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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.

The purpose of this paper is to study the electric vehicle (EV) drive efficiency of a traction motor considering regenerative braking according to various motor cores.

A software program was developed to predict the driving performance of an EV. It determines the driving mileage, the required power of the traction motor, and the operation points on a torque-speed map when drive cycles are given. The driving performance is calculated from the battery capacity, vehicle specification, and efficiency map of the traction motor computed using the finite element analysis.

As a result, the motor core is a significant design variable for raising the driving mileage of an EV. It is noted that the change of electrical steels used for the motor core is the lowest priced method of increasing the driving range by 2 km.

The comparative analysis of motor core by replacing 35PN250 to 25PNX1250F results in improvement effects traveling 4.62 and 5.16 km farther in the Simplified Federal Urban Driving Schedule (SFUDS) and Highway Fuel Economy Driving Schedule (HWFET), respectively. It was also verified that regenerative braking system is able to enhance drive efficiency by 29-31.3 km in the SFUDS and 6.5-7.3 km in the HWFET. From comparison of price rise for increasing driving mileage by 2 km, it is noted that the change of electrical steels used for the motor core is the lowest priced method.

The popularity of electric and hybrid cars has been growing worldwide, and Portugal is no exception. Companies have been offered incentives as a way to promote the transition to more sustainable transportation systems and supply chains. Celebrities and influencers are endorsing the new technology, and consumer preferences are changing. However, in Portugal, there are still consumers with misconceptions about the autonomy, cost and reliability of electric cars, which may favour the choice of a conventional car, in a new car purchase decision-making process.

In this study, we analyse whether purchase intention in the near future of an electric car varies with a pro-environmental lifestyle, perceived symbolic value of the electric car, mobility patterns, age, and place of residence, (performance, social, financial and externalities) risk avoidance, consumer perceptions, knowledge about the cost, the autonomy and the existing infrastructures. A sample of 308 Portuguese consumers was collected with an online survey. Results from survey subsample analysis of 170 consumers who unequivocally claim that would opt for an electric vehicle or not show a positive relationship between the purchase intention of an electric car, the fuel cost increase, the proximity of convenient charging places and battery lifetime perception. It was also found that age, knowledge and perceived symbolic value of the electric car, in general, have a positive influence on consumers' choice of an electric car. A negative relationship was found between the purchase intention, social and financial risk avoidance, perceived symbolic value of the electric car in particular and the number of cars each family has.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty.

First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform.

Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system.

The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

This paper aims to address the broad question of how organizations capture value from foresight exercises. Through a comparative case analysis, this paper looks at what firms do to make the information usable and create value. It explores factors that cause different firms to respond differently to the same trends. It analyzes the passenger car segment of the automobile industry and the response of six major firms to fossil fuel and changing environmental regulations through an analysis of their policies and strategic activities, such as new product development. It finds foresight to be an important link between firm capabilities and environmental changes.

This paper adopts the case approach to capture the linkage between the issue and the context (Yin, 1994) and uses multiple cases to explore the variables by comparing and contrasting the cases on key aspects (Eisenhardt and Graebner, 2007). As the paper ' s objective is to understand the similarities and differences between dominant firms in the sector, it chooses through theoretical sampling, six firms that have a presence in all the major regions of the world – two each from the USA, Europe and Japan – Ford, General Motors, Volkswagen, Renault, Toyota and Honda. This sample represents the firms and regions traditionally strong in the passenger car industry.

Thus, it is seen that the relationship that was posited in the conceptual model between the goal of the firms, the vision of the future and the nature of products and approach to technology/competence development seems to be valid. However, in addition, the paper perceives that some additional linkages that link between foresight and the goals and vision of the future seem to be influenced by the extent of uncertainty. In addition, the decisions regarding portfolio of products and approaches to technology and competence development seem to be also influenced by the perception of existing competencies and the external competitive context.

This paper was based on multiple cases created out of secondary information, hence the constructs used are those which are perceived and stated.

The paper could help firms understand decisions related to technology choices in field involving high levels of uncertainty and competition.

This paper could improve learning processes from foresight exercises, and enable strategic decisions to be taken on these.

Thus, this paper has explored the linkages between what firms perceive and state, and what is reflected in their actions. It has looked at this linkage from the perspective of foresight, and the strategic perspective of the firm. It has come up with additional issues and questions that influence this relationship. These can inform future research in this domain.

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 case deals with the unforeseen uncertainties faced by Reva, the first electric car of India, while entering the Indian market. The company was able to take up the challenge of making an energy efficient car. As a new product, Reva achieved operational success, developing an electric, low energy car. Its marketing strategies had limited consumer pull and had to be strengthened to gain consumer acceptance. The ecosystem worldwide is looking for support from governments on the concept and the infrastructure of this product category.

The purpose of this paper is to design an improved parallel regenerative braking system (IPRBS) for electric vehicles (EVs) that increases energy recovery with a constant brake pedal feel (BPF).

The conventional hydro-mechanical braking system is redesigned by incorporating a reversing linear solenoid (RLS) and allowed to work in parallel with a regenerative brake. A braking algorithm is proposed, and correspondingly, a control system is designed for the IPRBS for its proper functioning, and a mathematical model is formulated considering vehicle drive during braking. The effectiveness of IPRBS is studied by analyzing two aspects of regenerative braking (BPF and regenerative efficiency) and the impact of regenerative braking contribution to range extension and energy consumption reduction under European Union Urban Driving Cycle (ECE).

IPRBS is found to maintain a constant BPF in terms of deceleration rate vs pedal displacement during the entire braking period irrespective of speed change and deceleration rate. The regenerative ratio of IPRBS is found to be high compared with conventional parallel regenerative braking, but it is quite the same at high deceleration.

A constant BPF is achieved by introducing an RLS between the input pushrod and booster input rod with appropriate controller design. Comparative analysis of energy regenerated under different regenerative conditions establishes the originality of IPRBS. An average contribution ratio to energy consumption reduction and driving range extension of IPRBS in ECE are obtained as 18.38 and 22.76, respectively.