Deployment of Autonomous Electric Taxis with Consideration for Charging Stations

Autonomous electric vehicles are set to replace most conventional vehicles in the near future. Extensive research is being done to improve efficiency at the individual and fleet level. There is much potential benefit in optimizing the deployment and rebalancing of Autonomous Electric Taxi Fleets (AETF) in cities with dynamic demand and limited charging infrastructure. We propose a Fleet Management System with an Online Optimization Model to assign idle taxis to either a region or a charging station considering the current demand and charging station availability. Our system uses real-time information such as demand in regions, taxi locations and state of charge (SoC), and charging station availability to make optimal decisions in satisfying the dynamic demand considering the range-based constraints of electric taxis. We integrate our Fleet Management System with MATSim, an agent-based transport simulator, to simulate taxis serving real on-demand requests extracted from the San Francisco taxi mobility dataset. We found our system to be effective in rebalancing and ensuring efficient taxi operation by assigning them to charging stations when depleted. We evaluate this system using different performance metrics such as passenger waiting time, fleet efficiency (taxi empty driving time) and charging station utilization by varying initial SoC of taxis, frequency of optimization and charging station capacity and power

Ready To Roll: Southeastern Pennsylvania's Regional Electric Vehicle Action Plan

On-road internal combustion engine (ICE) vehicles are responsible for nearly one-third of energy use and one-quarter of greenhouse gas (GHG) emissions in southeastern Pennsylvania.1 Electric vehicles (EVs), including plug-in hybrid electric vehicles (PHEVs) and all-electric vehicles (AEVs), present an opportunity to serve a significant portion of the region's mobility needs while simultaneously reducing energy use, petroleum dependence, fueling costs, and GHG emissions. As a national leader in EV readiness, the region can serve as an example for other efforts around the country."Ready to Roll! Southeastern Pennsylvania's Regional EV Action Plan (Ready to Roll!)" is a comprehensive, regionally coordinated approach to introducing EVs and electric vehicle supply equipment (EVSE) into the five counties of southeastern Pennsylvania (Bucks, Chester, Delaware, Montgomery, and Philadelphia). This plan is the product of a partnership between the Delaware Valley Regional Planning Commission (DVRPC), the City of Philadelphia, PECO Energy Company (PECO; the region's electricity provider), and Greater Philadelphia Clean Cities (GPCC). Additionally, ICF International provided assistance to DVRPC with the preparation of this plan. The plan incorporates feedback from key regional stakeholders, national best practices, and research to assess the southeastern Pennsylvania EV market, identify current market barriers, and develop strategies to facilitate vehicle and infrastructure deployment

Towards electric bus system: planning, operating and evaluating

The green transformation of public transportation is an indispensable way to achieve carbon neutrality. Governments and authorities are vigorously implementing electric bus procurement and charging infrastructure deployment programs. At this primary but urgent stage, how to reasonably plan the procurement of electric buses, how to arrange the operation of the heterogeneous fleet, and how to locate and scale the infrastructure are urgent issues to be solved. For a smooth transition to full electrification, this thesis aims to propose systematic guidance for the fleet and charging facilities, to ensure life-cycle efficiency and energy conservation from the planning to the operational phase.One of the most important issues in the operational phase is the charge scheduling for electric buses, a new issue that is not present in the conventional transit system. How to take into account the charging location and time duration in bus scheduling and not cause additional load peaks to the grid is the first issue being addressed. A charging schedule optimization model is constructed for opportunity charging with battery wear and charging costs as optimization objectives. Besides, the uncertainty in energy consumption poses new challenges to daily operations. This thesis further specifies the daily charging schedules with the consideration of energy consumption uncertainty while safeguarding the punctuality of bus services.In the context of e-mobility systems, battery sizing, charging station deployment, and bus scheduling emerge as crucial factors. Traditionally these elements have been approached and organized separately with battery sizing and charging facility deployment termed planning phase problems and bus scheduling belonging to operational phase issues. However, the integrated optimization of the three problems has advantages in terms of life-cycle costs and emissions. Therefore, a consolidated optimization model is proposed to collaboratively optimize the three problems and a life-cycle costs analysis framework is developed to examine the performance of the system from both economic and environmental aspects. To improve the attractiveness and utilization of electric public transportation resources, two new solutions have been proposed in terms of charging strategy (vehicle-to-vehicle charging) and operational efficiency (mixed-flow transport). Vehicle-to-vehicle charging allows energy to be continuously transmitted along the road, reducing reliance on the accessibility and deployment of charging facilities. Mixed flow transport mode balances the directional travel demands and facilities the parcel delivery while ensuring the punctuality and safety of passenger transport

Smart Energy and Intelligent Transportation Systems

With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation

Optimizing Sustainable Transit Bus Networks in Smart Cities

Urban mobility has been facing several challenges in the recent years due to the increasing populations and private vehicles ownership, which led to several negative environmental and social impacts in big cities. In this dissertation, we investigate how decision support systems can enhance the role of transit bus networks in the transition to more sustainable and convenient urban mobility

Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control

This thesis proposes methodologies to improve heavy vehicle design by reducing the total cost of ownership and by increasing energy efficiency and safety.Environmental issues, consumers expectations and the growing demand for freight transport have created a competitive environment in providing better transportation solutions. In this thesis, it is proposed that freight vehicles can be designed in a more cost- and energy-efficient manner if they are customized for narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to minimize the total cost of ownership and to deliver customized vehicles with tailored propulsion components that best fit the given transportation missions and operational environment. Optimization-based design of the vehicle components was found to be effective due to the simultaneous consideration of the optimization of the transportation mission infrastructure, including charging stations, loading-unloading, routing and fleet composition and size, especially in case of electrified propulsion. Implementing integrated vehicle hardware-transportation optimization could reduce the total cost of ownership by up to 35% in the case of battery electric heavy vehicles. Furthermore, in this thesis, the impacts of two future technological advancements, i.e., heavy vehicle electrification and automation, on road freight transport were discussed. It was shown that automation helps the adoption of battery electric heavy vehicles in freight transport. Moreover, the optimizations and simulations produced a large quantity of data that can help users to select the best vehicle in terms of the size, propulsion system, and driving system for a given transportation assignment. The results of the optimizations revealed that battery electric and hybrid heavy combination vehicles exhibit the lowest total cost of ownership in certain transportation scenarios. In these vehicles, propulsion can be distributed over different axles of different units, thus the front units may be pushed by the rear units. Therefore, online optimal energy management strategies were proposed in this thesis to optimally control the vehicle motion and propulsion in terms of the minimum energy usage and lateral stability. These involved detailed multitrailer vehicle modeling and the design and solution of nonlinear optimal control problems

Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces