Search Results (37)

Electric Level 4 connected automated vehicles (CAVs) are now allowed to demonstrate their automation capability in shared mobility robotaxi and microtransit services in geofenced areas in several cities around the world. Private and public sector stake-holders need predictions of their adoption without regulatory constraints for personal mobility and use in shared mobility services. In anticipation of the future presence of CAVs in transportation vehicle fleets, governments are planning necessary regulatory and infrastructure changes. Accompanying this need for forecasts is the acknowledgement that CAV adoption decisions must be made under uncertain states of technology and infrastructure readiness. This paper presents a Bayesian predictive modelling framework for electric Level 4 CAV adoption in the 2030–2035 application context. The inputs to the Bayesian model are obtained from effectiveness estimates of CAV applications that are processed with the Monte Carlo method to account for uncertainties in these estimates. Scenarios of CAV adoption in the 2030–2035 period are analyzed using the Bayesian model, including the quantification of the value of new information obtainable from demonstration studies intended to reduce uncertainties in technology and infrastructure readiness. The results show that in the 2030–2035 application context, the CAVs are likely to be adopted, provided that the trajectory of progress in technology and infrastructure readiness continues, and potential adopters are offered opportunities to learn about Level 4 CAV technological capabilities in a real life service environment. The threshold level of the probability of adoption enhances significantly with high-reliability demonstration results that can reduce uncertainties in adoption decisions. The findings of this research can be used by private and public sector interest groups. Full article

To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through EMS_MPC-P, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with SoC₀ = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by EMS_MPC-P compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development. Full article

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty. Full article

The increasing volume of personal motorized vehicles (PMVs) in cities has become a serious issue leading to congestion, noise, air pollution and high land consumption. To ensure the sustainability of urban transportation, it is imperative to transition the current transportation paradigm toward a more sustainable state. Transitions within socio-technical systems often arise from niche innovation. Therefore, this paper pursues the technical optimization of such a niche innovation by applying a technical sustainability perspective on an innovative mobility and logistics concept within a case study. This case study is based on a centrally managed connected, automated, shared and electric (CASE) vehicle fleet which might replace PMV use in urban city centers of the future. The key technical system components of the envisioned mobility and logistics concept are analyzed and optimized with regard to economic, ecological and social sustainability dimensions to maximize the overall sustainability of the ecosystem. Specifically, this paper identifies key challenges and proposes possible solutions across the vehicle components as well as the orchestration of the vehicles’ operations within the envisioned mobility and logistics concept. Thereby, the case study gives an example of how different engineering disciplines can contribute to different sustainability dimensions, highlighting the interdependences. Finally, the discussion concludes that the early integration of sustainability considerations in the technical optimization efforts of innovative transportation systems can provide an important building block for the transition of the current transportation paradigm to a more sustainable state. Full article

Novel vehicle concepts are needed to meet the requirements of resource-conserving and efficient mobility in the future, especially in urban areas. In the automated, driverless electric vehicle concept U-Shift, a new form of mobility is created by separating a vehicle into a drive module and a transport capsule. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and is therefore used to transport both people and goods. The wide range of possible capsules poses major challenges for the development of the Driveboard and the chassis in particular. A lifting actuator integrated into the chassis concept enables levelling and, thus, the raising and lowering of the Driveboard and the capsules to ground level. This means that no additional lifting devices are required for changing the capsules or for lowering them to the ground, e.g., for loading and unloading the capsules. To realise this mechanism simply and efficiently, a fully electromechanical actuator is designed and constructed. The actuator consists primarily of a profile rail guide, a steel cable winch, an electric motor, a housing that connects the subsystems and a locking mechanism. The electric motor is used to lift the vehicle and regulate the weight force-driven lowering of the vehicle. This paper describes the design of the actuator and shows the dimensioning of all main components according to the boundary conditions. Finally, the prototype model of the realised concept is presented. Full article

We present an innovative cooperative driving strategy known as Dynamic Resequencing and Platooning (DRP) designed to ensure the safe and efficient traversal of Connected and Automated Vehicles (CAVs) through signal-free intersections. By employing a Resequencing and Platooning Algorithm (RPA) grounded in state transition networks and CAV platooning, the optimal crossing sequence for CAVs is ascertained within a finite time. Through the utilization of a decentralized energy-optimal control framework, optimal trajectories are devised for CAVs, thereby facilitating optimal coordination among them. Simulation results underscore the substantial performance benefits of the DRP strategy compared to traffic light, First-In-First-Out (FIFO), and Local Dynamic Resequencing (LDR) strategies, with notable reductions observed in both travel delay and fuel consumption. Full article

With the electrification and connectivity of vehicles in transportation, traditional vehicles with single drivetrains are being replaced by pure electric or hybrid electric vehicles (HEVs). This evolution has given rise to diverse electromechanical coupling drivetrains. There is a pressing need to update simulation software to assess the economic performance of vehicles in various environments, and promote sustainable development and energy conservation. This paper presents a unified framework for the construction and automated operation of large-scale automated vehicle simulations with multiple drivetrain types, facilitating synchronous information exchange among vehicles. Central to the framework is the development of a plug-and-play vehicle model based on a modular component design, facilitating the rapid assembly of vehicles with varied drivetrain configurations and standardizing simulation file management. Additionally, a standardized simulation process construction is established to accommodate the automated operation of simulations. Furthermore, a data scheduling method among vehicles is introduced to achieve multi-vehicle interconnection simulation. Finally, the effectiveness of the framework is demonstrated through a case study involving queue-following control for HEVs. This framework aims to provide a comprehensive solution for quickly establishing automated simulation environments for multi-vehicle interaction, enhancing model reusability and scalability. Full article

In the near future, the incorporation of shared electric automated and connected mobility (SEACM) technologies will significantly transform the landscape of transportation into a sustainable and efficient mobility ecosystem. However, these technological advances raise complex scientific challenges. Problems related to safety, energy efficiency, and route optimization in dynamic urban environments are major issues to be resolved. In addition, the unavailability of realistic and various data of such systems makes their deployment, design, and performance evaluation very challenging. As a result, to avoid the constraints of real data collection, using generated artificial datasets is crucial for simulation to test and validate algorithms and models under various scenarios. These artificial datasets are used for the training of ML (Machine Learning) models, allowing researchers and operators to evaluate performance and predict system behavior under various conditions. To generate artificial datasets, numerous elements such as user behavior, vehicle dynamics, charging infrastructure, and environmental conditions must be considered. In all these elements, symmetry is a core concern; in some cases, asymmetry is more realistic; however, in others, reaching/maintaining as much symmetry as possible is a core requirement. This review paper provides a comprehensive literature survey of the most relevant techniques generating synthetic datasets in the literature, with a particular focus on the shared electric automated and connected mobility context. Furthermore, this paper also investigates central issues of these complex and dynamic systems regarding how artificial datasets could be used in the training of ML models to address the repositioning problem. Hereby, symmetry is undoubtedly a crucial consideration for ML models. In the case of datasets, it is imperative that they accurately emulate the symmetry or asymmetry observed in real-world scenarios to be effectively represented by the generated datasets. Then, this paper investigates the current challenges and limitations of synthetic datasets, such as the reliability of simulations to the real world, and the validation of generative models. Additionally, it explores how ML-based algorithms can be used to optimize vehicle routing, charging infrastructure usage, demand forecasting, and other important operational elements. In conclusion, this paper outlines a series of interesting new research avenues concerning the generation of artificial data for SEACM systems. Full article

With the continuous advancements in electrification, connectivity, and intelligence in the automotive industry, the mixed traffic of vehicles with different levels of driving automation is changing the carbon emission characteristics in the impact areas of off-ramps on highways. Considering the insufficient research on the carbon emission characteristics of heterogeneous traffic flow in the downstream influence areas of highway off-ramps, this study applied a scenario analysis method. Furthermore, considering factors such as vehicle composition, road control, and platoon management, it establishes and calibrates measurement models for carbon emissions from conventional vehicles, intelligent vehicles, the platoon driving of electric vehicles, and the mixed platoon driving of conventional vehicles and electric vehicles. This study also provides a simulation scenario for a three-lane highway off-ramp based on the actual conditions of the Xi’an Ring Expressway. Finally, by applying the constructed carbon emission calculation models for heterogeneous traffic flow in the intelligent vehicle mixed traffic scenario, a quantitative analysis was conducted to assess the impacts of the intelligent vehicle infiltration rate, off-ramp vehicle proportion, smart-vehicle-dedicated lanes, and platoon driving on carbon emissions in the downstream influence area of off-ramps. The results revealed the impact of intelligent vehicle integration and platoon driving on carbon emission characteristics in the downstream influence areas of highway off-ramps. Full article

Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types. Full article

Electric vehicle battery systems are made up of a variety of different materials, each battery system contains hundreds of batteries. There are many parts that need to be connected in the battery system, and welding is often the most effective and reliable connection method. Laser welding has the advantages of non-contact, high energy density, accurate heat input control, and easy automation, which is considered to be the ideal choice for electric vehicle battery manufacturing. However, the metal materials used for the electrodes of the battery and the connectors used to connect the battery are not the same, so the different materials need to be welded together effectively. Welding different materials together is associated with various difficulties and challenges, as more intermetallic compounds are formed, some of which can affect the microstructure, electrical and thermal properties of the joint. Because the common material of the battery housing is steel and aluminum and other refractory metals, it will also face various problems. In this paper reviews, the challenges and the latest progress of laser welding between different materials of battery busbar and battery pole and between the same materials of battery housing are reviewed. The microstructure, metallographic defects and mechanical properties of the joint are discussed. Full article

The Internet of Things (IoT) is a network of interconnected physical devices, vehicles, and buildings that are embedded with sensors, software, and network connectivity, enabling them to collect and exchange data. This exchange of data between the physical and digital worlds allows for a wide range of applications, from smart homes and cities to industrial automation and healthcare. However, a key challenge faced by IoT nodes is the limited availability of energy to support their operations. Typically, these nodes can only function for a few days based on their duty cycle. This paper introduces a solution that aims to ensure the sustainability of IoT applications by addressing this energy challenge. Thus, we develop a design of a hybrid sustainable energy system designed specifically for IoT nodes, using solar photovoltaic (PV) and wind turbines (WT) chosen for their multiple benefits and complementarity. The system uses the single-ended primary-inductance converter (SEPIC) and is controlled using a hybrid approach, combining Harris Hawks Optimization and Particle Swarm Optimization (HHHOPSO). Each SEPIC converter boost the electrical energy generated to attain the required voltage level when charging the battery. The proposed methodology is implemented in MATLAB/Simulink and its performance is measured using appropriate metrics. In terms of efficiency and average power, the results show that the suggested method outperforms previous strategies. Our system powers also many sensor nodes, leading to a high level of sustainability and lowering the carbon footprint associated with traditional energy sources. Full article

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value. Full article

The development of vehicle electrification and intelligent network technologies has led to a new type of mixed and power-heterogeneous traffic flow, comprised of regular vehicles (RVs) and connected and automated vehicles (CAVs), fuel vehicles (FVs) and electric vehicles (EVs). To reduce the energy consumption of mixed and power-heterogeneous traffic flow operating at a signalized intersection, the Ecological Control Unit–Cooperative Adaptive Control (ECU-CACC) is proposed in this paper. The vehicle platoon is divided into units which are named minimum ecological control units (min-ECUs). A bi-level control framework is designed to improve traffic efficiency and reduce energy consumption. The lower-level aims to plan the best ecological trajectory for every min-ECU, and the upper-level optimizes the passing strategies for efficiency through speed coordination. Scenario numerical experiments are performed to verify the effectiveness of the bi-level optimal control model and analyze the energy-saving effect of ECU-CACC under different vehicle mixing situations. The results from the experiment prove the excellent energy-saving potential of the proposed ECU-CACC, which helps the min-ECUs save about 10–20% energy consumption compared with a regular pattern. Full article

For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research. Full article