Electric vehicle adoption reluctance, customer insights using logistic regression

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Hegde, Prabakar S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9522215/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research examines ecological awareness and acceptance that shape the usage of Electric Vehicles (EVs). Study highlights the transition from “traditionally perceived usefulness” to ‘’customer perceived usefulness” driven by environmental benefits. This study emphasizes the role of batteries in EV adoption. Although there are studies in past around problems with charging and other factors, this study empirically measures the impact of EV charging infrastructure on adoption of electric vehicles and their role in fostering cleaner urban mobility and promoting responsible consumption patterns. This study utilises logistic regression method and considers the factors such as charging time and comfort in mobility as variables. Study underscores the importance of charging time and comfort as important factors in shaping consumer behaviour through prospective consumer survey. The study iterates the role of policies in providing economic incentives for promotion of greener mobility; this study provides avenues for Asian nations to transform the infrastructure challenges into strategic business opportunities for sustainable environment. Electric Vehicle batteries infrastructure SDG 11 SDG 12 policies Introduction Electric vehicles (EVs) represent one of the most promising near-term technological solutions for reducing greenhouse gas emissions and decreasing reliance on fossil fuels that are traditionally associated with conventional automobiles. Despite their numerous environmental and consumer benefits, several barriers continue to hinder widespread adoption. Among these, customer hesitation toward new technologies remains a critical challenge, often reinforcing policies that reflect scepticism about EVs. Currently, EVs are still in the early stages of development, with adoption constrained by concerns over price and driving range. Battery technology plays a pivotal role in shaping EV costs, as high-capacity batteries significantly influence both pricing structures and subsidy mechanisms within the EV ecosystem. While EVs offer cutting-edge innovations capable of mitigating climate change by lowering emissions, externalities such as knowledge spill overs and pollution reduction generate broader societal and economic benefits that are not fully captured in vehicle pricing. Governance and policy interventions along with various other initiatives by industry have tried to address these market inefficiencies, yet socioeconomic factors continue to shape adoption rates. Our study highlights the substantial impact of EV development on environmental protection, while also recognizing that public acceptance remains relatively low, presenting ongoing marketing challenges. Drawing on a questionnaire survey, this paper applies logistic regression modelling to identify key variables influencing consumer adoption of EVs. Ultimately, EVs are positioned to replace conventional vehicles, contributing to a more sustainable and climate conscious future. In alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), EV adoption is positioned as a key driver of sustainable urban mobility and responsible consumer choices. Organizations have undertaken various initiatives to address market inefficiencies, yet socioeconomic factors continue to shape adoption rates. Our study highlights the substantial impact of EV development on environmental protection, while also recognizing that public acceptance remains relatively low, presenting ongoing marketing challenges. Autonomous vehicles are increasingly recognized as a cost-effective solution for sustaining urban transportation by reducing dependence on fossil fuels and lowering carbon emissions, thereby contributing to both ecological and public health benefits (Buekers,J et.al 2014). Many countries have set ambitious targets and implemented strategies to encourage the adoption of electric vehicles (EVs), with projections suggesting that EVs will constitute a significant share of the future automotive industry (Bakker, S et.al 2013). Globally, EV registrations rose dramatically from just six thousand in 2010 to over seven hundred fifty thousand in 2016, and forecasts estimate that by 2030, approximately one hundred fifty million EVs will be on the road (Cazzola et.al 2017 ). EVs are widely regarded as a technological innovation capable of addressing environmental challenges linked to global warming and greenhouse gas emissions. However, their acceptance remains limited without external drivers such as stricter pollution regulations, rising fuel prices, and financial incentives ( Global EV Outlook 2013 ) . Public subsidies and incentives are consistently identified as critical to fostering widespread adoption ( Economic inefficiencies, including uneven product distribution and issues related to emission reduction and information spill overs that further slow the diffusion of EVs (Hidrue et.al 2011 ). Market imperfections distort EV pricing compared to internal combustion engine. A growing body of empirical literature is emerging alongside the rapid expansion of the electric vehicle (EV) sector. While current research provides valuable insights into customer driving behaviours, social assistance programs, delivery systems, and technological advancements, many studies continue to rely on similar assumptions. Notably, most investigations into EV demand are based on surveys and decision-making experiments (Wolbertus R et.al 2018 ) Descriptive studies, however, often focus on the experiences of existing EV owners (Anfinsen et.al 2018 ), the potential benefits that could influence new buyers, and the barriers faced by individuals and organizations in adopting EVs. In contrast, there remains limited empirical work exploring the perspectives and rationales of non-owners or potential buyers. As highlighted by refs. (Hafner et.al 2017 ), there is a shortage of descriptive methodologies that connect reflective questionnaires and preference experiments to individual consumer attitudes and reasoning. Similar critiques have been raised in EV marketing studies (Axsen et.al 2011), which argue that an overemphasis on “mechanical rationality” neglects broader aspects of consumer behaviour (Marsden, G et.al 2017). Further studies such as the present one could inform future research on consumer views regarding EV subsidies, transportation policies, and the need for more subjective investigations across diverse customer groups. Carbon dioxide and other greenhouse gases are recognized as harmful atmospheric pollutants that threaten human health and welfare. Their excessive emission contributes to climate change and global warming. The combustion of fossil fuels in the transportation and electricity sectors remains the largest source of carbon emissions worldwide (Marsden, G et.al 2011)To counter this trend, governments are increasingly promoting EV adoption as a strategy to reduce emissions. While EVs offer significant ecological benefits, widespread adoption continues to face economic, infrastructural, and cultural challenges.(Fan, J.; et.al 2015 ) Emerging transportation issues have driven advancements in automotive technologies, particularly in propulsion systems and autonomous vehicles (Todorovic, et.al 2017 ) By reducing petroleum dependence and carbon emissions, EVs are widely acknowledged as an effective pathway toward sustainable urban transportation, with positive implications for both climate stability and human well-being. In alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production ) , EV adoption plays a critical role in building resilient, low-carbon urban transport systems and encouraging environmentally responsible consumer choices. The transportation sector alone accounts for nearly three-quarters of its carbon dioxide emissions, representing 23% of global emissions (Wang.S. et.al 2016). Addressing these challenges requires substantial reductions in transportation-related emissions (Wang.S. et.al 2016). Consequently, the transition toward renewable energy sources in transport systems has become imperative (Todorovic, et.al 2017 ). By embedding EV adoption within the framework of SDG 11 and SDG 12, this study underscores how sustainable mobility and responsible consumption can jointly contribute to mitigating climate change and advancing global sustainability. The expansion of plug-in electric vehicle (PEV) charging infrastructure, both at home and in public spaces, carries significant societal implications. Widespread accessibility of charging stations is expected to raise public awareness of PEV technology and improve perceptions of its functionality, potentially fostering progressive “green cultural branding” (Community Energy Association 2013 ). Home charging remains a critical factor for consumers considering the purchase of a PEV, as drivers often face limited power supply after completing trips (Bakker, J.et.al). This is a problem for those constrained by vehicle range, knowledge of public charging options can encourage longer travel distances powered by electricity. Nevertheless, prior studies have limitations, as they often rely on small samples of existing PEV owners and drivers, without adequately addressing the perspectives or acceptance levels of non-owners. Projections suggest that increasing charger availability from 10% to 33% could boost EV demand by up to 50%. Moreover, simulations based on respondent data indicate that a tenfold increase in charger accessibility could nearly double demand, raising estimated market share from 2.2% to 8.9%. Research further shows that greater recharge availability enhances consumers’ willingness to pay for new vehicles, reduces time spent locating charging infrastructure, improves individual utility, and increases the likelihood of purchasing a PEV. Numerous studies have identified consumer-related factors that influence the likelihood of acquiring an electric vehicle (EV). Common indicators include education, income, household vehicle ownership, environmental attitudes, and affinity for technology. However, the literature remains divided on which of these characteristics are most critical. Several studies suggest that higher levels of education are associated with a greater probability of being “EV-oriented”, while others report that neither education nor economic status significantly affects EV adoption rates across nations. Studies so far found no evidence that increased income alters the likelihood of being “EV-oriented.” Household vehicle ownership also plays a role: living in a multi-car family has been shown to reduce the probability of EV orientation. Conversely, actively buying or planning to buy a vehicle is a strong predictor of interest in EV ownership. Access to charging infrastructure is equally important; the absence of a dedicated charging outlet may hinder adoption, particularly among residents of apartments or shared housing units. Participants in EV driving trials valued at-home charging for the autonomy it provided (Graham-Rowe, et.al). Evidence regarding the influence of environmental preferences on EV purchase decisions remains mixed, leaving uncertainty about which consumer identifiers best predict long-term EV acceptance. (Graham-Rowe, et.al). Research into EV adoption consistently highlights driving range as the most significant non-financial barrier. It is reported that over 70% of respondents considered limited driving range a “significant disadvantage” or “somewhat of a disadvantage.” Since these surveys were conducted in urban areas, the findings may underestimate concerns in suburban or rural contexts, where longer travel distances are common. The Minneapolis EV cost payback model, focusing on trip chaining and range, suggests that plug-in hybrid electric vehicles (PHEVs) may be more appealing than battery electric vehicles (BEVs). However, Monte Carlo simulations of alternative fuel vehicles (AFVs) indicate that BEVs are more likely to be chosen than PHEVs. Researchers argue that expanding charging infrastructure is a more effective strategy to alleviate range anxiety than simply extending vehicle range. This underscores the importance of charging time. When fast charging is unavailable, consumers place greater value on extended driving range, highlighting the interdependence of range and charging duration. Further it is noted that a one-hour charging time discourages long-distance travel, whereas reducing charging time to ten minutes would allow BEVs to compete with internal combustion engine vehicles in terms of travel efficiency. Further research is needed to explore the relationship between driving range, charging time, and charging network development. Survey-based utility models confirm that EV competitiveness improves when adequate infrastructure is available, with overnight home charging emerging as a significant factor. Studies similarly found strong consumer preference for at-home charging, citing convenience, safety, and security of both the vehicle and charging equipment. Technological advancements have also introduced innovative powertrain systems. The new electric–hydraulic powertrain integrates a traction motor, battery pack, hydraulic pump/motor (secondary component), hydraulic accumulator, reservoir, and hydraulic valves. The hydraulic circuit comprises two subsystems: the drive circuit and the drain circuit. In the drive circuit, a cartridge valve, one-way valve, and two-position four-way valve regulate fluid flow. In case of Electric vehicles (EVs) the battery storage system is both the most critical and the most costly one. To ensure safety and reliability, an accurate battery management system (BMS) is essential for monitoring and controlling battery states. Based on these measurements, the residual useful life (RUL) of the battery can be estimated, helping to prevent potential safety hazards that could damage the battery, compromise the vehicle, or endanger passengers. Key parameters such as battery voltage, current, and temperature over time must be measured to assess battery health and predict RUL. These measurements are typically collected through embedded systems, with the resulting data stored for analysis. Various machine learning techniques including artificial neural networks (ANNs), long short-term memory (LSTM) models, support vector regressors (SVRs), random forest (RF), and boosting methods have been employed to estimate battery RUL. Additionally, optimization algorithms such as particle swarm optimization (PSO) and whale optimization algorithm (WOA) have been integrated with the extreme learning machine (ELM) to enhance prediction accuracy. The root mean square error (RMSE) serves as the primary metric for evaluating algorithm performance. Results indicate that PSO-ELM and WOA-ELM outperform other machine learning approaches, achieving RMSE values of 1.46% and 1.51%, respectively. In comparison, LightGBM, random forest, AdaBoost, XGBoost, and CatBoost yielded higher RMSE values of 2.24%, 2.25%, 2.74%, 2.84%, and 3.56%, respectively (Hussien et al., 2023).Studies in past have conducted on predictive battery thermal management strategy that were developed and validated using a single state-of-the-art BEV platform (Acker, et.al 2026 ). Although the methodology is rigorous, the reliance on one vehicle architecture limits generalizability. Variations in battery chemistry, cooling system design, and actuator configurations across manufacturers may influence the effectiveness of the proposed controller. The analysis of heating strategies under cold conditions (Zhang, Z et.al 2022) was based on controlled simulations and laboratory experiments. Real-world environments introduce additional uncertainties such as fluctuating ambient temperatures, user behaviour, and grid constraints. These factors may affect the accuracy of the performance predictions and the applicability of the results to diverse climates. The Chimp Tangent Search Algorithm (ChTSA) model (Ramy g et.al 2025) was tested under simulated EV network conditions. While the algorithm integrates multiple objectives such as cost, distance, and user preference, the simulation may not fully replicate the complexity of real urban charging ecosystems. Variables such as unpredictable traffic patterns, sudden surges in demand, or infrastructure failures were not incorporated, which could affect scheduling efficiency in practice. The behavioural analysis emphasized charging duration and comfort in mobility as predictors of sustainable transport adoption. Other potentially influential factors such as cost of charging, availability of renewable energy, policy incentives and social norms were not included in the regression model. This narrowed scope may understate the multifactorial nature of adoption decisions Electric vehicles (EVs) offer significant environmental advantages, and many countries are actively working to integrate them into daily life. Recent improvements in battery capacity and charging times have addressed some of the technological challenges, yet the development of widespread charging infrastructure remains essential. Broadly distributed charging facilities, supported by modern facility management systems, can monitor charger status in real time, resolve technical issues, and optimize operational strategies. Digital controllers further enable the creation of smart, city-wide transportation networks. Greater awareness of public charging facilities enhances driver confidence, encouraging longer travel distances powered by electricity and alleviating concerns about limited range. EVs play a pivotal role in the transition to a low-carbon energy system. By shifting charging demands over time, they help balance the intermittent nature of renewable energy sources, making digitalization of mobility increasingly vital. EVs can also connect directly to the grid, supporting the stability of renewable energy dominated systems. This integration reduces emissions from the transportation sector, which remains a major contributor to global CO₂ output. Smart buildings are now being designed to incorporate EV technology, improving energy efficiency, sustainability, and long-term reductions in carbon emissions. By leveraging renewable energy sources, these buildings further minimize environmental impact. EV ratings are typically based on product quality, perceived performance, and reliability. Additional factors such as societal impact, government involvement, and infrastructure development also play a role. Subcategories like “perceived affordability” and “accessibility” are particularly influential in shaping public acceptance and adoption of EVs. In alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), EV adoption supports cleaner, smarter, and more resilient urban transport systems while encouraging responsible consumer choices. To strengthen this connection, our study employs logical regression model to identify the variables such as charging time and comfort in mobility that directly influence consumer behaviour. These methodological insights can be mapped to SDG indicators,. Research Objective The main aim of this study is to find the reasons that affect the buying behaviour of EV consumers, and understanding the barriers in adopting EVs.The additional aim of the study is to identify the comfort and use of Electric vehicle under 24 * 7 economy and intense business competition. We propose the following hypothesis to be tested.. H1 There is a relationship between EV purchases to factors like charging time H2 There is a relationship between EV purchases to factors like comfort in mobility Research methodology The existing literature clearly demonstrates that several factors influence consumer perceptions of electric vehicle (EV) adoption in developed markets. This makes it particularly important to examine consumer behaviour in one of the world’s most populous countries India. Building on prior research and addressing this gap, the present study seeks to identify the key factors shaping consumer satisfaction with EV usage and to evaluate how these dimensions affect consumer recommendations, thereby influencing potential new buyers in India’s rapidly growing EV market. The study is conducted using a convenience sample. Two critical factors have been identified and categorized as Charging Time (CT), and Comfort mobility (CM). Data for this study were collected using a standardized questionnaire administered through Google form with participants across Bengaluru, Karnataka area. Respondents represented diverse demographics, including men and women of varying ages, income levels, and occupational backgrounds such as business, service, and other professions (Table 1 ). Each parameter was measured on open ended,” yes ‘ and No,’( Table 2 ) questionnaire. Logistic regression tests were employed to assess whether significant correlations exist between consumer satisfaction and demographic variables. In alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), this study emphasizes how consumer satisfaction and recommendations can accelerate EV adoption in India. By linking consumer behaviour to sustainable urban mobility and responsible consumption patterns the findings provide actionable insights for policymakers and industry stakeholders. This integration highlights the dual importance of infrastructure development and consumer awareness in fostering a sustainable EV ecosystem. Results Table 1: Demography of the survey respondents Age Gender Educational qualification Occupation Monthly income(INR) > 20 36% Male 57% Below graduate 7% Service industry 27% 25 24% Female 43% Under Graduate 49% Entrepreneur 24% 30 19% Post graduate 24% Students 48% 35 15% Others 20% Others 1% 40 6% 45 0% <45 K 3% * Primary data set/ own source Table2: Survey results Predictor Sample size YES NO Charging Time 200 106 94 Comfort in Mobility 200 104 96 * YES / NO → counts of customers recommending (YES) or not recommending (NO). Predictors → Charging Time and Comfort in Mobility. Table 3: Results of logistic regression Using SPSS 26 Variables P S.E. Wald Df Sig. Charging Time 0.03 0.331 5.071 1 0.000 Comfort in mobility 0.05 0.315 4.403 1 0.000 Constant < 0.05 0.227 3.84 1 0.000 Since the significant value of the factor Charging Time is less than 0.05 and higher than the Wald’s value, it can be inferred that there is significant effect of the factor Charging Time on the EV purchase decisions. The significant value of the comfort in mobility is less than 0.5 it is inferred that there is significant effect of the factor comfort in mobility on the Ev purchase decisions. Logistic Regression Equation From table 3: Charging Time : Coefficient (B) = 0.03 Standard Error (SE) = 0.331 Wald = 5.071 Sig. (p-value) = 0.000 Comfort in Mobility : Coefficient (B) = 0.05 SE = 0.315 Wald = 4.403 Sig. = 0.000 Constant (Intercept) : Coefficient (B) ≈ < 0.05 (likely 0.05 or less) SE = 0.227 Wald = 3.84 Sig. = 0.000 logit model is: logit (p)=β0+ β1(Charging Time)+ β2(Comfort in Mobility) logit (p)=0.05+ 0.03(Charging Time)+ 0.05(Comfort in Mobility ) Probability of the outcome is: P = 1/ 1+e/-­ ( 0.05+ 0.03(Charging Time)+ 0.05(Comfort in Mobility) Both predictors are statistically significant, with small but positive effects on the likelihood of the outcome. Interpretation of Results Wald values (5.071 and 4.403) are both greater than the chi-square critical value (≈3.84 at), meaning both predictors are statistically significant .Sig. values(p = 0.000) confirm strong significance. Coefficients are small (0.03 and 0.05), meaning the predictors have a modest effect on the log-odds of the outcome. Constant ensures the baseline log-odds when predictors are zero. At the behavioural level, the logistic regression analysis revealed that operational convenience and experiential satisfaction exert a measurable influence on individual decision‑making. Although the coefficients associated with charging duration and comfort in mobility were modest, their consistent positive contribution underscores the importance of designing systems that align with human expectations. When travellers perceive reduced waiting times and smoother journeys, they are more inclined to embrace alternatives that mitigate ecological harm. This finding highlights the necessity of embedding human‑centred design principles into infrastructure planning, ensuring that technological advancements resonate with everyday realities rather than remaining abstract improvements. The results are in line with the alternative hypothesis. From a technical perspective, there are researches with certain amount of solutions such as the predictive battery thermal management strategy, this work represents a significant step forward (Acker, et.al 2026). By shifting from rigid temperature set points to dynamic zones, the controller gains elasticity, enabling actuators to operate in ways that balance safety with energy efficiency. This approach not only conserves resources but also prolongs component lifespan, thereby reducing lifecycle costs. The staged methodology offline optimization through dynamic programming followed by experimental validation illustrates a rigorous pathway for bridging theoretical insights with practical implementation. Such methodological robustness ensures ongoing innovations that are not confined to laboratory settings but can be translated into real world applications. Adoption of these technologies and supporting policies can contribute positively to user experience. There are studies which are carried out in extreme cold conditions. The exploration of low‑temperature charging scenarios adds further depth to the discourse (Zhang, Z et.al 2022). Cold environments pose unique challenges, as electrochemical reactions slow and internal resistance rises, leading to inefficiencies and potential safety risks. By integrating heating strategies with charging, shows positive results under performance analysis. This study demonstrates that a comprehensive approach is essential. Rather than focusing solely on reducing charging time, engineers must consider the broader thermal and electrochemical context to maintain performance under adverse conditions. This holistic perspective provides actionable guidance for designing systems that remain reliable across diverse climates. Beyond the hardware dimension, systemic coordination emerges as a critical factor. The introduction of the Chimp Tangent Search Algorithm (ChTSA) exemplifies how hybrid computational techniques can resolve complex optimization problems inherent in charging networks (Ramy g et.al 2025). By incorporating diverse criteria such as cost, distance, priority, and user preference, the algorithm ensures equitable and efficient allocation of resources. This not only reduces congestion at charging stations but also enhances user satisfaction, reinforcing the behavioural drivers identified earlier. The synergy between deep learning models and evolutionary algorithms demonstrates the potential of artificial intelligence to orchestrate large‑scale mobility ecosystems, ensuring that technological progress translates into systemic efficiency. However, to utilise the recent researches, it is essential to start with better infrastructure and adoption of new researches. The Electric vehicles have travelled a long way over the last few decades. The early studies on the charging efficiencies should build the way forward. Conclusion This research highlights the intricate interplay between technological innovation, behavioural dynamics, and systemic coordination in advancing environmentally responsible mobility. Strength of this study lies in the fact that it weaves together empirical analysis, predictive modelling, and algorithmic optimization. The research demonstrates that sustainable transport adoption is not merely a matter of engineering progress but a holistic endeavour requiring attention to user experience, infrastructure efficiency, and intelligent system design. The broader societal implications of these findings resonate strongly with global sustainability agendas. Within the framework of SDG 11 (Sustainable Cities and Communities), the emphasis on infrastructure quality and user experience aligns with the vision of inclusive and resilient urban environments. Cities that invest in reliable charging networks and prioritize ease of movement can reduce emissions, improve accessibility, and foster healthier living conditions. This directly supports international objectives aimed at creating urban spaces that are safe, resilient, and environmentally sound. Equally important is the connection to SDG 12 (Responsible Consumption and Production). By shortening charging durations and enhancing travel comfort, individuals are nudged toward choices that reduce dependence on fossil fuels. This behavioural shift contributes to cleaner production cycles and supports the principles of a circular economy, where efficiency and user-centred design drive sustainable consumption. The research thus bridges the gap between technological innovation and consumer responsibility, showing how targeted improvements can catalyse systemic change. For policymakers and industry stakeholders, the implications are profound. Adoption of sustainable mobility is not solely a matter of technological feasibility but also of human perception and systemic coordination. Investments in advanced thermal management, intelligent scheduling algorithms, and user-friendly infrastructure will yield dividends not only in environmental terms but also in social acceptance and economic viability. Urban planners should integrate these insights into long-term strategies, ensuring that cities evolve in ways that harmonize technological progress with human needs. The study also underscores the importance of interdisciplinary collaboration. Engineers, data scientists, behavioural researchers, and policymakers must work together to design solutions that are technically robust, socially acceptable, and environmentally beneficial. The integration of predictive modelling, experimental validation, and algorithmic optimization exemplifies how diverse expertise can converge to address complex challenges. This collaborative spirit is essential for advancing sustainable mobility. Looking ahead, several avenues for future research emerge. First, the predictive thermal management strategy could be extended to incorporate real-time adaptive learning, allowing controllers to adjust dynamically based on evolving conditions. Second, the scheduling algorithms could be integrated with broader energy management systems, ensuring that charging demand aligns with renewable energy availability. Third, further exploration of user behaviour across different cultural and geographic contexts would provide deeper insights into how comfort and convenience influence adoption globally. By pursuing these directions, researchers can continue to refine the interplay between technology, behaviour, and policy. In summary, the study provides compelling evidence that targeted improvements in charging technology and infrastructure that can enhance mobility experience. This can significantly advance sustainable transport adoption. By combining statistical analysis, technical innovation, and algorithmic optimization, the research offers a holistic perspective on how to accelerate the transition toward environmentally responsible mobility systems. The research underscores that the sustainability is not achieved through isolated interventions but through integrated strategies that address both human and technical dimensions. Ultimately, the path toward sustainable mobility requires a delicate balance between efficiency, comfort, and systemic coordination. By investing in infrastructure that minimizes waiting times, designing controllers that optimize thermal conditions and deploying algorithms that manage charging demand intelligently, societies can create mobility systems that are not only environmentally sound but also socially desirable. This integrated approach will enable cities to evolve into inclusive, resilient, and sustainable communities, while fostering responsible consumption patterns that support a circular economy. This research thus contributes meaningfully to the global discourse on sustainability, offering practical insights and strategic guidance for policymakers, industry leaders, and researchers alike. By prioritizing infrastructure efficiency, user experience, and intelligent coordination, stakeholders can accelerate the adoption of sustainable mobility solutions and contribute to the realization of international sustainability goals. Declarations Declaration of interest : All data provided is primary and is in compliance with the ethical standards. The authors declare that they have no conflicts of interest. Ethics approval: This study was approved by the Institutional Review Board of Dayananda Sagar University. Written informed consent was obtained from all participants. Ethics accordance : The study was conducted in accordance with the IRB guidelines and regulations. Consent for publication : Not applicable Clinical trial number : Not applicable. Funding Declaration : No Funding received for this research Data availability : The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Consent to participate : Informed consent was obtained from all participants involved in the study. Consent to Publish : Not applicable. References Acker L, Hofmann P, Konrad J (2026) Predictive battery thermal management for fast charging of electric vehicles using nonlinear model predictive control and dynamic programming. Automot Engine Technol 11:1. https://doi.org/10.1007/s41104-025-00157-7 Anfinsen M, Lagesen VA, Ryghaug M (2018) Green and gendered? Cultural perspectives on the road towards electric vehicles in Norway. 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Hegde","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5003-715X","institution":"Dayananda Sagar University, Bengaluru, India","correspondingAuthor":true,"prefix":"","firstName":"Suneeta.","middleName":"","lastName":"Hegde","suffix":""},{"id":629308125,"identity":"cdf01352-c0cb-489d-93fd-ab62e33d9bb5","order_by":1,"name":"Prabakar S","email":"","orcid":"https://orcid.org/0000-0003-3231-746X","institution":"Dayananda Sagar University, Bengaluru, India","correspondingAuthor":false,"prefix":"","firstName":"Prabakar","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2026-04-25 04:53:48","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9522215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9522215/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107965969,"identity":"7119fd7a-f39c-4490-a4ae-5967ff1884f4","added_by":"auto","created_at":"2026-04-28 05:42:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":268010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9522215/v1/463ca420-263a-4c71-9891-f796f0b85108.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eElectric vehicle adoption reluctance, customer insights using logistic regression\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eElectric vehicles (EVs) represent one of the most promising near-term technological solutions for reducing greenhouse gas emissions and decreasing reliance on fossil fuels that are traditionally associated with conventional automobiles. Despite their numerous environmental and consumer benefits, several barriers continue to hinder widespread adoption. Among these, customer hesitation toward new technologies remains a critical challenge, often reinforcing policies that reflect scepticism about EVs.\u003c/p\u003e \u003cp\u003eCurrently, EVs are still in the early stages of development, with adoption constrained by concerns over price and driving range. Battery technology plays a pivotal role in shaping EV costs, as high-capacity batteries significantly influence both pricing structures and subsidy mechanisms within the EV ecosystem. While EVs offer cutting-edge innovations capable of mitigating climate change by lowering emissions, externalities such as knowledge spill overs and pollution reduction generate broader societal and economic benefits that are not fully captured in vehicle pricing. Governance and policy interventions along with various other initiatives by industry have tried to address these market inefficiencies, yet socioeconomic factors continue to shape adoption rates. Our study highlights the substantial impact of EV development on environmental protection, while also recognizing that public acceptance remains relatively low, presenting ongoing marketing challenges. Drawing on a questionnaire survey, this paper applies logistic regression modelling to identify key variables influencing consumer adoption of EVs. Ultimately, EVs are positioned to replace conventional vehicles, contributing to a more sustainable and climate conscious future.\u003c/p\u003e \u003cp\u003eIn alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), EV adoption is positioned as a key driver of sustainable urban mobility and responsible consumer choices. Organizations have undertaken various initiatives to address market inefficiencies, yet socioeconomic factors continue to shape adoption rates. Our study highlights the substantial impact of EV development on environmental protection, while also recognizing that public acceptance remains relatively low, presenting ongoing marketing challenges.\u003c/p\u003e \u003cp\u003eAutonomous vehicles are increasingly recognized as a cost-effective solution for sustaining urban transportation by reducing dependence on fossil fuels and lowering carbon emissions, thereby contributing to both ecological and public health benefits (Buekers,J et.al 2014). Many countries have set ambitious targets and implemented strategies to encourage the adoption of electric vehicles (EVs), with projections suggesting that EVs will constitute a significant share of the future automotive industry (Bakker, S et.al 2013). Globally, EV registrations rose dramatically from just six thousand in 2010 to over seven hundred fifty thousand in 2016, and forecasts estimate that by 2030, approximately one hundred fifty million EVs will be on the road (Cazzola et.al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEVs are widely regarded as a technological innovation capable of addressing environmental challenges linked to global warming and greenhouse gas emissions. However, their acceptance remains limited without external drivers such as stricter pollution regulations, rising fuel prices, and financial incentives \u003cem\u003e(\u003c/em\u003eGlobal EV Outlook 2013\u003cem\u003e)\u003c/em\u003e. Public subsidies and incentives are consistently identified as critical to fostering widespread adoption ( Economic inefficiencies, including uneven product distribution and issues related to emission reduction and information spill overs that further slow the diffusion of EVs (Hidrue et.al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Market imperfections distort EV pricing compared to internal combustion engine.\u003c/p\u003e \u003cp\u003eA growing body of empirical literature is emerging alongside the rapid expansion of the electric vehicle (EV) sector. While current research provides valuable insights into customer driving behaviours, social assistance programs, delivery systems, and technological advancements, many studies continue to rely on similar assumptions. Notably, most investigations into EV demand are based on surveys and decision-making experiments (Wolbertus R et.al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) Descriptive studies, however, often focus on the experiences of existing EV owners (Anfinsen et.al \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the potential benefits that could influence new buyers, and the barriers faced by individuals and organizations in adopting EVs. In contrast, there remains limited empirical work exploring the perspectives and rationales of non-owners or potential buyers. As highlighted by refs. (Hafner et.al \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), there is a shortage of descriptive methodologies that connect reflective questionnaires and preference experiments to individual consumer attitudes and reasoning. Similar critiques have been raised in EV marketing studies (Axsen et.al 2011), which argue that an overemphasis on \u0026ldquo;mechanical rationality\u0026rdquo; neglects broader aspects of consumer behaviour (Marsden, G et.al 2017). Further studies such as the present one could inform future research on consumer views regarding EV subsidies, transportation policies, and the need for more subjective investigations across diverse customer groups.\u003c/p\u003e \u003cp\u003eCarbon dioxide and other greenhouse gases are recognized as harmful atmospheric pollutants that threaten human health and welfare. Their excessive emission contributes to climate change and global warming. The combustion of fossil fuels in the transportation and electricity sectors remains the largest source of carbon emissions worldwide (Marsden, G et.al 2011)To counter this trend, governments are increasingly promoting EV adoption as a strategy to reduce emissions. While EVs offer significant ecological benefits, widespread adoption continues to face economic, infrastructural, and cultural challenges.(Fan, J.; et.al \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) Emerging transportation issues have driven advancements in automotive technologies, particularly in propulsion systems and autonomous vehicles (Todorovic, et.al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) By reducing petroleum dependence and carbon emissions, EVs are widely acknowledged as an effective pathway toward sustainable urban transportation, with positive implications for both climate stability and human well-being.\u003c/p\u003e \u003cp\u003eIn alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production\u003cb\u003e)\u003c/b\u003e, EV adoption plays a critical role in building resilient, low-carbon urban transport systems and encouraging environmentally responsible consumer choices. The transportation sector alone accounts for nearly three-quarters of its carbon dioxide emissions, representing 23% of global emissions (Wang.S. et.al 2016). Addressing these challenges requires substantial reductions in transportation-related emissions (Wang.S. et.al 2016).\u003c/p\u003e \u003cp\u003eConsequently, the transition toward renewable energy sources in transport systems has become imperative (Todorovic, et.al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By embedding EV adoption within the framework of SDG 11 and SDG 12, this study underscores how sustainable mobility and responsible consumption can jointly contribute to mitigating climate change and advancing global sustainability.\u003c/p\u003e \u003cp\u003eThe expansion of plug-in electric vehicle (PEV) charging infrastructure, both at home and in public spaces, carries significant societal implications. Widespread accessibility of charging stations is expected to raise public awareness of PEV technology and improve perceptions of its functionality, potentially fostering progressive \u0026ldquo;green cultural branding\u0026rdquo; (Community Energy Association \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Home charging remains a critical factor for consumers considering the purchase of a PEV, as drivers often face limited power supply after completing trips (Bakker, J.et.al). This is a problem for those constrained by vehicle range, knowledge of public charging options can encourage longer travel distances powered by electricity.\u003c/p\u003e \u003cp\u003eNevertheless, prior studies have limitations, as they often rely on small samples of existing PEV owners and drivers, without adequately addressing the perspectives or acceptance levels of non-owners. Projections suggest that increasing charger availability from 10% to 33% could boost EV demand by up to 50%. Moreover, simulations based on respondent data indicate that a tenfold increase in charger accessibility could nearly double demand, raising estimated market share from 2.2% to 8.9%. Research further shows that greater recharge availability enhances consumers\u0026rsquo; willingness to pay for new vehicles, reduces time spent locating charging infrastructure, improves individual utility, and increases the likelihood of purchasing a PEV.\u003c/p\u003e \u003cp\u003eNumerous studies have identified consumer-related factors that influence the likelihood of acquiring an electric vehicle (EV). Common indicators include education, income, household vehicle ownership, environmental attitudes, and affinity for technology. However, the literature remains divided on which of these characteristics are most critical. Several studies suggest that higher levels of education are associated with a greater probability of being \u0026ldquo;EV-oriented\u0026rdquo;, while others report that neither education nor economic status significantly affects EV adoption rates across nations. Studies so far found no evidence that increased income alters the likelihood of being \u0026ldquo;EV-oriented.\u0026rdquo; Household vehicle ownership also plays a role: living in a multi-car family has been shown to reduce the probability of EV orientation. Conversely, actively buying or planning to buy a vehicle is a strong predictor of interest in EV ownership. Access to charging infrastructure is equally important; the absence of a dedicated charging outlet may hinder adoption, particularly among residents of apartments or shared housing units. Participants in EV driving trials valued at-home charging for the autonomy it provided (Graham-Rowe, et.al). Evidence regarding the influence of environmental preferences on EV purchase decisions remains mixed, leaving uncertainty about which consumer identifiers best predict long-term EV acceptance. (Graham-Rowe, et.al).\u003c/p\u003e \u003cp\u003eResearch into EV adoption consistently highlights driving range as the most significant non-financial barrier. It is reported that over 70% of respondents considered limited driving range a \u0026ldquo;significant disadvantage\u0026rdquo; or \u0026ldquo;somewhat of a disadvantage.\u0026rdquo; Since these surveys were conducted in urban areas, the findings may underestimate concerns in suburban or rural contexts, where longer travel distances are common. The Minneapolis EV cost payback model, focusing on trip chaining and range, suggests that plug-in hybrid electric vehicles (PHEVs) may be more appealing than battery electric vehicles (BEVs). However, Monte Carlo simulations of alternative fuel vehicles (AFVs) indicate that BEVs are more likely to be chosen than PHEVs. Researchers argue that expanding charging infrastructure is a more effective strategy to alleviate range anxiety than simply extending vehicle range. This underscores the importance of charging time. When fast charging is unavailable, consumers place greater value on extended driving range, highlighting the interdependence of range and charging duration. Further it is noted that a one-hour charging time discourages long-distance travel, whereas reducing charging time to ten minutes would allow BEVs to compete with internal combustion engine vehicles in terms of travel efficiency. Further research is needed to explore the relationship between driving range, charging time, and charging network development. Survey-based utility models confirm that EV competitiveness improves when adequate infrastructure is available, with overnight home charging emerging as a significant factor. Studies similarly found strong consumer preference for at-home charging, citing convenience, safety, and security of both the vehicle and charging equipment.\u003c/p\u003e \u003cp\u003eTechnological advancements have also introduced innovative powertrain systems. The new electric\u0026ndash;hydraulic powertrain integrates a traction motor, battery pack, hydraulic pump/motor (secondary component), hydraulic accumulator, reservoir, and hydraulic valves. The hydraulic circuit comprises two subsystems: the drive circuit and the drain circuit. In the drive circuit, a cartridge valve, one-way valve, and two-position four-way valve regulate fluid flow. In case of Electric vehicles (EVs) the battery storage system is both the most critical and the most costly one. To ensure safety and reliability, an accurate battery management system (BMS) is essential for monitoring and controlling battery states. Based on these measurements, the residual useful life (RUL) of the battery can be estimated, helping to prevent potential safety hazards that could damage the battery, compromise the vehicle, or endanger passengers. Key parameters such as battery voltage, current, and temperature over time must be measured to assess battery health and predict RUL. These measurements are typically collected through embedded systems, with the resulting data stored for analysis. Various machine learning techniques including artificial neural networks (ANNs), long short-term memory (LSTM) models, support vector regressors (SVRs), random forest (RF), and boosting methods have been employed to estimate battery RUL. Additionally, optimization algorithms such as particle swarm optimization (PSO) and whale optimization algorithm (WOA) have been integrated with the extreme learning machine (ELM) to enhance prediction accuracy. The root mean square error (RMSE) serves as the primary metric for evaluating algorithm performance. Results indicate that PSO-ELM and WOA-ELM outperform other machine learning approaches, achieving RMSE values of 1.46% and 1.51%, respectively. In comparison, LightGBM, random forest, AdaBoost, XGBoost, and CatBoost yielded higher RMSE values of 2.24%, 2.25%, 2.74%, 2.84%, and 3.56%, respectively (Hussien et al., 2023).Studies in past have conducted on predictive battery thermal management strategy that were developed and validated using a single state-of-the-art BEV platform (Acker, et.al \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Although the methodology is rigorous, the reliance on one vehicle architecture limits generalizability. Variations in battery chemistry, cooling system design, and actuator configurations across manufacturers may influence the effectiveness of the proposed controller. The analysis of heating strategies under cold conditions (Zhang, Z et.al 2022) was based on controlled simulations and laboratory experiments. Real-world environments introduce additional uncertainties such as fluctuating ambient temperatures, user behaviour, and grid constraints. These factors may affect the accuracy of the performance predictions and the applicability of the results to diverse climates. The Chimp Tangent Search Algorithm (ChTSA) model (Ramy g et.al 2025) was tested under simulated EV network conditions. While the algorithm integrates multiple objectives such as cost, distance, and user preference, the simulation may not fully replicate the complexity of real urban charging ecosystems. Variables such as unpredictable traffic patterns, sudden surges in demand, or infrastructure failures were not incorporated, which could affect scheduling efficiency in practice.\u003c/p\u003e \u003cp\u003eThe behavioural analysis emphasized charging duration and comfort in mobility as predictors of sustainable transport adoption. Other potentially influential factors such as cost of charging, availability of renewable energy, policy incentives and social norms were not included in the regression model. This narrowed scope may understate the multifactorial nature of adoption decisions\u003c/p\u003e \u003cp\u003eElectric vehicles (EVs) offer significant environmental advantages, and many countries are actively working to integrate them into daily life. Recent improvements in battery capacity and charging times have addressed some of the technological challenges, yet the development of widespread charging infrastructure remains essential. Broadly distributed charging facilities, supported by modern facility management systems, can monitor charger status in real time, resolve technical issues, and optimize operational strategies. Digital controllers further enable the creation of smart, city-wide transportation networks. Greater awareness of public charging facilities enhances driver confidence, encouraging longer travel distances powered by electricity and alleviating concerns about limited range.\u003c/p\u003e \u003cp\u003eEVs play a pivotal role in the transition to a low-carbon energy system. By shifting charging demands over time, they help balance the intermittent nature of renewable energy sources, making digitalization of mobility increasingly vital. EVs can also connect directly to the grid, supporting the stability of renewable energy dominated systems. This integration reduces emissions from the transportation sector, which remains a major contributor to global CO₂ output. Smart buildings are now being designed to incorporate EV technology, improving energy efficiency, sustainability, and long-term reductions in carbon emissions. By leveraging renewable energy sources, these buildings further minimize environmental impact. EV ratings are typically based on product quality, perceived performance, and reliability. Additional factors such as societal impact, government involvement, and infrastructure development also play a role. Subcategories like \u0026ldquo;perceived affordability\u0026rdquo; and \u0026ldquo;accessibility\u0026rdquo; are particularly influential in shaping public acceptance and adoption of EVs.\u003c/p\u003e \u003cp\u003eIn alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), EV adoption supports cleaner, smarter, and more resilient urban transport systems while encouraging responsible consumer choices. To strengthen this connection, our study employs logical regression model to identify the variables such as charging time and comfort in mobility that directly influence consumer behaviour. These methodological insights can be mapped to SDG indicators,.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Objective\u003c/strong\u003e \u003cp\u003eThe main aim of this study is to find the reasons that affect the buying behaviour of EV consumers, and understanding the barriers in adopting EVs.The additional aim of the study is to identify the comfort and use of Electric vehicle under 24 * 7 economy and intense business competition. We propose the following hypothesis to be tested..\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eThere is a relationship between EV purchases to factors like charging time\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eThere is a relationship between EV purchases to factors like comfort in mobility\u003c/p\u003e \u003c/p\u003e"},{"header":"Research methodology","content":"\u003cp\u003eThe existing literature clearly demonstrates that several factors influence consumer perceptions of electric vehicle (EV) adoption in developed markets. This makes it particularly important to examine consumer behaviour in one of the world\u0026rsquo;s most populous countries India. Building on prior research and addressing this gap, the present study seeks to identify the key factors shaping consumer satisfaction with EV usage and to evaluate how these dimensions affect consumer recommendations, thereby influencing potential new buyers in India\u0026rsquo;s rapidly growing EV market. The study is conducted using a convenience sample.\u003c/p\u003e \u003cp\u003eTwo critical factors have been identified and categorized as Charging Time (CT), and Comfort mobility (CM). Data for this study were collected using a standardized questionnaire administered through Google form with participants across Bengaluru, Karnataka area. Respondents represented diverse demographics, including men and women of varying ages, income levels, and occupational backgrounds such as business, service, and other professions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each parameter was measured on open ended,\u0026rdquo; yes \u0026lsquo; and No,\u0026rsquo;( Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) questionnaire. Logistic regression tests were employed to assess whether significant correlations exist between consumer satisfaction and demographic variables.\u003c/p\u003e \u003cp\u003eIn alignment with the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), this study emphasizes how consumer satisfaction and recommendations can accelerate EV adoption in India. By linking consumer behaviour to sustainable urban mobility and responsible consumption patterns the findings provide actionable insights for policymakers and industry stakeholders. This integration highlights the dual importance of infrastructure development and consumer awareness in fostering a sustainable EV ecosystem.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Demography of the survey respondents\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Gender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational qualification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly income(INR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u003c/strong\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e36%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e57%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Below graduate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eService industry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e27%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;15K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e35%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u003c/strong\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e24%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e43%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnder Graduate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e49%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntrepreneur\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e24%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;25 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e18%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u003c/strong\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost graduate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e24%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudents\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e48%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e21%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u003c/strong\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e20%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;35 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;40 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;45 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; 3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e* Primary data set/ own source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table2: Survey results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Charging Time\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e106\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Comfort in Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e104\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e* YES / NO\u003c/strong\u003e → counts of customers recommending (YES) or not recommending (NO).\u003cstrong\u003ePredictors\u003c/strong\u003e → Charging Time and Comfort in Mobility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Results of logistic regression Using SPSS 26\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharging Time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComfort in mobility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.227\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSince the significant value of the factor Charging Time is less than 0.05 and higher than the Wald’s value, it can be inferred that there is significant effect of the factor Charging Time on the EV purchase decisions. The significant value of the comfort in mobility is less than 0.5 it is inferred that there is significant effect of the factor comfort in mobility on the Ev purchase decisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression Equation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom table 3:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharging Time\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eCoefficient (B) = 0.03\u003c/p\u003e\n\u003cp\u003eStandard Error (SE) = 0.331\u003c/p\u003e\n\u003cp\u003eWald = 5.071\u003c/p\u003e\n\u003cp\u003eSig. (p-value) = 0.000\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComfort in Mobility\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eCoefficient (B) = 0.05\u003c/p\u003e\n\u003cp\u003eSE = 0.315\u003c/p\u003e\n\u003cp\u003eWald = 4.403\u003c/p\u003e\n\u003cp\u003eSig. = 0.000\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Constant (Intercept)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Coefficient (B) ≈ \u0026lt; 0.05 (likely 0.05 or less)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;SE = 0.227\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Wald = 3.84\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Sig. = 0.000\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elogit model\u003c/strong\u003e is:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elogit (p)=β0+ β1(Charging Time)+ β2(Comfort \u0026nbsp;in \u0026nbsp;Mobility)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elogit (p)=0.05+ 0.03(Charging Time)+ 0.05(Comfort in Mobility\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eProbability of the outcome is:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP = 1/ 1+e/-­ (\u003c/strong\u003e\u003cstrong\u003e0.05+ 0.03(Charging Time)+ 0.05(Comfort in Mobility)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth predictors are statistically significant, with small but positive effects on the likelihood of the outcome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWald values (5.071 and 4.403) are both greater than the chi-square critical value (≈3.84 at), meaning both predictors are statistically significant .Sig. values(p = 0.000) confirm strong significance. Coefficients are small (0.03 and 0.05), meaning the predictors have a modest effect on the log-odds of the outcome. Constant ensures the baseline log-odds when predictors are zero.\u003c/p\u003e\n\u003cp\u003eAt the behavioural level, the logistic regression analysis revealed that operational convenience and experiential satisfaction exert a measurable influence on individual decision‑making. Although the coefficients associated with charging duration and comfort in mobility were modest, their consistent positive contribution underscores the importance of designing systems that align with human expectations. When travellers perceive reduced waiting times and smoother journeys, they are more inclined to embrace alternatives that mitigate ecological harm. This finding highlights the necessity of embedding human‑centred design principles into infrastructure planning, ensuring that technological advancements resonate with everyday realities rather than remaining abstract improvements. \u0026nbsp;The results are in line with the alternative hypothesis.\u003c/p\u003e\n\u003cp\u003eFrom a technical perspective, there are researches with certain amount of solutions such as the predictive battery thermal management strategy, this work represents a significant step forward (Acker, et.al 2026). By shifting from rigid temperature set points to dynamic zones, the controller gains elasticity, enabling actuators to operate in ways that balance safety with energy efficiency. This approach not only conserves resources but also prolongs component lifespan, thereby reducing lifecycle costs. The staged methodology offline optimization through dynamic programming followed by experimental validation illustrates a rigorous pathway for bridging theoretical insights with practical implementation. Such methodological robustness ensures ongoing innovations that are not confined to laboratory settings but can be translated into real world applications. Adoption of these technologies and supporting policies can contribute positively to user experience. There are studies which are carried out in extreme cold conditions. The exploration of low‑temperature charging scenarios adds further depth to the discourse (Zhang, Z et.al 2022). Cold environments pose unique challenges, as electrochemical reactions slow and internal resistance rises, leading to inefficiencies and potential safety risks. By integrating heating strategies with charging, shows positive results under performance analysis. This study demonstrates that a comprehensive approach is essential. Rather than focusing solely on reducing charging time, engineers must consider the broader thermal and electrochemical context to maintain performance under adverse conditions. This holistic perspective provides actionable guidance for designing systems that remain reliable across diverse climates.\u003c/p\u003e\n\u003cp\u003eBeyond the hardware dimension, systemic coordination emerges as a critical factor. The introduction of the Chimp Tangent Search Algorithm (ChTSA) exemplifies how hybrid computational techniques can resolve complex optimization problems inherent in charging networks (Ramy g et.al 2025). By incorporating diverse criteria such as cost, distance, priority, and user preference, the algorithm ensures equitable and efficient allocation of resources. This not only reduces congestion at charging stations but also enhances user satisfaction, reinforcing the behavioural drivers identified earlier. The synergy between deep learning models and evolutionary algorithms demonstrates the potential of artificial intelligence to orchestrate large‑scale mobility ecosystems, ensuring that technological progress translates into systemic efficiency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, to utilise the recent researches, it is essential to start with better infrastructure and adoption of new researches. The Electric vehicles have travelled a long way over the last few decades. The early studies on the charging efficiencies should build the way forward.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research highlights the intricate interplay between technological innovation, behavioural dynamics, and systemic coordination in advancing environmentally responsible mobility. Strength of this study lies in the fact that it weaves together empirical analysis, predictive modelling, and algorithmic optimization. The research demonstrates that sustainable transport adoption is not merely a matter of engineering progress but a holistic endeavour requiring attention to user experience, infrastructure efficiency, and intelligent system design.\u003c/p\u003e \u003cp\u003eThe broader societal implications of these findings resonate strongly with global sustainability agendas. Within the framework of SDG 11 (Sustainable Cities and Communities), the emphasis on infrastructure quality and user experience aligns with the vision of inclusive and resilient urban environments. Cities that invest in reliable charging networks and prioritize ease of movement can reduce emissions, improve accessibility, and foster healthier living conditions. This directly supports international objectives aimed at creating urban spaces that are safe, resilient, and environmentally sound.\u003c/p\u003e \u003cp\u003eEqually important is the connection to SDG 12 (Responsible Consumption and Production). By shortening charging durations and enhancing travel comfort, individuals are nudged toward choices that reduce dependence on fossil fuels. This behavioural shift contributes to cleaner production cycles and supports the principles of a circular economy, where efficiency and user-centred design drive sustainable consumption. The research thus bridges the gap between technological innovation and consumer responsibility, showing how targeted improvements can catalyse systemic change.\u003c/p\u003e \u003cp\u003eFor policymakers and industry stakeholders, the implications are profound. Adoption of sustainable mobility is not solely a matter of technological feasibility but also of human perception and systemic coordination. Investments in advanced thermal management, intelligent scheduling algorithms, and user-friendly infrastructure will yield dividends not only in environmental terms but also in social acceptance and economic viability. Urban planners should integrate these insights into long-term strategies, ensuring that cities evolve in ways that harmonize technological progress with human needs.\u003c/p\u003e \u003cp\u003eThe study also underscores the importance of interdisciplinary collaboration. Engineers, data scientists, behavioural researchers, and policymakers must work together to design solutions that are technically robust, socially acceptable, and environmentally beneficial. The integration of predictive modelling, experimental validation, and algorithmic optimization exemplifies how diverse expertise can converge to address complex challenges. This collaborative spirit is essential for advancing sustainable mobility. Looking ahead, several avenues for future research emerge. First, the predictive thermal management strategy could be extended to incorporate real-time adaptive learning, allowing controllers to adjust dynamically based on evolving conditions. Second, the scheduling algorithms could be integrated with broader energy management systems, ensuring that charging demand aligns with renewable energy availability. Third, further exploration of user behaviour across different cultural and geographic contexts would provide deeper insights into how comfort and convenience influence adoption globally. By pursuing these directions, researchers can continue to refine the interplay between technology, behaviour, and policy.\u003c/p\u003e \u003cp\u003eIn summary, the study provides compelling evidence that targeted improvements in charging technology and infrastructure that can enhance mobility experience. This can significantly advance sustainable transport adoption. By combining statistical analysis, technical innovation, and algorithmic optimization, the research offers a holistic perspective on how to accelerate the transition toward environmentally responsible mobility systems. The research underscores that the sustainability is not achieved through isolated interventions but through integrated strategies that address both human and technical dimensions.\u003c/p\u003e \u003cp\u003eUltimately, the path toward sustainable mobility requires a delicate balance between efficiency, comfort, and systemic coordination. By investing in infrastructure that minimizes waiting times, designing controllers that optimize thermal conditions and deploying algorithms that manage charging demand intelligently, societies can create mobility systems that are not only environmentally sound but also socially desirable. This integrated approach will enable cities to evolve into inclusive, resilient, and sustainable communities, while fostering responsible consumption patterns that support a circular economy.\u003c/p\u003e \u003cp\u003eThis research thus contributes meaningfully to the global discourse on sustainability, offering practical insights and strategic guidance for policymakers, industry leaders, and researchers alike. By prioritizing infrastructure efficiency, user experience, and intelligent coordination, stakeholders can accelerate the adoption of sustainable mobility solutions and contribute to the realization of international sustainability goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eDeclaration of interest\u003c/em\u003e: All data provided is primary and is in compliance with the ethical standards. The authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval:\u003c/em\u003e This study was approved by the Institutional Review Board of Dayananda Sagar University. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;Ethics accordance\u003c/em\u003e: \u0026nbsp;The study was conducted in accordance with the IRB guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding Declaration\u003c/em\u003e: No Funding received for this research\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e: The datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u003cem\u003eConsent to participate\u003c/em\u003e: Informed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to Publish\u003c/em\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcker L, Hofmann P, Konrad J (2026) Predictive battery thermal management for fast charging of electric vehicles using nonlinear model predictive control and dynamic programming. 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Transp Res Part D Transp Environ 62:283\u0026ndash;297 [CrossRef]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Min H, Yu Y, Cao Q, Li M, Yan K An optimal thermal management system heating control strategy for electric vehicles under low-temperature fast chargingconditions.Appl.Therm.Eng. 207,118123 (2022) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.applthermaleng.2022.118123\u003c/span\u003e\u003cspan address=\"10.1016/j.applthermaleng.2022.118123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Not applicable","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electric Vehicle batteries, infrastructure, SDG 11, SDG 12, policies","lastPublishedDoi":"10.21203/rs.3.rs-9522215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9522215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research examines ecological awareness and acceptance that shape the usage of Electric Vehicles (EVs). Study highlights the transition from \u0026ldquo;traditionally perceived usefulness\u0026rdquo; to \u0026lsquo;\u0026rsquo;customer perceived usefulness\u0026rdquo; driven by environmental benefits. This study emphasizes the role of batteries in EV adoption. Although there are studies in past around problems with charging and other factors, this study empirically measures the impact of EV charging infrastructure on adoption of electric vehicles and their role in fostering cleaner urban mobility and promoting responsible consumption patterns. This study utilises logistic regression method and considers the factors such as charging time and comfort in mobility as variables. Study underscores the importance of charging time and comfort as important factors in shaping consumer behaviour through prospective consumer survey. The study iterates the role of policies in providing economic incentives for promotion of greener mobility; this study provides avenues for Asian nations to transform the infrastructure challenges into strategic business opportunities for sustainable environment.\u003c/p\u003e","manuscriptTitle":"Electric vehicle adoption reluctance, customer insights using logistic regression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 05:40:26","doi":"10.21203/rs.3.rs-9522215/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3a36db4-37b1-4381-a650-3bab682572c6","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T05:40:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 05:40:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9522215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9522215","identity":"rs-9522215","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}