Autonomous Electric Vehicles Research Articles

A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet

The convergence of electrification and automated driving will introduce opportunities to improve the operation and energy-efficiency of transportation systems. This paper discusses the challenges of dispatching autonomous electric vehicles (AEVs) in a ride-hailing fleet and their interactions with charging infrastructure. An integrated decision-making framework for dispatching and charging has been proposed using system optimization approaches. An agent-based platform has been developed for simulating and testing the proposed methods. A case study using New York City taxi data has been performed with different fleet sizes, dispatching strategies, and charging networks. Advantages of optimization-based approaches for AEV fleet management have been studied and demonstrated, for example, for a fleet of 1,750 AEVs to meet 100,000 daily requests, optimization-based centralized fleet management would result in 14% more ride requests satisfied and 43% fewer zero-occupancy miles traveled than if AEVs make independent decisions based on heuristic strategy. Benefits on reducing fleet size and charging downtime from optimization approaches are also comprehensively illustrated.

Model for Crowdsourced Parcel Delivery Embedded into Mobility as a Service Based on Autonomous Electric Vehicles

Faced promising development of autonomous vehicles, the concept Mobility as a Service embraces AVs. The autonomous delivery has also been arising and tested for last-mile solution. Accordingly, passenger transportation and delivery service are to be jointly coordinated in the future MaaS. The research niche is summarized as how to embed crowdsourced parcel delivery into MaaS based on AVs. Electricity powered vehicles are to be applied considering energy consumption and air emission. Research questions are identified as what this novel service is and how it works, especially focusing on task coordination and the related information management. The system engineering process-oriented approaches, matching theory, and numerical method have been applied. The service concept has been elaborated, which contains information system architecture model and functional model. The matching condition of this service is summarized. As the embedded delivery can partially share the demand of traditional delivery service, the calculation scenarios of energy savings and emission decrease per parcel delivery are presented. The results facilitate service planning and development.

Autonomous vehicle adoption: use phase environmental implications

This paper investigates the environmental trade offs resulting from the adoption of autonomous vehicles (AVs) as a function of modal shifts and use phase. An empirical approach is taken to formulate a mode choice model informed from a stated preference (SP) survey conducted in Madison, Wisconsin. A life cycle analysis based on well-to-wheel model is then conducted to quantify the use phase environmental impacts across different categories. The mode choice analysis reveals the potential users of AVs and its attractiveness as a mode of transportation, ultimately competing with traditional modes available. This translates into modal shifts that are shown to result in an expected increase in environmental impacts across all studied categories: energy consumption (5.93%), greenhouse gas emissions (5.72%), particulate matter (6.80%), sulfur (6.85%) and nitrogen oxides (5.70%). The adoption of electric AVs (E-AVs) is then analyzed as an offsetting strategy to combat the increase in environmental impacts. The analysis reveals that E-AVs are capable of offsetting the foreseen impacts, yet their effectiveness is dependent on the electricity generation mix and adoption rate.

Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach

Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.

BROMINATED ISOBUTYLENE-CO-PARAMETHYLSTYRENE WITH SUPERIOR IMPERMEABILITY FOR TIRE INNERLINER APPLICATIONS

ABSTRACT Isobutylene elastomers are of great commercial importance in tire applications because of their notable low gas permeability properties, owing to their efficient molecular packing. Brominated isobutylene-co-para-methylstyrene (BIMSM) elastomers are a special class of isobutylene elastomers synthesized by random cationic polymerization of isobutylene and para-methylstyrene (pMS), followed by a selective bromination of the methyl group of the pMS units. BIMSM elastomer, commercially known as Exxpro™ specialty elastomer, exhibits superior heat resistance and aging properties and is much more resistant to chemicals and ozone than butyl- or halobutyl polymers because of its fully saturated backbone structure. The permeability properties of BIMSM elastomers can be tuned by the level of pMS comonomer present in the polymer chain. The pMS comonomer increases the glass transition temperature of the copolymer, and polymers with very low gas permeability needed for demanding tire air retention applications can be produced by suitably selecting the pMS content. A single type of benzylic bromide, but with a versatile functional group, allows for precise control of vulcanization chemistry, potential for other chemical transformations to achieve other reactive groups, and grafting reactions. We present the new material developments to meet the growing market demands and requirements for low maintenance, low inflation pressure loss rate tires and tires for connected autonomous shared electric vehicles.

An Artificial-Neural-Network-Based Model for Real-Time Dispatching of Electric Autonomous Taxis

This paper presents a real-time dispatching model for electric autonomous vehicle (EAV) taxis that combines mathematical programming and machine learning. The EAV taxi dispatching problem is formulated and solved as an integer linear program that maximizes the total reward for serving customers. The optimal dispatch solutions are generated by simulating electric autonomous taxis that are dispatched by the optimization model. The artificial-neural-network-(ANN)-based model was trained using the optimization model’s dispatch solutions to learn the optimal dispatch strategies. Although the dispatch decisions made by the ANN-based model are not optimal, the system’s performance is very close to the optimization dispatch model in terms of customer service and taxis’ operational efficiency. In addition, the ANN-based dispatch model runs much faster. By comparing with current taxis, it was found that the EAV taxis dispatched by our ANN-based model can improve operational efficiency by reducing empty travel distance. EAV taxis can also reduce fleet size by 15% while maintaining a comparable level of service with the current taxi fleet.

Analysis and Design of Drivetrain Control for the AEV With Network-Induced Compounding-Construction Loop Delays

This paper presents a robust predictive control scheme with a graphic-based delay boundary analysis to mitigate the electric vehicle (EV) drivetrain oscillating issues, subject to the multi-channel compounding-construction loop delays. The application of Controller Area Network (CAN) in autonomous electric vehicles (AEVs) inevitably induces multi-channel compounding-construction loop delays into the control loop. The in-deep analyzing and understanding of the network-induced loop delays is critical for the electrified powertrain and its motion control. This study aims to guarantee, explicitly, the motion stability of AEV drivetrains as safe-critical and hard real-time applications. Firstly, a graphic-based constructional representation approach is presented for modeling of the compounding-construction loop delays. To resolve the upper bound of the compounding-construction loop delays further, a mathematic expression of delay boundary-envelopment analysis is derived. Secondly, based on the reasonable upper bound, Taylor series expansion is applied to make the system model with nonlinear uncertainties caused by the network-induced loop delays represent in the form of the convex polytope. Then, with the convex polytope of the drivetrain system model, a robust model predictive control (RMPC) approach is developed to enhance the system robustness against the unexpected network-induced delays. To attenuate the online calculation burden, a scheme combining off-line design and on-line synthesis is provided. Finally, the satisfactory motion control performance in both the co-simulations (Matlab&Carsim) and bench experimental tests can strongly verify the effectiveness of the proposed approaches.

The Location Routing Problem of the Car-Sharing System with Autonomous Electric Vehicles

Car-sharing, electrification, and autonomous driving are greatly revolutionizing the future of the transportation system. This study proposes a location routing problem for the car-sharing system with autonomous electric vehicles to determine optimal station location and vehicle routing, where each station is both a depot and a charging station. A mathematical model is formulated and then extended to three variants, while simultaneously considering different recharging and service options. The proposed mixed-integer nonlinear models are separately solved by general algebraic modeling system (GAMS) and genetic algorithm (GA), and the efficiency of the GA is demonstrated. The comparative experimental results of instances are presented, and the benefits of allowing partial recharge are obtained. More significant savings in cost can be achieved if partial service is simultaneously allowed. Furthermore, the trade-off between the operator’s interests and the interests of users, as well as the operator’s immediate profits and future profits, are explored through sensitivity analysis.

Vehicle Localization Using Wheel Speed Sensor (WSS) and Inertial Measurement Unit (IMU)


 
 
 The advent of autonomous driving has led researchers toward a whole new technological age where vehicle positioning and localization system form the back bone of an autonomous electric vehicle. However, localization becomes poor as a vehicle enters GPS-denied areas due to multi path errors. Autonomous vehicle, in addition, needs to be localized from time to time and be guided on the right path along its destination. The purpose of this study is to overcome the problem of adopting an alternative method by using the vehicle’s Wheel Speed Sensor (WSS) for localization. WSS as an auxiliary sensor is attached to the vehicle’s wheel to track its position upon considering its travelling speed in a period of time. This is done in such a way that the existence of obscured portion along the guideway will be neglected. The data obtained from WSS are combined with yaw rate from an Inertial Measurement Unit (IMU) through Kinematic Modelling algorithm and then be converted to get the local position coordinates. In order to analyse whether the yaw rate produced by IMU is acceptable or not, comparison with simulation is needed. A Bicycle Model is used to generate simulated yaw rate from the steering angle of the vehicle and Kalman Filter estimates the simulated yaw rate to be close with the raw yaw rate. Therefore, this will clarify that the yaw rate obtained from IMU is acceptable and that true localization path is generated.
 
 

Sustainable Mobility Driven Prioritization of New Vehicle Technologies, Based on a New Decision-Aiding Methodology

In an era of environmental and socio-economic crisis, sustainable transport planning is vital as ever, especially given that the transport sector is responsible for the greatest part of total air pollution and greenhouse gas emissions. New vehicle technologies, such as autonomous and electric vehicles, emerge as promising alternatives, creating, however, both opportunities and challenges and raising questions relating to their performance. Can these new vehicle technologies really perform better than conventional ones in terms of sustainable mobility? Which one of them constitutes the optimum solution? How does each alternative perform with regard to different evaluation criteria, such as air pollution or road safety? In order to answer such questions, and to select the optimum solution, a comparison between autonomous, electric, autonomous electric and conventional vehicles is executed, based on a set of social, economic and environmental criteria. For this purpose, a new decision-aiding methodology, allowing for a holistic evaluation of the alternatives through a comprehensive literature review and experts’ participation, is applied. It is mainly based on the combined application of two hybrid multi-criteria analysis models, creating a more solid background towards optimum decision-making, thus constituting an important decision support tool for project appraisal and funding within the framework of sustainability in any sector.

A Comprehensive Emissions Model Combining Autonomous Vehicles with Park and Ride and Electric Vehicle Transportation Policies

Several transport policies reduce pollution levels caused by private vehicles by introducing autonomous or electric vehicles and encouraging mode shift from private to public transport through park and ride (P&R) facilities. However, combining the policies of introducing autonomous vehicles with the implementation of electric vehicles and using the P&R system could amplify the decrease of transport sector emissions. The COPERT software has been used to calculate the emissions. This article aims to study these policies and determine which combinations can better reduce pollution. The result shows that each combination of autonomous vehicles reduces pollution to different degrees. In conclusion, the shift to more sustainable transport modes through autonomous electric vehicles and P&R systems reduces pollution in the urban environment to a higher percentage. In contrast, the combination of autonomous vehicles has lower emission reduction but is easier to implement with the currently available infrastructure.

Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments

In this study, the hardware and software design and implementation of an autonomous electric vehicle are addressed. We aimed to develop an autonomous electric vehicle for path tracking. Control and navigation algorithms are developed and implemented. The vehicle is able to perform path-tracking maneuvers under environments in which the positioning signals from the Global Navigation Satellite System (GNSS) are not accessible. The proposed control approach uses a modified constrained input-output nonlinear model predictive controller (NMPC) for path-tracking control. The proposed localization algorithm used in this study guarantees almost accurate position estimation under GNSS-denied environments. We discuss the procedure for designing the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system’s full state is estimated by fusing visual inertial odometry (VIO) measurements with wheel odometry data using an extended Kalman filter (EKF). Simulation and real-time experiments are performed. The obtained results demonstrate that our designed autonomous vehicle is capable of performing path-tracking maneuvers without using Global Navigation Satellite System positioning data. The designed vehicle can perform challenging path-tracking maneuvers with a speed of up to 1 m per second.

A Synergy of Innovative Technologies towards Implementing an Autonomous DIY Electric Vehicle for Harvester-Assisting Purposes

The boom in the electronics industry has made a variety of credit card-sized computer systems and plenty of accompanying sensing and acting elements widely available, at continuously diminishing cost and size levels. The benefits of this situation for agriculture are not left unexploited and thus, more accurate, efficient and environmentally-friendly systems are making the scene. In this context, there is an increasing interest in affordable, small-scale agricultural robots. A key factor for success is the balanced selection of innovative hardware and software components, among the plethora being available. This work describes exactly the steps for designing, implementing and testing a small autonomous electric vehicle, able to follow the farmer during the harvesting activities and to carry the fruits/vegetables from the plant area to the truck location. Quite inexpensive GPS and IMU units, assisted by hardware-accelerated machine vision, speech recognition and networking techniques can assure the fluent operation of a prototype vehicle exhibiting elementary automatic control functionality. The whole approach also highlights the challenges for achieving a truly working solution and provides directions for future exploitation and improvements.

Energy-Saving Optimization and Control of Autonomous Electric Vehicles With Considering Multiconstraints.

The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle's powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.

Agent-Based Framework for Self-Organization of Collective and Autonomous Shuttle Fleets

The mobility of people is at the center of transportation planning and decision-making of the cities of the future. In order to accelerate the transition to zero-emissions and to maximize air quality benefits, smart cities are prioritizing walking, cycling, shared mobility services and public transport over the use of private cars. Extensive progress has been made in autonomous and electric cars. Autonomous Vehicles (AV) are increasingly capable of moving without full control of humans, automating some aspects of driving, such as steering or braking. For these reasons, cities are investing in the infrastructure and technology needed to support connected, multi-modal transit networks that include shared electric Autonomous Vehicles (AV). The relationship between traditional public transport and new mobility services is in the spotlight and need to be rethought. This paper proposes an agent-based simulation framework that allows for the creation and simulation of mobility scenarios to investigate the impact of new mobility modes on a city daily life. It lets traffic planners explore the cooperative integration of AV using a decentralized control approach. A prototype has been implemented and validated with data of the city of Trento.

NFTSM control of direct yaw moment for autonomous electric vehicles with consideration of tire nonlinear mechanical properties

NFTSM control of direct yaw moment for autonomous electric vehicles with consideration of tire nonlinear mechanical properties

V2G-capable shared autonomous electric vehicles fleet: Economic viability and environmental co-benefits

V2G-capable shared autonomous electric vehicles fleet: Economic viability and environmental co-benefits

MPC based active disturbance rejection control for automated steering control

This paper proposes a control strategy for the design of an automated steering control for an autonomous electric vehicle. The proposed Active Disturbance Rejection Control (ADRC) with Model Predective Control (MPC) is not only capable of alleviating the disturbance but also shows robustness against structured uncertainties which may arise due to models that represent the vehicle dynamics. Simulations have been carried out to assess the effectiveness of the proposed control strategy. Simulation results show that the proposed scheme is better in terms of tracking performance than MPC and ADRC. The steering control system, with the proposed strategy, can achieve faster response, higher tracking accuracy, and improved robustness performance in dealing with model uncertainties and external disturbances.

Optimal operations planning of electric autonomous vehicles via asynchronous learning in ride-hailing systems

Ride-hailing systems with electric autonomous vehicles are recognized as a next-generation development to ease congestion, reduce costs and carbon emissions. In this paper, we consider the operation planning problem involving vehicle dispatching, relocation, and recharging decisions. We model the problem as a Markov Decision Process (MDP) to generate the optimal policy that maximizes the total profits. We propose a flexible policy to provide optimal actions according to the reward considering future requests and vehicle availability. We show that our model outperforms the predetermined rules on improving profits. To handle the curse-of-dimensionality caused by the large scale of state space and uncertainty, we develop an asynchronous learning method to solve the problem by approximating the value function. We first draw the samples of exogenous information and update the quality of approximations based on the quality of decisions, then approximate the exact cost-to-go value function by solving an approximation subproblem efficiently given the state at each period. Two variant algorithms are presented for cases with scarce and sufficient information. We also incorporate the state aggregation and post-decision analysis to further improve computational efficiency. We use a set of shared actual data from Didi platform to verify the proposed model in numerical experiments. To conclude, we extract managerial insights that suggest important guidelines for the ride-hailing operations planning problem.

Perspectives of Convertors and Communication Aspects in Automated Vehicles, Part 2: Printed Antennas and Sensors for Automotive Radars

Automated vehicles are becoming popular across the communities of e-transportation across the globe. Hybrid electric vehicles and autonomous vehicles have been subjected to critical research for decades. The research outcomes pertinent to this topic in the literature have been motivated by the industry and researchers to emphasize automated vehicles. Part 1 of this survey addressed the critical aspects that concern the bidirectional converter topologies and condition monitoring activities. In the present part, 24- and 77-GHz low-profile printed antennas are studied for automotive radar applications. These antennas are mounted on automated vehicles to avoid collision and are used for radio tracking applications. The present paper states the types of antenna structures, feed mechanisms, dielectric material requirements, design techniques, performance parameters, and challenges at 24- and 77-GHz resonating frequency applications. The recent developments in feed methodologies, beam scanning concepts, and the effect of sidelobe levels are addressed. Furthermore, the reasons behind the transition from 24 to 77 GHz are reported in detail. The recent advances in the application of various sensor schemes in an automated vehicle have also been discussed.