electric vehicle research paper 2021 india

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Management of Environmental Quality

ISSN : 1477-7835

Article publication date: 9 February 2022

Issue publication date: 10 March 2022

The study identifies the challenges of developing the “electric vehicle (EV)” charging infrastructure in India, having an ambitious target of 30% EV adoption by 2030.

Design/methodology/approach

First, a systematic literature review determined EV adoption and challenges in the EV charging infrastructure development globally and specifically in India. Secondly, a focussed group study in which 10 domain experts were consulted to identify additional challenges in India's EV adoption involving EV charging infrastructure.

Accordingly, 11 significant challenges of EV charging infrastructure development in India have been identified–seven through the comparative analysis of the literature review and four from the focussed group study. Secondary data provides insight into the situation around developed countries and in developing countries, specifically in India. Finally, the Government of India's measures and priorities to facilitate such a development are emphasised.

Research limitations/implications

The study can help policymakers/researchers understand the gaps and align measures to address the challenges. A focussed group study may have its limitations due to the perception of the experts.

Originality/value

The systematic literature review of 43 articles using comparative analysis and subsequently a focussed group study of experts to verify and add challenges has made the study unique.

• Electric vehicles
• Charging infrastructure
• Charging stations

Kore, H.H. and Koul, S. (2022), "Electric vehicle charging infrastructure: positioning in India", Management of Environmental Quality , Vol. 33 No. 3, pp. 776-799. https://doi.org/10.1108/MEQ-10-2021-0234

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Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach

• Published: 18 May 2022
• Volume 16 , pages 221–238, ( 2022 )

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• Sushil Kumar Dixit   ORCID: orcid.org/0000-0002-4228-9821 1 &
• Ashirwad Kumar Singh   ORCID: orcid.org/0000-0002-0349-7038 2  

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Electric mobility has been around for a long time. In recent years, with advancements in technology, electric vehicles (EVs) have shown a new potential to meet many of the challenges being faced by humanity. These challenges include increasing dependence on fossil fuels, environmental concerns, challenges posed by rapid urbanization, urban mobility, and employment. However, the adoption of electric vehicles has remained challenging despite consumers having a positive attitude toward EVs and big policy pushes by governments in many countries. Marketers from the electric vehicle (EV) industry are finding it difficult to identify genuine buyers for their products. In this context, the present study attempts to develop a machine learning model to predict whether a person would “Buy” or “Won’t Buy” an electric vehicle in India. To develop the model, an exploration of EV context was done first by conducting a text analysis of online content relating to electric vehicles. The objective was to find frequently occurring words to gain a meaningful understanding of the consumer’s interests and concerns relating to electric vehicles. The machine learning model indicates that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electrical vehicle purchase in India. The level of education, employment, and government subsidy are not significant predictors of EV uptake.

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1 Introduction

In business and economics literature, the term “industry” has been defined in varied context with different meanings for different purposes. Researchers and policy makers define industries depending on their objectives [ 1 , 2 , 3 , 4 ]. In general, “industry” refers to a set of business activities that is slightly domain specific. With this view, the authors define “electric vehicles industry” as a subset of automobile industry, including all businesses involved in the manufacture, trade, and service of all types of vehicles powered by electrical energy, and associated and ancillary businesses. Electric vehicles are considered one of the most important means by which some of the serious challenges being faced by modern societies are met, e.g., energy security, environmental deterioration, and urban mobility.

All industries evolve in time and space. New industries emerge and old ones vanish with changes in technology or consumer preferences. For the last few years, the electric vehicle industry has evolved in differing contexts. In case of electric vehicles (EVs), the government plays a significant role in shaping not only the perception, but also adoption of EVs by the masses. Governments across the world are coming up with electric vehicle policy, focusing on reducing dependence on fossil fuels, meeting environmental concerns and challenges posed by rapid urbanization, enhancing employment, among others. Governments in many countries have facilitated the adoption of EVs by policy interventions such as supporting research and development, infrastructure development, and financial incentives to industry and consumers.

Over the last few years, the Indian government has started focusing on electric vehicles [ 5 , 6 ]. Recently, the Indian government declared that it aims to have EV sales account for 30% of private cars, 70% of commercial vehicles, and 80% of two and three wheelers by 2030 as there is an immediate need to de-carbonize the transport sector [ 7 ]. The central and state governments have both initiated policy measures to promote manufacture and adoption of EVs. To date, 15 state governments have announced EV policy for their states. Key components of the Indian government’s EV policy are making electric vehicles economically viable, developing charging/swapping infrastructure, technology advancement, and focusing on small and public vehicles to make an early impact. The EVs are also seen to contribute to economic development and employment in India. Many automobile manufacturers have recently launched EV models in two-wheeler and four-wheeler segments.

At the time of conducting this study, the Indian automobile industry was undergoing a severe slowdown, affecting overall consumer perception and sentiments. Retail prices of automobile fuel were at an all-time high, and economically, India’s GDP growth rate had slowed down. Steps like demonetization and Goods and Services Tax (GST) implementation has also negatively affected many small- and medium-size industries. The Reserve Bank of India (RBI) has been continuously reducing the policy rate, and the COVID-19 pandemic has also impacted all industries across the word, including the automobile industry.

The EV industry is moving at a fast pace in most of the countries, not only in terms of evolution of technology, but also in terms of government policy and consumer expectations. Studies conducted in the EV domain in the past will become less relevant soon due to the fast-evolving nature of the industry. Many studies have been conducted in the past on the Indian automobile industry or its traditional segments, but the literature on the EV industry in India is limited and fragmented. Most of the earlier studies attempted to understand consumer sentiments toward electrical vehicles in western developed countries and China. Few studies focusing on understanding electric vehicle uptake were also conducted in the context of developed nations. In the past, only a few studies were conducted explaining consumer understanding and expectations toward EVs in the Indian context [ 8 , 9 ]. A study focusing on consumer concerns for electric vehicles and understanding the factors affecting electric vehicle uptake was missing in the Indian context. Indian policy makers and industry professionals lacked much needed insight into the EV domain. The present study aims to bridge this glaring gap in the literature. The study will help government policymakers and business professionals to understand Indian consumers’ concerns, which will help them design better policies and strategies to give a big boost to EV adoption by Indian consumers.

This research paper is divided into two sections. Section 1 focuses on text analysis of Indian consumers’ posts on social media platforms to understand the user concerns toward electric vehicles. The text analysis of social media posts along with the review of existing literature provides insights into the context of the Indian electric vehicle industry, as well as the possible factors affecting EV uptake in India. Section 2 presents a machine learning model to predict who will buy an electric vehicle in India. It considers demographic, social, contextual, level of environmental awareness, and other relevant considerations of Indian consumers to develop a machine learning classification model that could predict whether a person would “Buy” or “Won’t Buy” an electric vehicle.

1.1 Objectives of Research

The two specific objectives of this study are:

Understanding Indian consumers’ concerns with electrical vehicles.

Developing a Predictive Machine Learning Model that can classify whether an Indian consumer will “Buy” or “Won’t Buy” an electric vehicle.

1.2 Literature Review

As previously mentioned, most of the existing studies examining consumer sentiments and factors relating to electrical vehicles have been conducted in either western or Chinese context. Shepherd et al. [ 10 ] developed a system dynamics model using factors such as subsidies, vehicle driving range, and availability of charging points, and concluded that subsidies have little impact, except in conditional marketing scenarios. Coffman et al. [ 11 ] concluded that despite significant performance improvements, most governments’ goals for EV uptake could not be met. Mixed evidence was found for the role of government incentives in EV uptake; however, public charging infrastructure availability was found to have a significant impact. The authors also noted the presence of an “attitude–action” gap, indicating a significant gap between having a positive attitude toward electric vehicles and actually buying one. In their study, Christidis and Focas [ 12 ] identified that income, educational attainment, and urbanization level had a significant impact on EV uptake in the European Union (EU). The study also found that the local conditions and regional variations have a major, if not deciding, effect on EV purchase. Kim et al. [ 13 ] in their study of 31 countries found that the share of electric vehicles in different markets was correlated with their relative price as compared to internal combustion engine vehicle, number of models available, and vehicle driving range. However, they observed that the relationship between electric vehicle market share and availability of charging infrastructure was insignificant. While studying electric vehicle adoption in the USA, Soltani-Sobh et al. [ 14 ] found that electricity price, use of urban roads, and government incentives play a significant role in EV adoption.

Wang et al. [ 15 ] studied factors affecting public acceptance of electric vehicles in Shanghai, China, and concluded that the level of available technology, marketing efforts, perceived risks, and the level of environmental awareness have significant effects on electric vehicle acceptance. In their study in Thailand, Thananusak et al. [ 16 ] found that performance factors like driving range, speed, and safety were more important than the availability of charging infrastructure, financial considerations like purchase and resale price, and operating and maintenance costs. One important finding was that an individual’s environmental concerns affect their decision to purchase electric vehicles, and they were also willing to pay a higher price premium for electric vehicles due to their positive impact on the environment. However, the price premium factor was found to have a negative moderating effect on the relationship between their intention to buy an electric vehicle and environmental concern. Tu and Yang [ 17 ] in their study on Taiwanese consumers found that resource availability and opinions from consumers surroundings, along with their environmental awareness, influence consumer EV purchase intentions. Li et al. [ 18 ] in their systematic study of 1846 papers to understand the factors affecting EV purchase found that all factors can be categorized into demographic, situational, and psychological factors. In their study on electric vehicle usage, Sang and Bekhet [ 19 ] found that for Malaysian consumers, the electric vehicle acceptance was significantly related to demographics, financial benefits, performance attributes, environmental concerns, social influences, infrastructure availability, and government interventions.

Kim et al. [ 20 ] in their study in Korea examining consumer intentions for purchasing an electric vehicle found that prior experience in driving electric vehicles, along with factors like number of vehicles in the household, educational achievement, availability of parking, and perception of government incentives significantly affect consumers’ intentions for purchasing electric vehicles. Sierzchula et al. [ 21 ] in their study in 30 countries found that the electric vehicle market share in different countries was positively correlated with financial incentives, availability of charging infrastructure, and local production. It was further observed that availability of charging infrastructure had the strongest correlation with electric vehicle adoption. Verma et al. [ 9 ] in their study on identifying factors affecting electric vehicle adoption in Bangalore, India, noted that the key motivators in electric vehicle adoption were perceived environmental benefits and financial incentives. Kumar et al. [ 8 ] studied challenges to the adoption of electric vehicles and concluded that sharing economy and public utilities will play a critical role in EV purchase, considering the high cost coupled with consumers’ low purchasing power and lack of availability of charging infrastructure in India. The study also recognized the role of government in terms of interventions at different levels to meaningfully enhance EV adoption in India. A 2020 study by Castrol in India noted that consumers generally have a positive attitude toward electric vehicles and estimated the Indian EV market would reach $2 billion by 2025. The study identified vehicle price, charging time, and driving range as the most important challenges in EV adoption in India. The average price point of $3100, charging time of 35 min, and vehicle driving range of 401 KM were identified as the tipping points to achieve mainstream EV adoption in India [ 22 ]. Higueras-Castillo et al. [ 23 ] in their study to find factors that affect electric vehicle purchase intentions in Spain conclude that driving range, financial incentives, and vehicle reliability are the most important predictors of the purchase intention. Bennett and Vijaygopal [ 24 ] found that a weak link exists between attitude and willingness to purchase an electric vehicle. Lin and Wu [ 25 ] examined the reasons for electric vehicle purchase by Chinese consumers and concluded that demographic characteristics such as gender, age, and marital status, along with attitude-related factors such as network externality, environmental awareness and concerns, price acceptability, government incentives, and vehicle performance have a significant impact on consumers’ willingness to purchase electric vehicles.

Therefore, it can be concluded that a majority of the earlier studies are context specific. The factors identified and studied also vary from one context to other. The studies do not converge in terms of identifying and listing factors affecting electric vehicle uptake in different contexts. There is no comprehensive list of the factors affecting electric vehicle uptake; thus, the present study aims to not only list the factors that are relevant in the current Indian context, but also develop a model to predict who will buy an electric vehicle in India.

1.3 Research Design

The present study was conducted in two stages. The first stage focused on identifying consumer interests and concerns related to electric vehicles. This stage is similar to opinion extraction or sentiment classification [ 26 , 27 ], and involves gathering and analyzing individuals’ opinions about some issue, event, product, etc. [ 28 , 29 ]. In the research context, opinion extraction can be understood as exploring and understanding issues that matter to Indian consumers in the electric vehicle context. Understanding public opinion helps in making better decisions. Presently, social media has become an important tool to express opinions on the issues that really matter to the masses [ 30 , 31 ]. Content created and shared in the EV context on Twitter by Indian people during January and February 2021 was collected by using N-Capture, i.e., a web browser extension that allows quick access and capture of web and social media content based on the keywords of interest. As N-Capture accesses all the data available on selected keywords, it can be treated like a census rather than a sampling study. To understand the opinions and concerns of Indian people for electric vehicles, a text analysis of the collected content was performed. Therefore, this stage was exploratory in nature with a focus on understanding Indian consumers’ opinions and concerns for electric vehicles. Similar approaches were also adopted in earlier studies [ 32 , 33 , 34 ]. The accessed text was analyzed with the help of SPSS and R. The outcome was a frequency count for the most often occurring words and word combinations. The findings can be presented either as a frequency distribution table or a word cloud. A word cloud presents high-frequency words in a visualization with different sizes. The size of different words indicates the frequency of its occurrence [ 35 ]. The word cloud provided much needed understanding about EV concerns of Indian consumers. Understanding consumer concerns and opinions relating to electric vehicles helped the researchers in designing a questionnaire for the second section of this paper.

Section 2 of the paper develops a predictive machine learning model to predict who will or will not buy EVs. This may be treated as descriptive research using survey methodology. A questionnaire was developed for the purpose of data collection. The questionnaire collected data on the parameters of concerns identified from the literature review and text analysis findings, and was tested for content/face validity by taking the opinion of 8 experts [ 36 ]. The experts were consistent in their opinions about the relevance of the parameters included in the questionnaire. Some changes related to the wording of statements were made. The changes suggested by the experts were incorporated into the questionnaire, which was sent to the respondents as a Google document to collect the data. The sampling methodology is convenience sampling, the reason for which is its efficiency in terms of time and money. In addition, due to electric vehicles being a subject of common concern, the sampling method provided a readily available sample in the given research context. A total of 245 respondents returned usable questionnaires. The collected data was analyzed with the help of SPSS and R to develop a predictive machine learning model.

2 Analysis and Findings

2.1 understanding stakeholders’ concerns using text analysis.

Social media, e-newspapers, and review websites have now become an active tool for everyone to express their views and opinions on specific issues. Analyzing these helps in gaining insights and a general overview of public opinions or concerns. India is a highly populated country with a huge population falling under the group of “working-age population,” and, according to the Ministry of Statistics and Programme Implementation, the government of India is 15–64 years of age. The digital penetration in India has increased significantly over the last few years. As per the Telecom Regulator Authority in India on June 30th, 2019, the tele-density, i.e., the number of telephonic connections for every 100 individuals living in the area, in Indian urban areas was 160. Further availability of 4G and LTE services across most of India has made the digital space more vibrant than ever.

Social media has now become a platform for exchanging ideas, concerns, and opinions by the masses. Twitter is the most widely used platform for this purpose by all groups of people. According to Statista, the number of Twitter users in India was estimated to be 24.45 million in October 2021. Almost all major influencers such as corporate leaders, policymakers, policy advocates, journalists, and media houses have a Twitter account; therefore, the platform is one among many social media platforms one can use to gain an insight into what consumers are discussing. For this study, tweets were extracted, and text analysis was carried out to understand what online users are talking about regarding electric vehicles in India. The tweets were collected by using hashtags #EVIndia and #EVIssues. The extracted tweets were then analyzed for the most frequently occurring words in the dataset. The sole purpose of this was to understand what the most frequently discussed topics are related to electric vehicles. Then, a word cloud was formed for better visualizations, which also became the basis for the questionnaire that was later floated to make a predictive model that could classify whether a consumer will “Buy” or “Won’t Buy” an electric vehicle.

2.2 Hashtag: #EVIndia

The first hashtag we used to collect content was #EVIndia. To analyze the collected content, a frequency bar plot and word cloud were formed, which are presented in (Figs.  1 , 2 ). It can clearly be seen that the most frequently used word was “electric,” followed by “charging,” “vehicle,” “battery,” and so on. It can be concluded from these high-frequency words that the Indian consumers are talking about electric vehicles. This simply means that electric vehicles have attracted the attention of Indian users and they are discussing and sharing their concerns. When it comes to concerns, the words “charging” and “battery” are used most frequently, indicating that consumers are most concerned about battery-charging issues. The word “experience” is another high-frequency word, but surprisingly, they are not talking much about vehicle price, government incentives, or the maintenance costs.

Frequency count of most frequently occurring words for #EVIndia

Word cloud visualization of most frequently occurring words #EVindia

2.3 Hashtag #EVIssues #EVIndia

These two hashtags together were chosen deliberately to capture public opinion and concerns related to EVs in India. The frequency bar plot and word cloud obtained are presented in (Figs.  3 , 4 ). From the bar plot, it can be observed that “battery” and “products” are the words most often used. Other high-frequency words are “experience,” “solution,” “showcased,” “innovators,” and “exhibitors.” This also indicates that consumers are most concerned about the battery and related issues. Frequent use of the word “innovators” may indicate a discussion about the firms innovating in the domain of electric vehicles. They also discuss showcasing and exhibition of electrical vehicles and related technologies. In this case, other words such as “price,” “operating cost,” and “subsidies” did not turn up as words occurring with high frequency.

Frequency count of most frequently occurring words for #EVIssues #EVIndia

Word cloud visualization of most frequently occurring word for #EVIssues #EVIndia

In summary, from the above text analysis, it may be concluded that Indian consumers are aware, concerned, and talking about electric vehicles and related issues. However, the amount and deepness of discussion relating to electric vehicles is quite limited. Consumers are most concerned about battery and battery-charging issues. Surprisingly, vehicle price and maintenance costs were not something they were concerned or talking much about.

2.4 Model to Predict Who Will Buy

A predictive machine learning model was developed to predict whether a consumer in India will or will not buy an electric vehicle depending on the input variables. There are many popular machine learning classification models that can predict with decent accuracy, but in this research paper, the logistic regression algorithm was used for forecasting. The advantages of using a logistic regression are that the normalization of data is not required, scaling of the data is not required, and missing data does not impact model building. However, one of the disadvantages of using logistic regression models is that training analysts to use the model takes longer, which at times becomes a concern [ 37 , 38 , 39 ].

Surveys are commonly used to understand consumer behavior. A primary survey involves first-hand data collection by the researcher, and the data obtained is further analyzed to gain insights. To collect data for the model, a questionnaire was floated among the respondents. The variables chosen were based on the literature review and the outcomes obtained from the text analysis of the tweets. The variables used in the model are a combination of demographic, social, contextual, level of environmental awareness, and other relevant considerations of Indian consumers. The predictive machine learning model developed can classify whether an Indian consumer would “Buy” or “Won’t Buy” an electric vehicle. A total of 245 respondents replied to the google form questionnaire sent to them. The (Table 1 ) presents the data dictionary for the variables chosen for the questionnaire.

2.5 Sample Description

The survey questionnaire was floated among respondents and received a total of 245 usable responses. The sample description is presented in (Table 2 ). The respondents consisted of 140 males and 105 females. In terms of their marital status, 59% were married and 41% were unmarried. Regarding their age, the majority of respondents were less than 25 years of age representing 44% of the total and were closely followed by the 26- to 35-year age group representing 38%. People above these two age groups represented only 22%; thus, the majority consisted of younger generation respondents. The reason for this could likely be that the younger respondents are more aware and concerned about EVs in India. In terms of educational attainment, a majority were post-graduate and represented 44% of respondents. This was followed by graduates with 33% representation. People who had not graduated represented 22% of the respondents. In terms of income, the majority of the respondents were from below 5 Lakh income group representing 45%, followed by 5–8 Lakh income group representing 31%. This is in line with the large proportion of respondents who were from the student community or early on in their employment. In terms of employment, the majority of 63% was in service, followed by 24% in business, and 12% in the unemployed category. In terms of their geographical location, east, west, north, south, and central India were represented with 25, 24, 29, 12, and 10% respondents in each group, respectively. From the age distribution, it can be observed that the sample has a slight bias toward the younger population. This seems to be normal because it is these younger generation people who are typically more concerned about new technological developments, including electric vehicles. This group is also more active in voicing their concerns relating to a subject of interest on internet and social platforms. Therefore, the sample was a fair representation of all groups from all geographical regions in India in the context of the present study. Similarly, the results of present research may be generalizable over the larger Indian population.

Before proceeding to the logistic regression model building, correlation among the continuous variables were explored. No variable was found to have a strong correlation with other variables, which indicates that the issue of multicollinearity was not present. Multicollinearity is a statistical phenomenon in which predictor variables in a logistic regression model are highly correlated. The existence of collinearity inflates the variances of the parameter estimates, resulting in incorrect inferences about relationships between predictor and outcome variables [ 40 ].

2.6 Machine Learning Classification Model

A logistic regression machine learning model was developed on SPSS for the data collected and outputs have been summarized in (Tables 3 , 4 , 5 , 6 , 7 , 8 ). Logistic regression is a classification technique that predicts whether something is true or false. The output of the logistic regression model is presented below. As previously mentioned, a total of 245 cases were included in the model. No instance of a missing case was reported. The dependent variable was coded as 1 to indicate an intention to buy, and 0 for no intention to buy an electrical vehicle. (Table 5 ) presents the omnibus tests of the model coefficients. The value given in the significance column is the probability of getting a Chi Square statistic (114.525) when the null hypothesis is true. The model is statistically significant as the p values reported are less than 0.05. The degree of freedom (df) column is an indication of the number of predictors in the model. For the current model, df is 13, which indicates that the model uses 13 predictors.

(Table 6 ) presents the model summary. The logistics regression does not have an R 2 to predict the variation in the outcome variable that can be explained by the model predictors as the case in OLS regression. In place of R 2 , a large variety of pseudo- R 2 statistics has been developed. Two such statistics developed by Cox and Snell and Nagelkerke are presented in (Table 6 ). However, these statistics are not good equivalents to R 2 statistics on OLS. These should be treated as supplementary to other evaluative indices such as overall evaluation of the model, test of individual regression coefficients, and goodness-of-fit test statistics.

The classification (Table 7 ) is used to calculate model accuracy, precision, and recall. The “observed” column presents the number of “Buy” and “Won’t Buy” as the dependent variables. The “predicted” column presents predicted values of the dependent variables based on the full logistic model. 64 cases are observed to be “Won’t Buy” and are correctly predicted to be “Won’t Buy;” 135 cases are observed to be “Buy” and are correctly predicted to be “Buy.” On the other hand, 27 cases are observed to be “Won’t Buy,” but are predicted to be “Buy;” 19 cases are observed to be “Buy” but are predicted to be “Not Buy.”

Table 8 presents the variables in the logistic regression equation. Different columns of this table present significant information to predict the outcome variable. Column B indicates log-odds units to predict dependent variables from independent variables. The prediction equation is:

The estimates for different variables indicate a predicted increase or decrease in the predicted log-odds to buy an electric vehicle that would be indicated by one unit increase or decrease holding all other predictors constant. For all predictor variables that are not significant, the coefficients are not significantly different from 0. To identify coefficients that are significant values in the column labeled Wald are used. These columns provide the Wald chi-square value and two-tailed p value used in testing the null hypothesis that the coefficient (parameter) is 0. Each p value is compared with the preselected value of alpha to determine whether it is a statistically significant predictor. Coefficients having p values less than alpha are statistically significant. To predict the purchase of an electric vehicle, the authors have chosen alpha to be 0.05, which means that in all cases where alpha is reported to be less than 0.05, the null hypothesis can be rejected, and it can be concluded that the coefficient is significantly different from 0.

From the Sig, it can be observed that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electric vehicle purchase. On the other hand, level of education, employment, and government subsidy were not found to be significant predictors of e -vehicle purchase or uptake. The constant of the model was also reported to not be a statistically significant predictor of electrical vehicle purchase. Exp(B) are the odds ratios for the predictor e-vehicle “Buy” or “Won’t Buy.” These are exponentials of the coefficients. The odds ratio indicates the change in the odds of the outcome variable given a unit change in any predictor variable.

2.7 Conclusions and Suggestions

After carrying out the literature review, it can be concluded that the electric vehicle industry in different regions and countries is at different levels of evolution. Many countries are making serious efforts in the development of electric vehicles and related infrastructure. Governments are also providing right policy, environment, and financial support to increase electric vehicle uptake. A wide variety of factors has been studied to influence the uptake of electric vehicles in different contexts. From the text analysis, it can be concluded that people are talking about electric vehicles in India, but the issues they are discussing on the internet and social media platforms are not very serious or deep. The reason for this lower interest is likely that the availability of electric vehicles is still limited for common consumers in India. The most used words in Indian electric vehicle context are “battery,” “charging,” and “experience.” Government subsidies or incentives for increasing electric vehicle uptake were not significantly discussed. It can be concluded from the text analysis that charging stations and batteries continue to be the most discussed words in the EV context on social media.

A Logistic Regression model with 81.22% accuracy was developed, which classifies consumers into either the “Buy” or “Won’t Buy” category. The model included age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior as significant predictors to classify a consumer in either category. However, level of education, employment, and government subsidy were not found to be significant predictors in e-vehicle purchase or uptake. The findings of the model can be used by electric vehicle marketers to enhance design, delivery, and marketing for better uptake of electric vehicles in India’s personal passenger vehicle segment.

2.8 Scope for Future Research

For the present study, the text analysis carried out was based on random tweets obtained from N-Capture software and thus future research may explore and capture conversations from a greater number of social media platforms. The wider content is expected to provide a better understanding of people’s opinions and concerns relating to electric vehicles. Similarly, a predictive model may be developed with better indicators that may also evolve with time. Finally, for data collection, a wider sample covering respondents with a wide demographic and regional representation may be surveyed to obtain results with better generalization potential.

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Dixit, S.K., Singh, A.K. Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach. Rev Socionetwork Strat 16 , 221–238 (2022). https://doi.org/10.1007/s12626-022-00109-9

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A review on electric vehicles: technologies and challenges.

1. Introduction

• Zero emissions: this type of vehicles neither emit tailpipe pollutants, CO 2 , nor nitrogen dioxide (NO 2 ). Also, the manufacture processes tend to be more respectful with the environment, although battery manufacturing adversely affects carbon footprint.
• Simplicity: the number of Electric Vehicle (EV) engine elements is smaller, which leads to a much cheaper maintenance. The engines are simpler and more compact, they do not need a cooling circuit, and neither is necessary for incorporating gearshift, clutch, or elements that reduce the engine noise.
• Reliability: having less, and more simple, components makes this type of vehicles have fewer breakdowns. In addition, EVs do not suffer of the inherent wear and tear produced by engine explosions, vibrations, or fuel corrosion.
• Cost: the maintenance cost of the vehicle and the cost of the electricity required is much lower in comparison to maintenance and fuel costs of traditional combustion vehicles. The energy cost per kilometer is significantly lower in EVs than in traditional vehicles, as shown in Figure 1 .
• Comfort: traveling in EVs is more comfortable, due to the absence of vibrations or engine noise [ 2 ].
• Efficiency: EVs are more efficient than traditional vehicles. However, the overall well to wheel (WTW) efficiency will also depend on the power plant efficiency. For instance, total WTW efficiency of gasoline vehicles ranges from 11% to 27%, whereas diesel vehicles range from 25% to 37% [ 3 ]. By contrast, EVs fed by a natural gas power plant show a WTW efficiency that ranges from 13% to 31%, whereas EVs fed by renewable energy show an overall efficiency up to 70%.
• Accessibility: this type of vehicle allows for access to urban areas that are not allowed to other combustion vehicles (e.g., low emissions zones). EVs do not suffer from the same traffic restrictions in large cities, especially at high peaks of contamination level. Interestingly, there was a recent OECD study that suggests that, at least in terms of Particulate Matter (PM) emissions, EVs will unfortunately not improve the air quality situation [ 4 ].
• Driving range: range is typically limited from 200 to 350 km with a full charge, although this issue is being continually improved. For example, the Nissan Leaf has a maximum driving range of 364 km [ 6 ], and the Tesla Model S can reach more than 500 km [ 7 ].
• Charging time: full charging the battery pack can take 4 to 8 h. Even a “fast charge” to 80% capacity can take 30 min. For example, Tesla superchargers can charge the Model S up to 50% in only 20 min, or 80% in half an hour [ 7 ].
• Battery cost: large battery packs are expensive.
• Bulk and weight: battery packs are heavy and take up considerable vehicle space. It is assumed that the batteries of this type of vehicles have an approximate weight of 200 kg [ 8 ], which can vary, depending on the battery capacity.

2. Existing EV-Related Surveys

3. electric vehicles, 3.1. electric vehicles taxonomy.

• Battery Electric Vehicles (BEVs): vehicles 100% are propelled by electric power. BEVs do not have an internal combustion engine and they do not use any kind of liquid fuel. BEVs normally use large packs of batteries in order to give the vehicle an acceptable autonomy. A typical BEV will reach from 160 to 250 km, although some of them can travel as far as 500 km with just one charge. An example of this type of vehicle is the Nissan Leaf [ 24 ], which is 100% electric and it currently provides a 62 kWh battery that allows users to have an autonomy of 360 km.
• Plug-In Hybrid Electric Vehicles (PHEVs): hybrid vehicles are propelled by a conventional combustible engine and an electric engine charged by a pluggable external electric source. PHEVs can store enough electricity from the grid to significantly reduce their fuel consumption in regular driving conditions. The Mitsubishi Outlander PHEV [ 25 ] provides a 12 kWh battery, which allows it to drive around 50 km just with the electric engine. However, it is also noteworthy that PHEVs fuel consumption is higher than indicated by car manufacturers [ 26 ].
• Hybrid Electric Vehicles (HEVs): hybrid vehicles are propelled by a combination of a conventional internal combustion engine and an electric engine. The difference with regard to PHEVs is that HEVs cannot be plugged to the grid. In fact, the battery that provides energy to the electric engine is charged thanks to the power generated by the vehicle’s combustion engine. In modern models, the batteries can also be charged thanks to the energy generated during braking, turning the kinetic energy into electric energy. The Toyota Prius, in its hybrid model (4th generation), provided a 1.3 kWh battery that theoretically allowed it an autonomy as far as 25 km in its all-electric mode [ 27 ].
• Fuel Cell Electric Vehicles (FCEVs): these vehicles are provided with an electric engine that uses a mix of compressed hydrogen and oxygen obtained from the air, having water as the only waste resulting from this process. Although these kinds of vehicles are considered to present “zero emissions”, it is worth highlighting that, although there is green hydrogen, most of the used hydrogen is extracted from natural gas. The Hyundai Nexo FCEV [ 28 ] is an example of this type of vehicles, being able to travel 650 km without refueling.
• Extended-range EVs (ER-EVs): these vehicles are very similar to those ones in the BEV category. However, the ER-EVs are also provided with a supplementary combustion engine, which charges the batteries of the vehicle if needed. This type of engine, unlike those provided by PHEVs and HEVs, is only used for charging, so that it is not connected to the wheels of the vehicle. An example of this type of vehicles is the BMW i3 [ 29 ], which has a 42.2 kWh battery that results in a 260 km autonomy in electric mode, and users can benefit an additional 130 km from the extended-range mode.

3.2. Subsidies and Market Position

4. batteries, 4.1. characteristics of the batteries.

• Capacity. The storage difficulty and cost is one of the main problems of electric power. Currently, this results in the allocation of great amounts of money in the development of new batteries with higher efficiency and reliability, thus improving batteries’ storage capacity. The battery capacity represents the maximum amount of energy that can be extracted from the battery under certain specified conditions. This unit can be expressed in ampere hour (Ah) or in watt hour (Wh), although the latter one is more commonly used by electric vehicles. When considering that, in EVs, the capacity of their batteries is a critical aspect, since it has a direct impact in the vehicles’ autonomy, the emergence of new technologies that enables the storage of a greater energy quantity in the shortest possible time will be a decisive factor in the success of this kind of vehicles. Table 2 shows data that are related to the battery capacities of EVs. As shown, the capacity of batteries is continuously growing and vehicles with more that 100 kWh batteries are expected very soon.
• Charge state. Refers to the battery level with regard to its 100% capacity.
• Energy Density. Obtaining the highest energy density possible is another important aspect in the development of batteries, in other words, that with equal size and weight a battery is able to accumulate a higher energy quantity. The energy density of batteries is measured as the energy that a battery is able to supply per unit volume (Wh/L).
• Specific energy. The energy that a battery is able to provide per unit mass (Wh/kg). Some authors also refer to this feature as energy density, and it can be specified in Wh/L or Wh/kg.
• Specific power. The power that a battery can supply per unit of weight (W/kg).
• Charge cycles. A load cycle is completed when the battery has been used or loaded 100%.
• Lifespan. Another aspect to consider is the batteries lifespan, which is measured in the number of charging cycles that a battery can hold. The goal is to obtain batteries that can endure a greater number of loading and unloading cycles.
• Internal resistance. The components of the batteries are not 100% perfect conductors, which means that they offer a certain resistance to the transmission of electricity. During the charging process, some energy is dispelled in the form of heat (namely, thermal loss). The generated heat per unit of time is equal to the lost power in the resistance, so the internal resistance will have a greater impact in high power charges [ 51 ]. Thus, more energy will be lost during quick charging processes when compared to slow ones. Therefore, it is highly important that batteries can support quick charging and higher temperatures induced due to the internal resistance. In addition, the decrease of this resistance can reduce the charging time that is required, which is one of the most important drawbacks of this type of vehicles today.
• Efficacy. It is the percentage of power that is offered by the battery in relation to the energy charged.

4.2. The Cornerstones: Cost, Capacity, and Charging Time

4.3. different components and battery types.

• Lead-acid batteries (Pb-PbO 2 ). These batteries were invented in 1859 and are the oldest kind of rechargeable battery. Although this kind of battery is very common in conventional vehicles, it has also been used in electric vehicles. It has very low specific energy and energy density ratios. The battery is formed by a sulfuric acid deposit and a group of lead plates. During the initial loading process, the lead sulfate is reduced to metal in the negative plates, while, in the positives, lead oxide is formed (PbO 2 ). The GM EV1 and the Toyota RAV4 EV, are examples of vehicles that used this kind of batteries.
• Nickel-cadmium batteries (Ni-Cd). This technology was used in the 90s, as these batteries have a greater energy density [ 66 ], but they present high memory effect, low lifespan, and cadmium is a very expensive and polluting element. For these reasons, nickel-cadmium batteries are currently being substituted by nickel-metal-hydride (NiMH) batteries.
• Nickel-metal-hydride batteries (Ni-MH). In this type of batteries, an alloy that stores hydrogen is used for negative electrodes instead of cadmium (Cd) [ 67 ]. Although they present a higher level of self discharge than those of nickel-cadmium, these batteries are used by many hybrid vehicles, such as the Toyota Prius and the second version of the GM EV1. The Toyota RAV4 EV, apart from having a lead-acid version, also had another with nickel-metal-hydride.
• Zinc-bromine batteries (Zn-Br 2 ). These kinds of batteries use a zinc-bromine solution stored in two tanks, and in which bromide turns into bromine in the positive electrode. This technology was used by a prototype, called ”T-Star”, in 1993 [ 68 ].
• Sodium chloride and nickel batteries (NA-NiCl). Also being referred to as Zebra, they are very similar to sodium sulfur batteries. Their advantage is that they can offer up to 30% more energy in low temperatures, although its optimum operating range is between 260 °C and 300 °C. These kinds of batteries are ideal for its use in electric vehicles [ 69 ]. The disappeared Modec company used them in 2006.
• Sodium sulfur batteries (Na-S), which contain sodium liquid (Na) and sulfur (S). This type of battery has a high energy density, high loading and unloading efficiency (89–92%), and a long life cycle. In addition, their advantage is that these materials have a very low cost. However, they can reach functioning temperatures of between 300 and 350 °C [ 70 ]. This type of batteries is used in the Ford Ecostar, the model that was launched in 1992–1993.
• Lithium-ion batteries (Li-Ion). These batteries employ, as electrolyte, a lithium salt that provides the necessary ions for the reversible electrochemical reaction that takes place between the cathode and anode. Lithium-ion batteries have the advantages of the lightness of their components, their high loading capacity, their internal resistance, as well as their high loading and unloading cycles. In addition, they present a reduced memory effect.

5. Charging of Electric Vehicles

• AC Level 1. Standard electrical outlet that provides voltage in AC of 120 V offering a maximum intensity of 16 A, which serves a maximum power of 1.9 kW.
• AC Level 2. Standard electrical outlet with 240 V AC and a maximum intensity of 80 A, so it offers a maximum power of 19.2 kW.
• DC Level 1. External charger that by inserting a maximum voltage of 500 V DC with a maximum intensity of 80 A, it provides a maximum power of 40 kW.
• DC Level 2. External charger that, by inserting a maximum voltage of 500 V DC with a maximum intensity of 200 A, provides a maximum power of 100 kW.

5.1. Charging Modes

• Mode 1 (Slow charging). It is defined as a domestic charging mode, with a maximum intensity of 16 A, and it uses a standard single-phase or three-phase power outlet with phase(s), neutral, and protective earth conductors. This mode is the most used in our homes.
• Mode 2 (Semi-fast charging). This mode can be used at home or in public areas, its defined maximum intensity is of 32 A, and, similar to the previous mode, it uses standardized power outlets with phase(s), neutral, and protective earth conductors.
• Mode 3 (Fast charging). It provides an intensity between 32 and 250 A. This charging mode requires the use of an EV Supply Equipment (EVSE), a specific power supply for charging electric vehicles. This device (i.e., the EVSE) provides communication with the vehicles, monitors the charging process, incorporates protection systems, and stops the energy flow when the connection to the vehicle is not detected.
• Mode 4 (Ultra-fast charging). Published in the IEC-62196-3, it defines a direct connection of the EV to the DC supply network with a power intensity of up to 400 A and a maximum voltage of 1000 V, which provides a maximum charging power up to 400 kW. These modes also require an external charger that provides communication between the vehicle and the charging point, as well as protection and control.

5.2. Connectors

• They are sealed solutions (not affected by water or humidity).
• They carry a mechanic or electronic blockage.
• They enable communication with the vehicle.
• Electricity is not supplied until the blockage system is not activated.
• While the blockage system is activated, the vehicle cannot be set in motion, so that a vehicle cannot leave while plugged.
• Some connectors are able to charge in three-phase mode.
• AC pins, two pins to provide power to the vehicle (phase and neutral).
• Ground connection, a security measure, which connects the electrical system to the ground.
• Proximity detection, which avoids the vehicle to move while plugged.
• Pilot Control, which allows communication with the vehicle.
• Type 1 (SAE-J1772-2009) Yazaki. With the aim of finding a standardized connector, the Type 1 AC charging, apart from being included in the SAE-J1772 standard, was also included in the IEC-62196-2. In fact, this connector is commonly found in charging equipments for EVs in North America and Japan [ 80 ], and it is used by a great amount of vehicles, such as the Nissan Leaf, the Chevrolet Volt, the Toyota Prius Prime, the Mitsubishi i-MiEV, the Ford Focus Electric, the Tesla Roadster, and the Tesla Model S. This connector can be observed in Figure 7 a.
• Type 2 (VDE-AR-E 2623-2-2) Mennekes. It was originally designed to be used in the industrial sector, so it was not specifically designed for EVs (see Figure 7 c). In single-phase it is limited up to 230 V, but, in three-phase, is able to hold high voltages and intensities. This connector has 7 pins, i.e., four for the power (in three-phase mode), one ground connection, and two pins to communicate with the vehicle (blockage and communications). An example of a vehicle that uses this connector is the Renault Zoe, which can be charged with the Mennekes connector up to 43 kWh. Although, at first, it was not designed for fast charging, Type 2 also includes another connector, called Combined Charging System (CCS) (see Figure 7 d), being essentially an adapted Mennekes adapted to supply up to 400 A to 1000 V, which would imply a charging power up to 400 kWh [ 81 , 82 ].
• Type 3 (EV Plug Alliance connector) Scame. Single-phase and three-phase connector, designed by the EV Plug Alliance in 2010. It supplies 230 V/400 V and from 16 to 63 A [ 83 ]. France and Italy suggested the use of this connector for their vehicles (see Figure 7 e), but, due to the poor acceptance, the production of Type 3 connectors has been finally abandoned.
• Type 4 (EVS G105-1993) CHAdeMO. Promoted by TEPCO (Tokyo Electric Power Company), it is commonly found in the EVs charging equipment in Japan, although it is also used in Europe and USA (see Figure 7 f). CHAdeMO is designed to supply fast charges in DC. In its first versions, it held up to 400 V, starting the charge with up to 200 A. Nowadays, CHAdeMO chargers have already been designed with 150 kW power, and they aim to reach 350 kW [ 84 ]. This connector has ten pins, two for DC power supply, one for ground connection, and seven pins for communicating with the network. On the 8th of February of 2018, there existed 7133 CHAdeMO charging points in Japan, 6022 in Europe, and 2290 in the USA [ 85 ]. In fact, it is added to numerous vehicles, such as in the Nissan Leaf, the Nissan e-NV200, the Mitsubishi i-MiEV, and the KIA Soul EV.

6. Power Control and Energy Management

Thermal management and power electronics, 7. challenges of the research and open opportunities, 7.1. new challenges and technologies in batteries for evs.

• Lithium iron phosphate (LiFePO 4 ). This kind of battery presents an energy density of approximately 220 Wh/L, a great durability (they are able to withstand between 2000 and 10,000 cycles) and tolerate high temperatures. However, although this type of battery is starting to be tested in EVs [ 94 ], it still can be found in an early stage of research and development. MIT researchers have managed to reduce its weight and they have developed a prototype-cell that can be completely charged in just 10–20 s, a reduced time if we compare it with the necessary 6 min. for standard battery cells [ 95 ].
• Magnesium-ion (Mg-Ion). These batteries change the use of lithium over magnesium, succeeding in storing more than double the charge and increasing its stability. It is expected that this type of battery can have a 6.2 kWh/L energy density [ 96 ], which would imply 8.5 times more than the best lithium batteries, which are currently able to apply up to 0.735 kWh/L. Organizations, such as the Advanced Research Projects Agency-Energy (ARPA-E), Toyota, or NASA, are investigating this type of battery [ 97 , 98 ].
• Lithium-metal. In these batteries, graphite-anode is replaced by a fine lithium-metal layer. This kind of battery is able to store double of the power than a traditional lithium battery [ 99 ]. SolidEnergy Systems, a MIT startup, have already started to deploy this type of batteries in drones, and it is expected that they can be included in EVs [ 100 ]. Lithium-metal batteries have a high Coulombic efficiency (above 99.1%), withstanding more than 6000 charging cycles, and, after 1000 cycles they maintain an average Coulombic efficiency of 98.4% [ 101 ].
• Lithium-air (Li-air). This kind of battery needs a constant supply of oxygen to conduct the reaction with the lithium. They were initially proposed in the 70s, although it was not until recently that have started to be developed and improved. It is expected that its specific energy reaches around 12 kWh/kg (almost 45 times the current of lithium), which would imply being at the same level as the fuel [ 102 ].
• Aluminum-air. Batteries that are developed with this technology produce electricity from the reaction of oxygen with aluminum. Their main advantage is that this type of battery reaches very large energy densities, attaining 6.2 kWh/L [ 103 ], which allows obtaining a high autonomies (up to 1600 km) [ 104 ]. The price of this kind of battery is decreasing, currently positioning in 300 €/kWh [ 105 ], and their advantage is that they are recyclable.
• Sodium-air (Na 2 O 2 ). The company BASF created a Sodium-air battery with an energy density of 4.5 kWh/L [ 106 ]. In electric vehicles, this type of battery can multiply the autonomy of the current lithium batteries at least thirteen times [ 107 ]. A great advantage of this type of batteries is that sodium is the sixth more abundant element in our planet [ 108 ].
• Graphene. Graphene is a material that is formed by pure carbon, which has a high thermal conductivity and it is extremely light (a one square meter blade weighs 0.77 mg) [ 109 ]. One of the major assets of graphene-based batteries is that they barely heat, enabling fast or ultra-fast charges without significant power losses due to heat. Graphenano, a Spanish company, has created a graphene battery that, added to a GTA Spano vehicle (900 hp), has been able to travel 800 km [ 110 ]. In a high power plug, this battery could be charged in only 5 min. This kind of battery is in an early phase of development, although there exist prototypes of graphene batteries with a specific power of 1 kWh/kg, and it is expected to reach 6.4 kWh/kg soon [ 111 ].

7.2. Improvements in the Charging Process

7.3. communications and ai in electric vehicles, 7.4. eco charge and sustainability, 8. conclusions, author contributions, conflicts of interest, abbreviations.

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Sanguesa, J.A.; Torres-Sanz, V.; Garrido, P.; Martinez, F.J.; Marquez-Barja, J.M. A Review on Electric Vehicles: Technologies and Challenges. Smart Cities 2021 , 4 , 372-404. https://doi.org/10.3390/smartcities4010022

Sanguesa JA, Torres-Sanz V, Garrido P, Martinez FJ, Marquez-Barja JM. A Review on Electric Vehicles: Technologies and Challenges. Smart Cities . 2021; 4(1):372-404. https://doi.org/10.3390/smartcities4010022

Sanguesa, Julio A., Vicente Torres-Sanz, Piedad Garrido, Francisco J. Martinez, and Johann M. Marquez-Barja. 2021. "A Review on Electric Vehicles: Technologies and Challenges" Smart Cities 4, no. 1: 372-404. https://doi.org/10.3390/smartcities4010022

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