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This study aims to explore and segment consumer preferences for electric and hybrid vehicles in Germany, Sweden, the Netherlands and Turkey, focusing on understanding the various factors that influence consumer decisions in these markets.

Using latent class analysis (LCA) on data collected through online surveys and discrete choice experiments, this research categorizes consumers into distinct segments. The approach allows for a nuanced understanding of how various factors such as income level, fuel cost, age, CO₂ emissions, purchase price, vehicle range, policy policies and environmental concerns interact with shape consumer preferences.

The analysis uncovers significant heterogeneity in consumer preferences for electric and hybrid vehicles across Germany, Sweden, the Netherlands and Turkey, revealing four key segments: “Eco-Driven Innovators,” “Value-Focused Pragmatists,” “Tech-Savvy Early Adopters” and “Reluctant Traditionalists.” “Eco-Driven Innovators” prioritize environmental benefits and are less sensitive to price, demonstrating a strong inclination toward vehicle CO₂ emissions and policy policies. “Value-Focused Pragmatists” weigh economic factors heavily, showing a sharp interest in fuel costs and purchase prices but are open to considering electric and hybrid vehicles if they present clear long-term savings. Technology-savvy early adopters are attracted by the latest technological advancements in vehicles, regardless of the type, and are motivated by factors beyond just environmental concerns or cost savings. Lastly, “Reluctant Traditionalists” exhibit minimal interest in electric and hybrid vehicles due to concerns over charging infrastructure and upfront costs. This detailed segmentation illustrates the diverse motivations and barriers influencing consumer choices, from governmental policies and environmental concerns to individual financial considerations and technological appeal.

This study stands out for its pioneering application of LCA to dissect the complexity of consumer preferences for electric and hybrid vehicles, a methodological approach not widely used in this research domain. Using LCA, the authors are able to uncover nuanced consumer segments, each with distinct preferences and motivations, providing a depth of insight into market dynamics that traditional analysis methods may overlook. This approach enables a more granular understanding of how diverse factors – ranging from environmental concerns to economic considerations and technological attributes – interact to shape consumer choices in different countries. The findings not only fill a critical gap in the existing literature by mapping the intricate landscape of consumer preferences, but also offer a novel perspective on strategizing market interventions. Therefore, the application of LCA enriches the discourse on sustainable transportation, offering stakeholders, manufacturers, policymakers and researchers – a refined toolkit for navigating the evolving market dynamics and fostering the adoption of electric and hybrid vehicles.

The current work aims to understand the consumers’ adoption of electric vehicles (two-wheelers) from their value perspective by utilizing the value-based adoption model.

The study considered data from 302 potential electric two-wheeler customers and tested the hypotheses using structural equation modeling.

The outcomes showed that perceived economic benefits, social image, enjoyable acceleration and enhanced fun and perceived environment (positively) and perceived physical safety risk, perceived cost of ownership and range and charging risk (negatively) influenced the customers’ perceived value linked with electric two-wheeler (ETW) adoption. Only low engine noise emission and infrastructure issues did not affect perceived value.

Most of the respondents considered in the study were less than 35 years old. Hence, the model can be tested for other age groups.

The study’s outcomes will help ETW marketers, manufacturing companies and governments (state and central) to provide a more convenient environment for electric two-wheelers' adoption and help them curate appropriate strategies.

The current work offers a better understanding of potential customers' ETW adoption by employing a value-based trade-off.

The study assesses the most significant architectural core technological system that converges into a Battery Electric Vehicle (BEV).

Conceptually grounded in the convergence phenomenon and utilizing the graph theory-based network construction approach, based on the Betweenness Centrality (BC) metric, core International Patent Classifications (IPCs) have been empirically identified. Based on these IPCs, the ownership structure of the patents was established through assignee analysis.

Analyzing the networks obtained at different IPC levels, we found that multiple technologies have converged in a BEV, from battery chemistry to electrical engineering and thermal management of electrical machines.

The outcome of this work has led to the identification of BEV technologies, which can be further developed to assess the trends of technologies and associated gaps and aid technology management for the selection, acquisition, and exploitation of technology.

The outcome of this work will aid technology management practitioners in better planning the selection, acquisition, and exploitation of technologies associated with BEV.

The paper adds an evidence-based approach to the body of knowledge to identify the built-in technologies that produce a BEV.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

This paper aims to conduct an experimental and theoretical investigation into fuel pre-delivery effects for a heavy fuel crankcase scavenged spark ignition two-stroke cycle engine for unmanned aerial vehicle application.

One-dimensional computational fluid dynamic modelling of the engine system using WAVE software supported by experimental dynamometer testing of the subject engine with kerosene JET A-1 and gasoline and fuels.

The experimental research has shown performance improvements using fuel preheating via use of auxiliary transfer port fuel injection. Computational simulation has allowed comparisons with auxiliary transfer port injection and direct in-cylinder injection to be made.

While some heavy fuel engines are now available for unmanned aerial vehicles the best solution to meet the military equipment single fuel policy remains an area of evolving research. The findings within this study show possibilities for fuel pre-treatment.

One-dimensional computational fluid dynamic modelling of the engine system using WAVE software supported by experimental dynamometer testing of the subject engine with kerosene JET A-1 and gasoline fuels.

To address the issues of the insufficient output torque associated with the application of intensifying-flux permanent magnet (PM) machines in electric vehicles, this paper aims to propose an intensifying-flux hybrid excitation PM machine. It is possible to adjust the air gap magnetic field by adjusting the field current in the excitation winding, thereby increasing the torque output capability and speed range of the machine.

First, a novel intensifying-flux hybrid excited permanent magnet synchronous machine (IF-HEPMSM) is proposed on the basis of intensifying-flux permanent magnet synchronous machine (IF-PMSM) and an equivalent magnetic circuit model is established. Second, the tooth width and yoke thickness of the machine stator are optimized to ensure the overload capacity of the machine while effectively improving the wide flux regulation range. Furthermore, the electromagnetic characteristics of the IF-HEPMSM are investigated and compared with the IF-PMSM and conventional permanent magnet synchronous machine (PMSM) by using finite element simulations.

The i_d of IF-HEPMSM and IF-PMSM is greater than zero low-speed magnetizing current. And the flux-weakening current of the IF-HEPMSM is 18% and 3% smaller than of the conventional PMSM and IF-PMSM.

Aiming at the problems of IF-PMSM applied to electric vehicles, this paper proposes an IF-HEPMSM. The air gap magnetic field is adjusted by controlling the current of the excitation winding to improve the reliability of the machine. Therefore, the IF-HEPMSM combines the advantages of high-power density and high efficiency of the PMSM and the controllable magnetic field of the electro-excitation machine, which is of great engineering value when applied in the field of electric vehicles.

The proposed IF-HEPMSM offers better flux regulation capability with electromagnetic characteristics analysis and maps of dq-axis current as compared to IF-PMSM and conventional PMSM. Moreover, the improvement of the torque can make up for the shortcomings of the insufficient torque output capability of the IF-PMSM.

The purpose of this study is to create sustainable additives for future vehicles, characterized by low levels of sulfated ash, sulfur and phosphorus (SAPS) or even SAPS-free alternatives. These newly developed additives must not only match or outperform the current commercial benchmarks in terms of tribological performance, but also align with the emerging sustainability trends. It is anticipated that this innovative technology will yield promising outcomes in the realm of hybrid and electric vehicles.

This research primarily focused on chemical synthesis, performance evaluation and characterizations. These aspects were studied through collaboration between Syensqo, Southwest Research Institute (the USA) and the Lab of the Future in France. The data was generated and analyzed by a team of research scientists, internship students and technical specialists.

Two types of additives have been specifically designed and synthesized in accordance with sustainable requirements. Both technologies have exhibited exceptional frictional and wear-resistant properties. Moreover, the leading candidates exhibit a lower rate of copper corrosion, stable electric conductivity and outstanding thermal stability when compared to commercial benchmarks. This study is expected to open a new research avenue for developing next-generation additives for lubricants, with wide potential applications including hybrid electric vehicle and electric vehicle markets.

In the current lubricant market, there is a lack of effective low-SAPS or SAPS-free additives. This research aims to address this gap by designing sustainable additives for next-generation vehicles that not only meet specific requirements but also maintain optimal lubrication performance.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0033/

The study aims to fulfil two objectives, namely, to explore what dimensions comes into play in the current consumer EV purchase behaviour based on relevant literature identified in the past and to find out whether there are other reasons that are peculiar to the Indian automobile market which influenced consumers to adopt this eco-friendly technology.

Mixed method approach for collecting the primary data was used. The study was conducted in five cities across India, through a questionnaire administered to 266 existing EV owners using judgemental sampling, that included dimensions as to what made them purchase EVs (any variant whether hybrid or battery) and encouraged their green behaviour. Moreover, the survey also took care of gathering other motivational constructs that may have played a significant role in the Indian EV market. To take care of this, use of single open-ended question was done. Statistical techniques like exploratory factor analysis (EFA) and Partial Least Square-Structural Equation Modelling (PLS-SEM) version 4.0 were performed to gain meaningful insights from this investigation.

Based on our analysis, EV purchase behaviour in the Indian market could be explained by six configurational paths identified in conceptual framework of this study. The data analysed revealed long-term operational benefits or relative advantages, trialability or prior driving experience and positive attitude towards EVs playing an important role towards EV purchase decision followed by environmental concern, social norms and personal values of Indian consumers. Besides, several other factors were identified in open comments like favourable government policies, brand consciousness, compatibility and functional benefits in terms of comfortable and noiseless driving which tend to drive the decision of Indian consumers towards buying EVs. The identification of these motivations can help policymakers in targeting the customer market accordingly.

Although the study is restricted by its scope of sampling criteria, yet is distinct in its methodological approach and thereby adds value to existing literature on marketing and diffusion of EVs. The study explores the untapped gap in the current literature by helping identify reasons on the basis of which consumers made their choices of different EVs. Instead of analysing the potential antecedents of EV uptake, the study brings to light the actual reasons of EV purchase behaviour in the context of an emerging economy like India. That is to say, the horizon of EV motivations spans across technological, individual and regulatory attributes in the Indian automobile market.

This study is relevant and fulfils the underlying gap in the existing literature related to actual motivations that consumers look for before buying a high involvement product such as EVs. The results can be beneficial for marketers to design effective marketing strategies from the perspective of consumers. Promotional strategies addressing ease of using EVs and how battery technology is better than before needs to be clearly communicated by the marketers. Consumer education is necessary, in this regard, to make people aware of the rising air pollution levels. They have to be made understood the real advantages of owning EVs and how these non-conventional vehicles are far better when compared to traditional cars. In a nutshell, the study underscored potential determinants or reasons that made Indian consumers to go for EVs, based on statistically significant evidences which can be crucial for stakeholders operating in the Indian EV industry.

The study has important implications for members of the community. By sacrificing their egoistic tendencies or individual preferences for energy intensive products such as ICE based vehicles, people can imbibe altruistic orientation and can contribute to the collective benefit of all by shifting towards energy rich vehicles powered by electricity. This is necessary to achieve the broader goals of sustainable development and to curtail the rising climate change phenomenon.

The study applied both quantitative (deductive) and qualitative (inductive) approach to study into the reasons for consumers’ purchase motivations towards electric cars. Taking a distinct methodological approach, the study enhances the knowledge on EVs by asking consumers to comment in their own words as to why they purchased EVs. This was necessary so as to better explore the needs of the Indian automobile market and which factors govern the purchase of EVs in domestic market. Past literature majorly focused on purchase intention towards EVs but this study in unexampled and analysed the purchase behaviour towards EVs. It should be noted that our investigation reveals the complexity of EV purchase reasons than those identified in past literature and it adds to the current pool of literature on EVs by explaining factors with unrivalled illustrative power.

Although the impact of carbon emissions regulations is evident to upstream automakers, their influence on downstream B2C car-sharing platforms remains unclear. This article reveals the influence of carbon emission regulations on the performance of supply chain members. In particular, we focused on the decision of B2C car-sharing platforms.

We develop a three-stage dynamic game model consisting of an automaker, a B2C car-sharing platform and consumers.

The carbon emission cap has a critical threshold. Above this threshold, the regulation is ineffective for the platform’s operating model. Below it, the regulation affects the platform, moderated by customers' green awareness. The threshold initially decreases (weakly) and then increases in awareness. Effective caps reduce profits for the manufacturer, B2C car-sharing platform and supply chain, while ineffective caps see higher profits with increased awareness.

Firstly, this paper explores the impact of carbon emission caps on the operational strategies of B2C car-sharing platforms within the sharing economy, complementing existing research. Secondly, it identifies conditions where stricter caps prompt B2C car-sharing platforms to adjust their operational models and offers fresh insights for managers and departments responsible for carbon emission policy formulation. Thirdly, the study uncovers how carbon emission caps affect the performance of supply chain members, providing crucial managerial insights for sustainable operations.