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Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective
... The adoption of EVs represents a crucial step towards achieving global climate goals by eliminating emissions from internal combustion engines and reducing the overall carbon footprint [5,6]. This transition is not merely a technological substitution, but symbolizes a broader social commitment to promote sustainable practices and ensure a cleaner and healthier future for generations to come [7,8]. ...
... In this chapter, the individual processes for obtaining results for the final acquisition of an EV energy model, which can be used for energy analysis under Italian conditions, will be presented. 7 First, exploratory data analysis (EDA) was performed to preliminarily verify and analyze the data. Subsequently, an electric vehicle energy model based on the deep neural network (DNN) technique was implemented in Python. ...
Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of Deep Neural Network (DNN) technology. The study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy. which is characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. The research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport
decision makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.
... The adoption of EVs represents a crucial step towards achieving global climate goals by eliminating emissions from internal combustion engines and reducing the overall carbon footprint [5,6]. This transition is not merely a technological substitution but symbolizes a broader social commitment to promote sustainable practices and ensure a cleaner and healthier future for generations to come [7,8]. ...
Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of deep neural network (DNN) technology. This study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy, which are characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. This research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport decision makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.
... However, the process of designing and fabricating educational EV prototypes is complex and multifaceted, requiring a comprehensive understanding of various components, systems, and methodologies [12]. From battery management systems and electric motors to transmission systems and chassis design, students must navigate a wide range of technical challenges [13]. The graph in Figure 1 illustrates the projected adoption of electric vehicles in major automotive markets from 2020 to 2040, providing a comprehensive view of the EV market's past growth and future potential. ...
Citation: Ravi, R.; Belkasmi, M.; Douadi, O.; Faqir, M.; Essadiqi, E.; Gargab, F.Z.; Ezhilchandran, M.; Kasinathan, P. Advancing Sustainable Transportation Education: A Comprehensive Analysis of Electric Vehicle Prototype Design and Fabrication. World Electr. Veh. J. 2024, 15, 354. https://doi.org/10.3390/ Abstract: The global shift towards electric vehicles (EVs) has necessitated a paradigm shift in engineering education, emphasizing hands-on experiences and innovative learning approaches. This review article presents a comprehensive analysis of the design and fabrication process of an educational EV prototype, highlighting its significance in preparing future engineers for the rapidly evolving EV industry. The article delves into the historical development and recent trends in EVs, providing context for the growing importance of practical skills in this field. A detailed examination of the key components and systems in modern EVs, such as battery packs, electric motors, transmission systems, and chassis design, lays the foundation for understanding the complexities involved in EV prototype development. The methodology section explores the research approach, conceptual design, simulations, material selection, and construction techniques employed in the creation of an educational EV prototype. The evaluation and testing phase assesses the prototype's performance, safety, and reliability, offering valuable insights into the lessons learned and areas for improvement. The impact of such projects on engineering education is discussed, emphasizing the importance of hands-on learning experiences and interdisciplinary collaboration in preparing students for future careers in the EV industry. The article concludes by addressing common challenges faced during EV prototype projects and providing recommendations for future educational initiatives in this field.
V2B Vehicle to Building V2G Vehicle to Grid V2H Vehicle to Home V2V Vehicle to Vehicle 2 V2X Vehicle to Everything VRP Vehicle Routing Problem 3 Abstract The majority of global road transportation emissions come from passenger and freight vehicles. Electric vehicles (EV) provide a sustainable transportation way, but customers' charging service related concerns affect the EV adoption rate. Effective infrastructure planning, charge scheduling, charge pricing, and electric vehicle routing strategies can help relieve customer perceived risks. The number of studies using machine learning algorithms to solve these problems is increasing daily. Forecasting, clustering, and reinforcement based models are frequently the main solution methods or provide valuable inputs to other solution procedures. This study reviews the studies that apply machine learning models to improve EV charging service operations and provides future research directions. Highlights • Consumers' charging service related concerns affect the EV adoption rate. • Infrastructure planning, charge scheduling, dynamic pricing, and routing studies can alleviate consumer perceived risks. • Studies increasingly use machine learning (ML) methods to solve these problems. • This review includes the charging service related studies that use ML. • Future research directions provide ways to improve the charging service research area.
With the advancement of technology, sharing and autonomous driving will be the two major themes in the future transportation field, and SAVs (Shared autonomous vehicles) will combine the two things. When SAVs come to market, they will affect the transportation system, so the objective of this paper is to examine people’s intentions to use SAVs and clarify the factors affecting people’s intentions to use SAVs. Due to the application of the theory of planned behavior (TPB) in traffic travel research having important practical significance, this paper used an extended theory of planned behavior model to study people’s intentions to use SAVs. Some important findings are found that the intention to use SAVs is directly affected by attitude, subjective norm, perceived behavior control, barrier, and effects of a public health emergency, and indirectly affected by perceived risk, technical interest, government policy, and environmental awareness. Moreover, perceived behavior control has the mediating effect between government policy and intention to use SAVs, between technical interest and intention to use SAVs, and between subjective norm and intention to use SAVs. According to the influence degree of related influencing factors, the corresponding development recommendations on SAVs development are put forward. The research results of this paper contribute to the subsequent listing of SAVs, promote the further development of intelligent transportation, and provide the scientific basis for future travel policy formulation and traffic planning.
Autonomous driving techniques are promising for improving the energy efficiency of electrified vehicles (EVs) by adjusting driving decisions and optimizing energy requirements. Conventional energy-efficient autonomous driving methods resort to longitudinal velocity planning and fixed route scenes, which are not sufficient to achieve optimality. In this paper, a novel decision-making strategy is proposed for autonomous EVs to maximize energy efficiency by simultaneously considering lane-change and car-following behaviors. Leveraging the deep RL algorithm, the proposed strategy processes complex state information of visual spatial-temporal topology and physical variables to better comprehend surrounding environments. A rule-based safety checker system is developed and integrated downstream of the RL decision-making module to improve lane-change safety. The proposed strategy is trained and evaluated in dynamic driving scenarios with interactive surrounding traffic participants. Simulation results demonstrate that the proposed strategy remarkably improves the EV’s energy economy over state-of-the-art techniques without compromising driving safety or traffic efficiency. Moreover, the results suggest integrating visual state variables into the RL decision-making strategy is more effective at saving energy in complicated traffic situations.
Equitable and sufficient charging infrastructure is required for transport decarbonization to reach its goals. Despite increased electric vehicle infrastructure roll out rates, there is still considerable uncertainty regarding the charging market. For example, studies have evidenced disparities in electric vehicle charging placement, however , predictable as the market caters for early adopters. While there is an emerging discourse surrounding social equity in charging infrastructure, this is scattered across interdisciplinary research covering broader aspects of electric vehicle infrastructure provision with a lack of studies consolidating issues. This study aims to synthesize evidence on social equity in various aspects of electric vehicle charging infrastructure provision and set an agenda for centering social equity in the debate. Findings of this critical synthesis of research have helped to draw out the complexities involved in the equitable roll out of electric vehicle charging infrastructure, which are interlinked with an array of other dimensions including the affordability of electric vehicle purchase. Research into solutions and best practice has shown examples of local target setting, monetary incentives (grants, loans and rebates for electric vehicle purchase and charging infrastructure and smart energy tariffs) and other policy incentives (increased public overnight charging, electric car-clubs, extended battery warranties for secondhand vehicles) that can or have been employed to redress the balance. The outcomes could be utilized when developing and implementing electric vehicle strategies to support uptake across all people. Policy implications and further study suggested could ensure that communities and individuals are not locked out of the benefits of investment.
Shared autonomous electric vehicles (SAEVs) have recently attracted significant public interest. The dynamic ride-sharing using SAEVs appears to have the advantages of reducing travel costs and relieving urban traffic congestion. It is meant to improve the practical application value of the dynamic ride-sharing mode of SAEVs. In this paper, to reduce the solution time complexity, a pre-matching algorithm considering the driverless and charging characteristics of SAEVs is developed, and then a two-stage, graph-based many-to-one ride-matching (GMOM) algorithm is proposed for the dynamic ride-sharing problem in the Autonomous Mobility-on-Demand system (AMOD). The dataset from DiDi during the peak travel time and the real-time traffic flow from the AutoNavi map were used to verify the effects of the method. The results demonstrate that the GMOM approach can effectively reduce computational complexity and improve user satisfaction. The dynamic ride-sharing mode based on the GMOM algorithm has a 5.67% higher service rate and 45.56% more vehicle calls than the non-ride-sharing mode under the same conditions. It is found that the cost-effectiveness of using ride-sharing services is relatively high for 10–20 km trips during peak travel time and the dynamic ride-sharing may extend total travel time but will reduce passengers’ waiting time.
Popularization of wireless power transfer (WPT) has promoted the multi-disciplinary explorations and integration. It gradually incubates numerous attractive solutions for industrial, domestic and medical scenarios. Therefore, a series of smart WPT emerges, and these state-of-the-arts will bring a great impact on the modern technology and human society. This review investigates and discusses the opportunities and challenges of the smart WPT, especially in the areas of wireless energy modulation, conversion and intellectualization. The key is to reveal the methodologies, approaches and foresights for the emerging technologies of the smart WPT. As one of the most promising development trends, the smart WPT will embrace the information-energy internet and construct the wireless energy router and wireless energy internet that support the wireless energy trading for peers, communities and energy internets.
State/temperature monitoring is one of the key requirements of battery management systems that facilitates efficient and intelligent management to ensure the safe operation of batteries in electrified transportation. This paper proposes an online end-to-end state monitoring method based on transferred multi-task learning. Measurement data is directly used for sharing information generation with the convolutional neural network. Then, the multiple task-specific layers are added for state/temperature monitoring. The transfer learning strategy is designed to improve accuracy further under various application scenarios. Experiments under different working profiles, temperatures, and aging conditions are conducted to evaluate the method, which covers the wide usage ranges in electric vehicles. Comparisons with several benchmarks illustrate the superiority of the proposed method with better accuracy and computational efficiency. The monitoring results under extremely current working profiles and variable internal and external conditions are evaluated. Results show that the mean absolute error and root mean square error of state of charge and state of energy estimation are less than 2.31% and 3.31%, respectively. The above errors in the prediction of future temperature five steps ahead are less than 0.89℃ and 1.29℃, respectively. The framework is also suitable for monitoring second-life batteries retired from electric vehicles. This paper illustrates the potential application of data-driven multi-state monitoring throughout the entire battery life.
Green-hydrogen production is vital in mitigating carbon emissions and is being adopted globally. In its transition to a more diverse energy mix with a bigger share for renewable energy, United Arab Emirates (UAE) has committed to investing billions of dollars in the production of green hydrogen. This study presents the results of the techno-economic assessment of a green-hydrogen-based commercial-building microgrid design in the UAE. The microgrid has been designed based on the building load demand, green-hydrogen production potential utilizing solar photovoltaic (PV) energy and discrete stack reversible fuel cell electricity generation during non-PV hours. Given the current market conditions and the hot humid climate of the UAE, a performance analysis is derived to evaluate the technical and economic feasibility of this microgrid. The study aims at maximizing both the building microgrid’s independence from the main grid and its renewable fraction. Simulation results indicate that the designed system is capable of meeting three-quarters of its load demand independently from the main grid and is supported by a 78% renewable-energy fraction. The economic analysis demonstrates a 3.117-/kWh levelized cost for storing hydrogen as electricity. Additionally, the levelized cost of system energy was found to be less than the current utility costs in the UAE. Sensitivity analysis shows the significant impact of the capital cost and discount rate on the levelized cost of hydrogen generation and storage.
Autonomous Vehicles (AVs) have advanced rapidly in recent years as they promise to be safe and minimize the burden coming from the driving task. AVs share the road with various categories of vehicles as Emergency Vehicles (EMVs) (e.g police and ambulance vehicles). When being approached by an active EMV, it is natural to expect all vehicles to cooperate with EMV, such that the EMV travel time is minimized. The decision-making block of an AV includes the responsibility of instructing the AV to change lanes, which is typically handled by the Lane Change Decision (LCD) model. A typical LCD model tends to overlook the presence of EMVs around, as they neglect the impact of the lane change on the EMV utility. To address this challenge, this paper proposes an Emergency Vehicle Aware LCD via utilizing Deep Reinforcement Learning. To our best knowledge, this is one of the pioneering works that propose a DRL solution for the problem, addressing important limitations that have been identified. The proposed solution was evaluated against a rule-based LCD known as MOBIL in terms of safety and level of cooperativeness with the EMV. Some key results found from the comparison between the proposed solution and MOBIL are (1) identical safety levels,(2) proposed solution is takes far less time to give up the lane when being approached by an EMV, and (3) proposed solution never blocks the path of the EMV, whereas MOBIL occasionally block the path.
A shared autonomous electric vehicle (SAEV) fleet and the vehicle-to-grid (V2G) strategy both have great potential to reduce GHG emissions. As these concepts have complementary value, they are even more promising combined. However, to the best of our knowledge, no research has yet been conducted on locating charging infrastructure for SAEVs with V2G feasibility. For this construction, the challenge lies in the fact that both mobility demand (mainly for SAEVs) and energy (for any installation of charging infrastructure) have a major influence on this problem. To find the optimal charging infrastructure (CI) allocation for SAEVs with V2G operations, both mobility requirements and grid constraints must be considered. In this paper, we find that optimization models are the most frequently used method to solve the CI allocation problem. We conduct separate examinations of the V2G and SAEVs location optimization models that have been formulated in the literature, for which objective functions are used, and which constraints are considered (with respect to mobility and the electric grid). We find that SAEV and V2G models have overlapping elements, but remain disjunct in their respective perspectives. CI allocation for SAEVs mainly takes mobility into account, but tends to ignore grid constraints or impacts. On the other hand, CI allocation for V2G focuses on the distribution network and the grid, forgetting about mobility demand. To take advantage of the SAEV-V2G potential, future research should combine mobility and grid aspects to find the optimal CI locations for SAEVs with V2G feasibility.
In this review paper, we show that the current battery electric vehicle (BEV) scale-up relies on several key technologies which all have detailed roadmaps with good track records for being met. These roadmaps include lightweighting of vehicle bodies using lightweight materials and architecture/structure design, and improvements in BEV powertrain with regard to the powertrain architecture/system design, battery and motor technology development. However, as technology take-up accelerates, our novel analysis suggests supply of zero carbon electricity may become a serious constraint. We find that the technical potential for abating the demand for electricity through powertrain and lightweighting improvements is just over a quarter of the projected total. Four promising avenues to mitigating this constraint – battery reusing and interoperable charging technology, shared mobility, advanced sensing technology, and novel compact space frame construction - are explored in brief, potentially enabling the large-scale deployment of BEVs without exhausting the supply of non-emitting electricity.
The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).
Connected and automated vehicles (CAVs), with the capability to exchange information with other CAVs and the infrastructure, open up new opportunities in road traffic systems including the recently introduced concept of lane-free traffic (LFT). In an LFT environment, vehicles move without the need for traditional fixed lanes. It is an emerging research area where many forms of LFT strategies are being investigated that can benefit road capacity optimization, thus reducing traffic congestion and increasing traffic efficiency. LFT strategies can also benefit from current research on non-lane based driving behavior observed in some countries. Thus, this paper overviews the available literature on the current non-lane based driving and future LFT. It also studies the taxonomy used in literature to clearly distinguish the two traffic modes. It details the current developments in LFT research and the underlying technologies. From these developments, the paper provides an overview of currently available approaches for realizing LFT. Finally, it discusses the policy considerations needed for the seamless integration of LFT into the current infrastructure once CAVs become mainstream.
The Autonomous Mobility-on-Demand system is an emerging green and sustainable transportation system providing on-demand mobility services for urban residents. To achieve the best recharging, delivering, and repositioning task assignment decision-making process for shared autonomous electric vehicles, this paper formulates the fleet dynamic operating process into a multi-agent multi-task dynamic dispatching problem based on Markov Decision Process. Specifically, the decision-making process at each time step is divided into 3 sub-processes, among which recharging and delivery task assignment processes are transformed into a maximum weight matching problem of bipartite graph respectively, and the repositioning task assignment process is quantified as a maximum flow problem. Kuhn-Munkres Algorithm and Edmond-Karp Algorithm are adopted to solve the above two mathematical problems to achieve the optimal task allocation policy. To further improve the dispatching performance, a new instant reward function balancing order income with trip satisfaction is designed, and a state-value function estimated by Back Propagation-Deep Neural Network is defined as a matching degree between each shared autonomous electric vehicle and each delivery task. The numerical results show that: (i) a reward function focusing on income and satisfaction can increase total revenue by 33.2%, (ii) the introduction of task allocation repositioning increases total revenue by 50.0%, (iii) a re-estimated state value function leads to a 2.8% increase in total revenue, (iv) the combination of charging and task repositioning can reduce user waiting time and significantly improve user satisfaction with the trip.
Even though shared autonomous vehicles (SAVs) have been studied extensively in recent years, little is known about how passengers who have used SAVs will adapt to the emerging technology. Additionally, an understanding of SAV technology's efficacy when integrated with the existing transportation systems is necessary for its wider adoption. This study aims to determine the factors influencing willingness to ride SAVs in the future using the data gathered from users and non users of a self-driving shuttle. We used structural equation modeling to evaluate the relationship between factors like owning private vehicles, the frequency of using SAVs, availability of other modes of transportation, SAV service attributes, and sociodemographic characteristics on individuals' willingness to ride SAVs in the future. Young Asian individuals with no access to private vehicles were a majority of SAV users. We discovered that the frequency of using SAV services positively influences people's willingness to use SAVs. This study provides crucial insights into user behavior following the integration of SAVs into current transportation systems to guide policymakers to develop policies enhancing SAV operations.
The United Arab Emirates is moving towards the use of renewable energy for many reasons, including the country’s high energy consumption, unstable oil prices, and increasing carbon dioxide emissions. The usage of electric vehicles can improve public health and reduce emissions that contribute to climate change. Thus, the usage of renewable energy resources to meet the demands of electric vehicles is the major challenge influencing the development of an optimal smart system that can satisfy energy requirements, enhance sustainability and reduce negative environmental impacts. The objective of this study was to examine different configurations of hybrid renewable energy systems for electric vehicle charging in Abu Dhabi city, UAE. A comprehensive study was conducted to investigate previous electric vehicle charging approaches and formulate the problem accordingly. Subsequently, methods for acquiring data with respect to the energy input and load profiles were determined, and a techno-economic analysis was performed using Hybrid Optimization of Multiple Energy Resources (HOMER) software. The results demonstrated that the optimal electric vehicle charging model comprising solar photovoltaics, wind turbines, batteries and a distribution grid was superior to the other studied configurations from the technical, economic and environmental perspectives. An optimal model could produce excess electricity of 22,006 kWh/year with an energy cost of 0.06743 USD/kWh. Furthermore, the proposed battery–grid–solar photovoltaics–wind turbine system had the highest renewable penetration and thus reduced carbon dioxide emissions by 384 tons/year. The results also indicated that the carbon credits associated with this system could result in savings of 8786.8 USD/year. This study provides new guidelines and identifies the best indicators for electric vehicle charging systems that will positively influence the trend in carbon dioxide emissions and achieve sustainable electricity generation. This study also provides a valid financial assessment for investors looking to encourage the use of renewable energy.
The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.
Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.
Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative towards reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study is performed with the aim of filling this research gap by presenting an impact analysis framework incorporating multisource datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces the annual charging costs and carbon emissions of a single bus route in this case study by 17.6% and 8.8% on average, respectively. The results suggest that the recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also impact the scheduling pattern of PESS considerably. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the investment budget is limited.
In this chapter argues that today, greenhouse gas (“GHG”) emissions continue to be the main perpetrator of climate change. The United States of America (the “USA”) is historically a significant contributor to global GHG emissions, and its participation in international agreements is crucial for the agreements’ success. However, the author observes that the domestic processes within the USA’s political sphere prevent policy for meaningful GHG emissions reductions from materialising. The problem is twofold. First, the USA’s executive processes are inconsistent. Second, the USA’s congressional processes are systemically deficient and experiences strong economic interests underpinning the vote of members. Cumulatively, these political processes prevent policy targeted at reducing GHG emissions from materialising. To advance a major climate change initiative, solidifying the USA’s stance to climate change, the author puts forth three suggestions: (1) an establishment of an independent climate change committee within the USA; (2) a government spearheaded campaigned targeted at changing public perception of climate change; and (3) the USA’s entry into a “climate club”.
Fleets of electric vehicles will likely shift electricity demand, and the effect of upstream charging emissions will come from generation sources that are dispatched in response. This study proposes a multi-stage charging and discharging problem to translate low-cost energy transactions into vehicle dispatch decisions. A day-ahead charging optimization problem minimizes electricity purchases and marginal emissions damages, with energy transactions becoming targets in an optimization-based dispatch strategy for an on-demand shared autonomous electric vehicle (SAEV) fleet. The framework was tested for Austin, Texas, using an agent-based simulator. Fleets can schedule charging to lower daily power costs (averaging 15.5% or 0.43/day/SAEV). Fleet managers can increase profits ($8 per SAEV per day) by adopting a multi-stage charging and discharging strategy that can serve more passengers per day than price-agnostic dispatch strategies.
In previous years, much research has been carried out to specify eco-innovations determinants. In most studies, eco-innovation was analyzed at a general level. From the perspective of climate change and the aim of achieving the Sustainable Development Goals and the European Green Deal objectives, the issue of innovations that replace the share of fossil fuel energy with renewable energy sources in the European Union appears to be particularly important. Therefore, this paper has two purposes. First, using a systematic literature review, it examines the key factors driving decisions taken by European Union enterprises to create innovations with environmental benefits. Second, it estimates the impact of these factors on introducing innovations that replace the share of fossil fuel energy with renewable energy sources. Specifically, using the logistic regression model, it analyses microdata from thirteen European Union Member States collected during the 2014 Community Innovation Survey. The effects show that six out of seven drivers under assessment positively affect the introduction of innovations that replace fossil fuel energy with renewable energy sources. These are (1) the high cost of energy, water, or materials, (2) environmental regulations or taxes expected in the future, (3) existing environmental taxes, charges, or fees, (4) government grants, subsidies, or other financial incentives for environmental innovations, (5) current or expected market demand for environmental innovations, and (6) voluntary actions or initiatives for good environmental practice within a sector.
In support of global decarbonization efforts, the adoption of electric vehicles is proceeding rapidly across transportation sectors, population groups, and regions. However, access to electric vehicle charging infrastructure remains sparse and inequitable, and technical, economic, and procedural challenges have encumbered its expansion. Charging-as-a-service, which can mitigate cost and effort risks of charging equipment ownership, adds a means of meeting many of these challenges. As charging-as-a-service has not been analyzed or addressed in the scholarly literature, we synthesize media, marketing, and analogous scholarly publications to categorize and describe the functions asserted by existing charging-as-a-service vendors. We demonstrate and quantify the benefits of many of these functions via four example business cases, sited at a detached residence, a multi-unit dwelling, a consumer-facing business, and a commercial fleet depot. Outcomes show that savings and revenues realized via charging-as-a-service can reduce charging costs, beneficially shape grid loads, and make charging installation economical where it otherwise would not be. Discussion highlights the need to deploy charging-as-a-service in cooperation with initiatives promoting societal benefits, including charging access equity.
Vehicle sharing, automation, and electrification are anticipated to move mobility and society towards low carbon and sustainability. Yet their positives and negatives to the society and natural environment largely depend on how people response to them which is poorly understood. A systematic review is performed in this study to discuss the modeling approaches that quantify travel behavior and demand implications of those technologies and summarize the pros-and-cons of the state-of-the-art. Evidences suggest that the negative rebound effect of the technologies lack mature discussion and require comprehensive quantification multidimensionally in technology adoption, trip generation, timing, travel distance, mode shift, and urban sprawl. Meanwhile, it is worth addressing the uncertainties of the net impact of the technologies resulted from data quality, population and regional heterogeneity, level of vehicle automation, temporal dynamics, behavior chain reactions, and societal issues like aging, fertility drop, and the pandemic. The findings of this review will enlighten the future endeavors of improving the estimation of mobility demand, energy, and carbon emissions implications of the innovative mobility technologies.
Integration of self-driving functions in electric vehicles is radically changing the transportation systems, and representing an opportunity for power utilities to develop innovative solutions for harnessing the spatio-temporal charging flexibility of autonomous electric vehicles (AEVs). This paper develops a multi-agent reinforcement learning model for intelligent real-time coordinated operation of interdependent autonomous electric ride-hailing system (AERS) and power distribution system (PDS), which enables the coordinated routing and charging of AEVs while ensuring the quality of service by meeting spatio-temporal passenger demand and regularly charging the batteries. The proposed model adopts a modified deep Q-network method with multi-agent rollout to determine the near-optimal solution for the coordinated operation problem that determines the routing and charging of numerous AEVs in real-time. The proposed model is tested on a 13-node transportation network, adopted from the Salt Lake City transportation system, and the IEEE 33-bus test power distribution system to showcase the efficiency of the proposed model in determining the real-time routing and charging decisions for AEVs while meeting the operational constraints of both AERS and PDS.
Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers' safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered. ARTICLE HISTORY
This paper proposes a dynamic ride-hailing sharing problem with multiple vehicle types and user classes. Ride-hailing vehicles (RHVs) can be classified into express ride-hailing vehicles (ERHVs) and premier ride-hailing vehicles (PRHVs) according to service levels. PRHVs can provide the high-quality ride-hailing service with upmarket vehicles and ERHVs provide the normal ride-hailing service with normal vehicles. The fare of PRHVs is higher. PRHVs can be temporarily used as ERHVs to serve the customers who order ERHVs with or without ride-sharing, which is referred to as the substitution of ERHVs with PRHVs. A lexicographic multi-objective function with three-level objectives is proposed to formulate the problem, in which the first-level objective is to maximize the profit of the platform, the second-level objective is to minimize the number of requests of customers who involve ordering ERHVs matched to PRHVs, and the third-level objective is to minimize the total driving distance of all RHVs. The dynamic problem is divided into a set of continuous and small ride-hailing sharing subproblems based on equal time intervals. Each subproblem is formulated as a mixed integer nonlinear program for matching RHVs to the requests collected in the last time interval or unmatched in previous time intervals and re-scheduling the vehicle routes. To solve the subproblems, a new solution method is proposed based on the modified artificial bee colony algorithm developed by Zhan et al. (2021). Numerical examples using real request data from Didi are given to explore the problem properties, and the results gain insights into the ride-hailing market. For example, the profit of the platform and the number of matched requests are higher when the substitution of ERHVs with PRHVs is allowed while the matching percentage of requests of customers who select a mixed choice is higher when there is no substitution. Different ratios of vehicle types and user classes influence the performance of the ride-hailing sharing market (e.g., the profit of the platform, the number of matched requests, matching percentage, etc.). The value of the fare discount multiplier for the passengers who successfully share RHVs with others can affect the number of shared requests, the number of matched requests, and platform profitability.
In this paper, we propose a closed queueing network model for performance analysis of electric vehicle sharing systems with a certain number of chargers in each neighborhood. Depending on the demand distribution, we devise algorithms to compute the optimal fleet size and number of chargers required to maximize profit while maintaining a certain quality of service. We show that the profit is concave with respect to the fleet size and the number of chargers at each charging point. If more chargers are installed within the city, we show that it can not only reduce the fleet size, but it also improves the availability of vehicles at all the points within a city. We further show through simulation that two slow chargers may outperform one fast charger when the variance of charging time becomes relatively large in comparison to the mean charging time.
The rapid development of intelligent and connected technologies is conducive to the efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy management strategies (EMSs) with optimized, intelligent, and connected functions have not been directly applied to such vehicles because existing technical conditions cannot meet the theoretical requirements of complex EMSs. Therefore, based on the mapping relationship between the information decision-making ability and the energy management effect, this study is the first to propose four development stages of HEV energy management practical application as follows: energy management based on instantaneous driving cycles (Stage 1 or S1); energy management based on forward driving cycle prediction (Stage 2 or S2); energy management based on global driving cycle prediction (Stage 3 or S3); and energy management based on autonomous velocity planning (Stage 4 or S4). The key technologies of each development stage are not independent, i.e., they complement each other in the process of practical application development. Furthermore, realizing energy management practical applications not only requires novel algorithm models but also involves several challenges such as acquiring and processing multi-source information, predicting the vehicle power demand in the spatial–temporal domain during travel, and determining the vehicle control characteristics and ability. Finally, according to the development goals of energy management, this study proposes an implementation framework for HEV energy management in higher development stages, namely cooperative vehicle–edge–cloud for intelligent energy management, i.e., CVEC- IEM, which executes information decision tasks on different computing platforms and realizes interconnection and interaction to provide development directions and goals for the efficient utilization of energy and successful deployment of HEV practical applications.
We study a new variant of the well-studied vehicle routing problem with time windows (VRPTW), called the fragility-constrained VRPTW, which assumes that (1) the capacity of a vehicle is organized in multiple identical stacks; (2) all items picked up at a customer are either “fragile” or not; (3) no nonfragile items can be put on top of a fragile item (the fragility constraint); and (4) no en route load rearrangement is possible. We first characterize the feasibility of a route with respect to this fragility constraint. Then, to solve this new problem, we develop an exact branch-price-and-cut (BPC) algorithm that includes a labeling algorithm exploiting this feasibility characterization to efficiently generate feasible routes. This algorithm is benchmarked against another BPC algorithm that deals with the fragility constraint in the column generation master problem through infeasible path cuts. Our computational results show that the former BPC algorithm clearly outperforms the latter in terms of computational time and that the fragility constraint has a greater impact on the optimal solution cost (compared with that of the VRPTW) when vehicle capacity decreases, stack height increases, and for a more balanced mix of customers with fragile and nonfragile items.
Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN 2015-06289 and RGPIN 2022-03916].
Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1168 .
Shared autonomous electric vehicles (SAEVs) are expected to become a popular choice for urban transportation in the future. A solution to a vehicle routing problem that considers congestion and energy consumption of SAEVs is proposed to assist green transportation under poor traffic conditions. In addition to routing the fleet of SAEVs to serve customers, the proposed method also determines the vehicle speed in each arc and the departure time at each node by minimizing the cost considering the travel distance, energy consumption, and travel time. In the time-dependent vehicle routing problem with departure time and speed optimization for SAEV service (SAEV-TD-VRP-DSO), the electric vehicles (EVs) exchange batteries at battery swapping stations (BSSs), and the corresponding vehicle routing method also includes recharge scheduling. We develop a mixed-integer linear model (MILP) to formulate the SAEV-TD-VRP-DSO and show that the state-of-the-art commercial optimization solver (CPLEX) can only solve a few instances (no more than 8 requests). Thus, an adaptive large neighborhood search (ALNS) algorithm is proposed to find near-optimal solutions for larger-size instances. The removal and insertion operators of ALNS help optimize the node sequences. A departure time and speed optimization procedure (DSOP) is employed to optimize the speed and departure time in each arc. The good performance of the proposed algorithm is demonstrated using instance sets. The optimization results of a case study in the Yanta Administrative District in Xi'an, China, show that speed and departure time optimization result in 24.99% cost savings. The sensitivity analysis results provide the SAEV operators with alternative cost-saving solutions. Empty vehicles traveling at a slower speed can reduce energy cost. The total cost of operating vehicles with passengers can be reduced by avoiding congestion and utilizing larger capacity batteries (32 kWh or more) and a lower minimum battery level (0.2-0.3).
Autonomous vehicles that travel without considering the lane marks and utilizing all road width have an opportunity to maximize the use of vehicles’ performance. By taking advantage of the entire width of curvy roads and the cooperative behavior of connected autonomous vehicles, new options for path planning can be implemented while utilizing the existing infrastructure. The proposed cooperative controller uses a nonlinear model predictive control (NMPC) approach for dozens of autonomous vehicles without considering lane marks. This controller maximizes vehicles’ progress on the road with minimal control efforts while complying with design constraints imposed by road geometry, distances between vehicles, and vehicle dynamics. The controller is tested in two simulation case studies. The first examines the performance under two different plant (reality) models. The second considers dozens of vehicles and compares the traffic flow characteristics between the lane-free concept and the lane-based concept within different vehicle densities. The simulation results show that the lane-free concept can improve the traffic flow performance compared with the lane-based road concept, i.e. reducing passengers’ time on the road, reducing energy consumption, and increasing road capacity. These improvements depend on the road density and track layout. In order to demonstrate the proposed controller, three laboratory experiments with several homogeneous and heterogeneous robots were conducted.
In many customer service operations, workers visit customer locations to perform on-site service tasks. Each worker drives a vehicle and so the task allocation problem is often solved as a vehicle routing problem. However, unlike delivery services, the workers spend the majority of their time working on the service tasks leaving the vehicles idle. This creates a possibility of sharing vehicles among the workers to save vehicles used and reduce the total carbon emissions. This paper studies this new problem of vehicle sharing and task allocation. Given the team of workers and a set of customer tasks with their locations and time requirements, decisions need to be made on the scheduling of workers to tasks, workers sharing each vehicle and the routing of the vehicles. We first formulate the problem as an integer programming model. To solve larger problem instances, a three-phase heuristic algorithm is then developed. Computational experiments are carried out to demonstrate the benefits of sharing vehicles. The effects of problem parameters on the solution are also investigated.
With the rise of digital sustainable business models in the Autonomous Vehicles (AV) industry, the traditional automakers are undergoing a major restructuring in their key performance areas and associated supply chains processes. Focusing on an innovative AV design (AD) concept, this paper investigates how Artificial Intelligence (AI) and Blockchain-based Smart Contracts can enhance sustainable supply chain operations. A novel design element, Margin Indicator (MI), is developed to obtain reliable predictive analytics results from the mainstream machine learning algorithms. The proposed approach supports a robust control of costs and energy, while maintaining a high level of transparency in managing decentralized AV supply chain processes, monetary impacts, and environmental sustainability. Testing the developed concept through a preliminary case study, we observed a reduction in energy wastage and hidden financial transactions by 12.48% and 11.58%, respectively. Supported by the rapid advancement in the blockchain and AI technologies, the developed framework is expected to improve product traceability, transaction transparency, and sustainable economic growth for the AV supply chains.
The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) is an important variant of the classical Capacitated Vehicle Routing Problem (CVRP) that aims to find routes that minimize the total traveling cost of a heterogeneous fleet of vehicles. This problem is of great interest given its importance in many industrial and commercial applications. In this paper, we present an Adaptive Iterated Local Search (AILS) heuristic for the HFVRP. AILS is a local search-based meta-heuristic that achieved good results for the CVRP. The main characteristic of AILS is its adaptive behavior that allows the adjustment of the diversity control of the solutions explored during the search process. The proposed AILS for the HFVRP was tested on benchmark instances containing up to 360 customers. The results of computational experiments indicate that AILS outperformed state-of-the-art metaheuristics on 87% of the instances.
Transportation agencies and researchers are optimistic about the potential use of data collected from connected vehicles (CVs) for a variety of traffic and transportation applications. However, the literature lacks the evaluation of data sharing intention of the public for CV applications and its relationship with CV acceptance. This study investigated this gap by conducting a questionnaire survey of 2400 US adults. The results showed that the intention to share CV data depends upon the use of data but not the type of data. The possible uses of CV data were found to be grouped under four categories: driver information, congestion assessment and reduction, and pavement and infrastructure assessment and improvement (ICP); enforcement of traffic rules and fees based on usage (EF); roadside assistance and crash investigation (RC); and research purposes (RP). The data sharing intention for these four data uses vary, though with some commonality, which reflects the overall data sharing intention in CV technology (CVT). In addition, it was found that data privacy and security issues of CVT lower the data sharing intention and CV acceptance. Thus, a number of ways to improve CV acceptance by minimizing the data issues of CVT are discussed. Significant differences in perception of data privacy and security, data sharing intention, and CV acceptance were observed for individuals of different socio-economic and driving-related characteristics.
The transportation sector is a major source of greenhouse gas (GHG) emissions. Shared autonomous electric vehicles (SAEVs) have the potential to mitigate emissions, but the effect can be highly dependent on the growth and operation of the SAEV fleet as well as its interaction with the evolving power system. In this study, we simulate travel and charging behaviors of SAEVs based on empirical data of ride-hail service operations, and integrate SAEV charging with the Grid Optimized Operation Dispatch (GOOD) model, taking into account the expansion of renewable generation and charger capacity over time. Emissions from SAEVs are compared across different market adoption levels, occupancy rates, and charging strategies. We find that under the Californian power grid, SAEVs are generally more than 5 times less carbon intensive than modern day ICVs on a per mile basis. The extent of aligning charging schedule with renewable generation is an essential determinant of the economic and emission impact from an SAEV fleet. At higher levels of renewable penetration, synergizing SAEV charging with grid operation can be the most impactful means to reduce emissions from an SAEV fleet, generating up to 95% less emissions than other charging strategies. We also examine the introduction of a carbon tax and find that it can further amplify the advantage of smart charging by approximately 1.5 times in the cost-effectiveness of emission mitigation.
Prediction of lane changes (LCs) provides critical information to enhance traffic safety and efficiency in a connected and automated driving environment. It is essential to precisely detect LCs from driving data to lay the groundwork for LC prediction. This study aims to develop LC detection and prediction models using large-scale real-world data collected by connected vehicles (CVs). At first, an autoencoder was used to detect LCs, and proved to be more precise and robust than conventional methods. Next, a transformer-based LC prediction model was developed, which concentrated computation power on key information via an attention mechanism. It outperformed the baseline models in terms of accuracy and computational efficiency. The prediction horizon was also analyzed and LC could be accurately predicted up to two seconds in advance. At last, the transformer model was implemented for real-time prediction and demonstrated a great potential for practical applications.
Electrification of vehicles is strengthening the interaction between power systems and transportation systems, formulating the coupled transportation power systems (CTPSs). A novel optimal traffic power flow (OTPF) problem is proposed to analyse the spatial and temporal congestion propagation on CTPSs, under congested roads, transmission lines and charging stations. The traffic flow is depicted by the spatial and temporal distribution of electric vehicles (EVs) on roads and charging stations, connected by multi-layer time-space networks (TSNs). This distribution is the solution to a mesoscale traffic assignment problem (TAP) of EV fleets on TSNs, where the charging, discharging, routing and origin-destination pairing can be optimized simultaneously. The power flow is captured by the optima of dynamic optimal power flow problems with security constraints. An extended alternating direction multiplier method algorithm with the convex-concave procedure is proposed to solve OTPFs. Results verify the effectiveness of the proposed scheme for congestion management on CTPSs.
As the technology of autonomous vehicle develops, online hailing autonomous taxi system is regarded as one of the most popular public transportation services in the future. Studies related to demand forecasting, ride matching, path planning, relocation, and pricing strategy for shared online hailing and autonomous taxi services have emerged in recent years. In this study, we conducted a survey based on 140 representative literatures from 1995 to 2021 to understand the state-of-the-art of the key problems of operating autonomous taxi service. First, a comprehensive review of the components of the shared autonomous taxi modelling is presented. Then, how the emerging technologies such as internet of vehicles, big data, cloud and edge computing, and blockchain can be used to enhance the autonomous taxi service is discussed. Last, the current research challenges and the concern or hurdle in public’s adoption of autonomous taxi services are identified.
The accelerating emergence of vehicle automation and the anticipation of the advent of shared mobility through fully autonomous vehicles indicate the beginning of a new era of mobility which has the potential to reshape the future of transport in urban areas. In light of such developments, it is important that communities prepare to adapt to the changes they might entail. Therefore, in this paper, traffic flow theory, simulation-based dynamic traffic assignment, and a computer experiment using PTV Visum software were employed to study the impact of different market penetration rates of shared autonomous vehicles (SAVs) on a city-size traffic system. The city of Budapest during morning peak period was chosen as a case study, and a simulation model was created by incorporating SAV elements and their interrelationships into the existing traffic model of the case study city; three alternative future penetration rates were examined in relation to five key performance indicators (KPIs). The simulation results indicated that the implementation of the SAV system has a positive effect on traffic performance. Based on the relationships between the modeled SAV demand shares and the network’s KPIs in the designed scenarios, the overall network performance showed improvement along with an increase in the SAV demand share.