Green Energy and Intelligent Transportation最新文献

Electric vehicles (EVs) have the potential to mitigate the severity of significant concerns including environmental pollution and reliance on fossil fuels; however, despite strong governmental promotional efforts, their market penetration is still at the nascent stage. This paper empirically investigates the factors that affect the consumers' intention to adopt EVs by conducting an exhaustive literature review. The initial search resulted in 1,690 publications, but after a thorough exclusion process, 537 articles were deemed relevant and were sorted by source, publication year, country of origin, data collection method, and research domain. The results revealed the influential factors over individuals’ desire to adopt an EV were categorized into four main types (contextual, situational, demographic, and psychological); situational factors, that can act as both barriers and motivators, had the most influencing components. The most cited barriers to adoption of EVs were found to be the lack of charging stations availability and their limited driving range. The most cited motivators to EV adoption were found to be reduction in air pollution and the availability of policy incentives. The findings of this study may guide policymakers in formulating effective transportation and energy policies, as well as provide guidance to those who are responsible for designing EVs that fit the needs and demands of potential consumers.

Efficient public transportation has always intrigued extensive research. Aiming to improve the commuting efficiency and fuel economy of the autonomous hybrid electric buses in the Bus Rapid Transit (BRT), a cloud-based eco-driving solution adopting dynamic programming and model predictive control is proposed in this paper. This solution contains an upper-level cloud-based scheduling strategy and a lower-level onboard predictive energy management, which is conceived to function in a Cyber-physical system of the cooperative vehicle-infrastructure system. The scheduling model carefully considered coupled spatiotemporal constraints for the driving of autonomous BRT buses, including traffic lights, traffic regulations, stations, and ride comfort. The onboard energy management leverages the pre-planned scheduling information to achieve near-optimal fuel economy. The eco-driving solution is examined in three scenarios with intersections, stations, and ramps. Simulation results show that the proposed method can deal with different spatiotemporal limits along the route, with virtually no non-essential stops and sudden acceleration or braking, and achieves 97%–98% energy-saving potential compared with the baseline performance.

Battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs), whose exhaust pipes emit nothing, are examples of zero-emission automobiles. FCEVs should be considered an additional technology that will help battery-powered vehicles to reach the aspirational goal of zero-emissions electric mobility, particularly in situations where the customers demand for longer driving ranges and where using batteries would be insufficient due to bulky battery trays and time-consuming recharging. This study stipulates a current evaluation of the status of development and challenges related to (i) research gap to promote fuel-cell based HEVs; (ii) key barriers of fuel-cell based HEVs; (iii) advancement of electric mobility and their power drive; (iv) electrochemistry of fuel cell technology for FCEVs; (v) power transformation topologies, communication protocols, and advanced charging methods; (vi) recommendations and future prospects of fuel-cell HEVs; and (vii) current research trends of EVs, and FCEVs. This article discusses key challenges with fuel cell electric mobility, such as low fuel cell performance, cold starts, problems with hydrogen storage, cost-reduction, safety concerns, and traction systems. The operating characteristics and applications of several fuel-cell technologies are investigated for FCEVs and FCHEVs. An overview of the fuel cell is provided, which serves as the primary source of energy for FCHEVs, along with comparisons and its electrochemistry. The study of power transformation topologies, communication protocols, and enhanced charging techniques for FCHEVs has been studied analytically. Recent technology advancements and the prospects for FCHEVs are discussed in order to influence the future vehicle market and to attain the aim of zero emissions.

The concept of the Internet-of-Batteries (IoB) has recently emerged and offers great potential for the control and optimization of battery utilization in electric vehicles (EV). This concept, which combines aspects of the Internet-of-Things (IoT) with the latest advancements in battery technology and cloud computing, can provide a wealth of new information about battery health and performance. This information can be used to improve battery management in a number of ways, including continuous battery prognosis and improved battery and vehicle management. In this paper, we reviewed in detail the basic structure of IoB, based on many existing studies. We also explored the potential benefits of this new approach, such as continuous battery prognosis and improved battery and vehicle management. Implementing the IoB in EVs is not without challenges, as the IoB faces a number of challenges, including the security of battery data, cross-platform functionality, and the technical complexities of applying IoB on a large scale. However, the potential benefits of the IoB are significant and with continued research and development, it has the ability to revolutionize the EV industry. The purpose of this review paper is to provide a comprehensive overview of the IoB, discussing its potential benefits and challenges. The paper also provides a roadmap for the future development of IoB, highlighting the key areas that need to be addressed to fully realize the potential of this technology.

In order to enhance the energy-saving potential of electric vehicles, a lane change decision method based on vehicle-to-everything (V2X) is designed to further improve the economics of intelligent connected electric vehicles. Firstly, the traversal test of electric vehicles is conducted at different speeds and accelerations to construct an energy consumption cloud model that reflects the mapping relationship between electric vehicle speed, acceleration and power. Next, the traffic flow information from V2X is used to train the long short-term memory neural network model optimized by particle swarm optimization (PSO-LSTM) for the prediction of the future speed of the vehicle in front of each lane. Then, according to the established energy consumption cloud model, the power performance corresponding to the predicted vehicle speed is obtained. Finally, a lane change decision method based on analytic hierarchy process (AHP) is established, and it is applied in four typical parallel scenarios to verify the robustness and effectiveness of the decision method. Simulation tests were conducted in a simulated urban traffic environment, involving both single-lane change scenarios and continuous lane change scenarios. The results show that this method can accurately and effectively select the lane with the best economic performance. Compared with the driving strategy of selecting a fixed lane, the energy consumption can be improved by up to 27.2% in the continuous lane change scenario.

Self-discharge is a spontaneous process that has considerable adverse effects on the performance of supercapacitors. In order to quantitatively investigate the contribution of self-discharge mechanism, this paper proposed a theoretical self-discharge model for carbon electrode of supercapacitors based on electric double layer theory. Three physical contributions, i.e., side reactions, ion diffusion, and ohmic leakage, were investigated. In addition, self-discharge measurement of carbon electrode was performed to validate such theoretical model. The results indicated that the potential drop due to side reactions was negligible throughout the self-discharge process in all cases. In addition, ion diffusion dominated for low initial potential and short holding time, accounting for 50%–80% of self-discharge. On the other hand, ohmic leakage dominated for high initial potential and long holding time. Furthermore, the potential drop due to ion diffusion increased monotonously with time while the potential drop due to ohmic leakage remained constant throughout the self-discharge. Moreover, the potential drop due to ohmic leakage increased with the increase in holding time, which compensated for the decreasing potential drop due to ion diffusion. This could explain the fact that the total electrode potential decay during the self-discharge was independent of holding time. Finally, dimensional analysis was performed to predict self-discharge. Time constants of side reactions, ion diffusion, and ohmic leakage were derived. Overlapping dimensionless electrode potential versus dimensionless time indicated that such dimensional analysis was generally applicable to predict self-discharge of carbon-based supercapacitors at a given initial potential and time.

A traffic signal controller is an essential part of a signalized intersection to alleviate congestion and pollution by ensuring safety. However, the available research solutions are focused on homogeneous traffic scenarios, whereas heterogeneous traffic is the reality in most countries. Hence, a traffic signal control scheme suitable for heterogeneous traffic conditions is proposed in the current study using Reinforcement Learning. A novel reward function with an objective to reduce the traffic residual is defined and a combination of exploration and exploitation optimal policy is applied which made the system learn quickly. The proposed scheme can choose the appropriate phase sequence with optimal signal lengths based on traffic demand on each approaching road. The simulation results proved that the proposed model is well-suited for heterogeneous traffic conditions and its performance against the actuated traffic signal controller is significant in reducing the green time wastage and mean waiting time at the intersection.

Due to the rising concept of advanced air mobility (AAM), electric vertical take-off and landing (eVTOL) aircraft has become the hotspot for academic research and commercial application. This paper provides a comprehensive review of latest researches related to autonomous eVTOL. It examines key technologies involved in autonomous eVTOL, including automated flight control, sensing & perception, safety & reliability, and decision making. It also addresses the technical, regulatory, and societal challenges associated with the wholesale adoption of autonomous eVTOL into AAM. The paper concludes with a discussion of future trends and recommendations, including the importance of integration with air traffic management, urban infrastructure and human–machine interaction. It aims to be a useful resource for those involved in the research, policy, and industry of autonomous eVTOL technology.

Rising negative externalities, including greenhouse gas emissions, climate change, and environmental pollution, shows the need for sustainable and environmentally friendly modes of transportation. Adopting zero-emission, environmentally friendly electric buses in public transportation systems can be an effective solution for both developing and developed countries, including India. While the Indian government is making numerous efforts to promote electric buses in commercial and public transportation systems, it faces several formidable obstacles. This research objective is to analyze and evaluate the primary factors influencing the adoption and usage of electric buses in the Indian public transportation system, within the limited available resources. A survey questionnaire is prepared with several perceptual subjects for the key perceived barriers. An empirical analysis using Structural Equation Modeling (SEM) is then performed to identify the critical barriers. The result of this study demonstrates that the infrastructural barriers substantially impact the adoption and utilization of electric buses. Further, the study provides critical insights and managerial implications for decision-makers.