–22%. However, there are a lot of difficulties with electric automobiles. Despite several difficulties and problems, moving to electric vehicles is better for the environment and more practical economically in the long run (Akhavan-Hejazi et al., 2014). Although the energy cost of producing an electric vehicle is also quite costly, given everything and how inexpensive it is to charge electric vehicles, EVs are a useful solution. The market for electric vehicles is expanding quickly (Cai, H. et al., 2014).
In order to maximize EV charging, big data analytics facilitate the mobility of electric vehicles (EVs). Big data analytics supports EV integration in a variety of ways, including improved charging, effective battery management, tracking EV status, and more. By conserving energy already present in the vehicle, electric vehicles' energy efficiency can be increased. Understanding issues like brake energy consumption, waste heat energy use, electric motor design, dependency on solar power supply, etc. would be necessary to achieve energy efficiency (Cheng, Z. et al., 2017). As a result, choosing environmentally beneficial options, like electric vehicles, is now required. The ideal levels of EV adoption depend on convenience, cost-effectiveness, and other considerations, but big data analysis can strengthen these levels. Amazon acquired an electric delivery van from Michigan-based Rivian to address climate change. By adopting an electric vehicle (EV), one can improve the health of society by reducing greenhouse gas emissions. These emissions could be further reduced if electric vehicles could be charged using renewable energy sources like solar or wind (Chen, T. D. et al., 2013).