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Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

The electric vehicle has been viewed as a technological solution to the dual plagues of dwindling fossil fuel supplies and pollutant emissions from gasoline powered vehicles. Futurists see a world where most personal transportation is electrically powered with energy supplied by tomorrow's power plants. In that future world, automobile power sources — representing millions of uncontrollable sources of pollution and energy waste — are consolidated into fewer, manageable, generators in fixed locations. With fixed and relatively few sources of pollution, resources can be better focused to provide clean, inexpensive energy for transportation. Many people share this vision of the future but few have been able to see how it can be brought into existence. Initial attempts have focused on legislation to stimulate the development of this market. As with any new technology, the electric vehicle field has developed its own terminology. For purposes of clarity throughout mis paper please bear in mind the following definitions.

The paper highlights the process of electric vehicles optimal design as an inverse problem and presents the global constrained optimization as the best way to solve it.

The electric vehicle optimal design is carried out by a new approach. It consists an electric vehicle design model managed by constrained optimization techniques. It includes sizing models for all drive train components and a vehicle dynamic model build in a new “design way” as an energy‐based model using the response surface methodology. The sensitivity of first simple sizing models can be evaluated by the experimental design method, giving information about the most important part of the model that must be improved.

The result shows the superiority of the constrained optimization technique that treats simultaneously the global optimization and the model adjustment. This method of simultaneous resolution is much more powerful than the successive resolution of each subproblem. The proposed “design approach” used for electric vehicle optimal design offer a large potential in the field of the complex systems design.

The electric vehicle design process is treated on a vehicle design model based on a design approach. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all components interactions. Furthermore, considering fine components sizing models, the components can be sized taking into account the whole system behavior in an optimal global design.

The aim of this study is to identify and describe the factors associated with Nissan Company's electric vehicle (EV) development. In addition, Nissan's different commercialization strategies toward EV and HEV development will be discussed.

This study uses a descriptive case study approach to provide a deep understanding of successful or failed projects of Nissan. In this term, the company's green car development between 1996 and 2012 will be analyzed. Based on the market presence, Nissan's electric vehicle production trend is divided into two different generations with different characteristics. The gap between these two generations has a structural effect on the current state of Nissan's EV development.

One key factor behind Nissan's success and lead in the current electric vehicle market is the long-term experience with specific type of market structure, which has nurtured the company with a strong green vehicle development capability. The study shows that the electric vehicles market gap acted as a catalyst for later Nissan's successful cases. Also, the authors demonstrate how the dramatic shifts in Nissan strategies helped the company to revitalize its leadership as an electric car guru.

This study provides a better insight into the importance of early stage commercialization strategies in the re-born market of eco-friendly vehicles.

This study aimed two objectives: The first objective was to explore carriers' intentions to use cargo electric vehicles (EVs) and the factors influencing these intentions in last-mile delivery (LMD). The other objective was to provide recommendations for policymakers and manufacturers to promote and customize cargo EVs to meet the requirements of carriers in the LMD sector.

In this study the authors constructed a research framework that adjusted and extended the original technology acceptance model (TAM). The proposed model combines eight psychological factors, including attitude, perceived usefulness, perceived ease of use, perceived risk, public engagement, face consciousness, financial incentive policy and carrier intention, in which four factors, namely attitude, perceived usefulness, perceived ease of use and intention, were obtained from the original TAM and the four remaining factors, namely perceived risk, public engagement, face consciousness and financial incentive policy, were added.

The results showed that psychological factors such as attitude, perceived ease of use, perceived risk, public engagement and face consciousness might affect carriers' intentions to use electric cargo vehicles in LMD service in Vietnamese cities. These results agree with the previous studies and confirm that attitude, perceived ease of use, perceived risk, public engagement and face consciousness might be important for shaping intention to use electric cargo vehicles in emerging markets.

This study has several limitations, first, the data were collected in Vietnam, a country with weak academic contributions. Therefore, these findings might not be generalizable to other areas. The authors expect to apply the same research framework to other countries to explore the similarities and differences across the countries. Second, the authors conducted the surveys in three cities; except for Hanoi, the other two cities are not really large markets in the LMD service sector. Third, the authors ignored the relationship between the demographic characteristics and electric cargo vehicles. Further studies should address this gap.

Based on the findings, manufacturers should ensure the high-quality performance of electric cargo vehicles in terms of extending driving range and shortening recharging time. Policymakers should develop the roadmaps for electric cargo vehicles, starting from switching from conventional cargo motorcycles to electric motorcycles. In addition, developing public charging infrastructure should be prioritized, which is the fundamental basis for operating electric cargo vehicles. Finally, manufacturers should research and develop a product that would improve the reputation of carriers because carriers with higher face consciousness are more likely to pursue brand-name and high-priced products in order to enhance their reputation.

This study contributes to the literature in two aspects: First, the authors investigated intentions to use electric cargo vehicles in LMD service, which is rare from other studies, and they further identified the psychological determinants of carriers' intentions. Second, the findings increase the knowledge of carriers' intentions and suggest implications for policymakers and manufacturers to promote the adoption of electric cargo vehicles in last-mile deliver service.

The purpose of this paper is to investigate the diffusion dynamics of electric and hybrid commercial vans and its enabling factors in the city logistics (CL) contexts. The case of parcel delivery in Torino, Italy, is considered. Attention is paid to the influence on the choice of low impact vehicles of not only public strategies but also operational aspects characterizing urban freight distribution systems.

A System Dynamics model based on the Bass diffusion theory computes the number of adopters of low-emission vehicles together with the quantity of vans required and the associated economic savings. The model includes variables about freight demand, delivery frequency, van carrying capacity, routes, stops, distances traveled, and vehicle charging stations. A sensitivity analysis has been completed to identify the main diffusion levers. The focus is on advertising and other drivers, such as public contributions, taxes traditional polluting vehicles are subjected to, as well as on routing optimization strategies.

Advertising programs, green image, and word-of-mouth drive market saturation, although in a long time period. In fact, low-impact vehicles do not offer any economic advantage over traditional ones requiring higher investment and operating costs. Public incentives to purchase both green vehicles and charging stations, together with carbon taxes and a congestion charge affecting polluting vehicles, are able to shorten the adoption time. In particular, public intervention reveals to be effective only when it unfolds through a number of measures that both facilitate the use of environmentally friendly vehicles and discourage the adoption of traditional commercial vans. Route optimization also hastens the complete market saturation.

This work fosters research about the mutual relationships between the diffusion of low-emission commercial vehicles and the operational and contextual CL factors. It provides a structured approach for investigating the feasibility of innovative good vehicles that might be part of assessments of CL measures and requirements. Finally, the model supports studies about the cooperation among stakeholders to identify effective commercial vehicle fleets.

This study fosters collaboration among CL players by providing a roadmap to identify the key factors for the diffusion of environmentally friendly freight vehicles. It also enables freight carriers to assess the operational and economic feasibility of adopting low-impact vehicles. Finally, it might assist public authorities in capturing the effects of new urban transportation policies prior to their implementation.

Most of the current CL literature defines policies and analyzes their effects. Also, there are several contributions on the diffusion of low emission cars. The present study is one of the first works on the diffusion of low-impact commercial vehicles in urban areas by considering the associated key operational factors. A further value is that the proposed model combines operational variables with economic and environmental issues.

The use of electric vehicles has received popularity as alternative fuel vehicles to reduce greenhouse gas emissions and energy cost, which are expected to perform a crucial role in the near future of emerging mobility markets. The purpose of this empirical study is to analyse the role of electric vehicle knowledge in predicting consumer adoption intention directly and indirectly in the backdrop of an emerging market.

The study approached an extended version of “Technology acceptance model” (TAM) based on the integrated framework of “knowledge-beliefs-intention”. The model was tested via direct and indirect path analyses with the data collected from Indian respondents using an online survey.

The results indicate the robustness of the present research model, which shows that consumer adoption is significantly driven by electric vehicle knowledge, perceived usefulness, perceived ease of use and perceived risk. Electric vehicle knowledge has emerged as the most powerful cognitive measure, which directly affects the adoption intention along with the measures of “TAM”. Additionally, this also poses a higher indirect effect on adoption intention in the integrated model.

The study has focused on potential young and educated consumers, which may not be warranted to generalise the research findings, while youth or millennials are more receptive to adopt innovative and clean technology products like electric vehicle. Based on the findings, implications are offered for encouraging electric vehicles in the backdrop of emerging automobile markets.

Concerning this cognitive phenomenon of knowledge, scant literature has been explored the role of subjective knowledge in consumer adoption for electric vehicles, particularly in the emerging markets like India. Thus, the present study analyses how consumers' knowledge about electric vehicle affects their decision to adopt this in the near future of Indian zero-emission mobility market.

Velomobiles or bicycles cars are human-powered vehicles, enclosed for improving aerodynamic performance and protection from weather and collisions. The purpose of this paper is to design and develop a three-wheeled velomobile equipped with a hybrid human-electric propulsion system.

The mechanical layout has been developed in order to improve safety, a CAD code has been used for the design and the dynamic performances have been studied by means of specific multi-body codes. The electric propulsion system has been designed both with analytical and FEM methods.

A special three-wheeled tilting vehicle layout equipped with a four-bar linkage connection has been developed. A particular synchronous reluctance machine has been developed, which is very suitable for human-electric hybrid propulsion. A MATLAB code for integrated mechanical and electrical analysis has been developed.

A new kind of light vehicle has been conceived. A new synchronous reluctance machine with high efficiency has been developed. A performance analysis in electric, human and hybrid working modes has been presented, which takes into account the specific features of both the electric motor and the pedaling legs. A prototype of the vehicle has been built.

The Electric Vehicles Initiative (EVI) is a multi-government policy forum devoted to speed up the introduction and adoption of electric vehicles (EVs) worldwide. EVI key themes for sustainable development include energy-efficient transportation with e-mobility (drive-by science and technology), reduced greenhouse gas emissions, decreased oil dependence and improved local air quality. India's transport sector contributes around 142 million tons of CO₂ every year, with road transport contributing 123 million tons.

Review methodology forms a basis for knowledge development, creating guidelines for policy and practice. Quality assessment of review articles is by using mixed methods appraisal tool (MMAT).

The research trends on Sustainable Development Goal (SDG) technological and social aspects highlight the critical role of technology in economic and social development, emphasising infrastructure development and communication of government policy and rewards for awareness and end-user acceptance.

The scenario brings a school of thought if it is equally important to address a social perspective to improve India's perception and acceptance of technology-enabled EVs.

The purpose of this paper is the investigation of the main aspects of optimal reliability allocation with respect to the design of hybrid electric vehicles. In particular, with reference to the hybrid electric vehicle propulsion system, the problem of data uncertainty, due to a scarce knowledge of the components' reliabilities, is taken into account. This problem is crucial for new technology systems and it is faced with a Bayesian approach: components' reliabilities are considered as random variables, characterised in the paper by negative log‐gamma distributions.

The main aspects of optimal reliability allocation with the design of hybrid electric vehicles are presented, pointing out the opportunity of a reliability evaluation in the planning stage.

The topic of a series hybrid vehicle reliability is addressed, nevertheless results can be easily extended to the parallel configuration. In particular, the opportunity of a reliability evaluation of the propulsion system in the design stage is highlighted, mainly when new technology components are involved.

The value of the paper consists in the methodology allowing to express the system reliability uncertainty as a function of component uncertain data. Then, as far as concern the practical implications, the optimal allocation of the components' reliabilities can be efficiently performed, in order to minimise the system total cost respecting a prefixed value of the system reliability. In the final part of the paper, a numerical application, related to a series hybrid electric vehicle propulsion system, is presented to show the feasibility of the approach.