Consolidating Bus Charger Deployment and Fleet Management for Public Transit Electrification: A Life-Cycle Cost Analysis Framework

Despite rapid advances in urban transit electrification, the progress of systematic planning and management of the electric bus (EB) fleet is falling behind. In this research, the fundamental issues affecting the nascent EB system are first reviewed, including charging station deployment, battery sizing, bus scheduling, and life-cycle analysis. At present, EB systems are planned and operated in a sequential manner, with bus scheduling occurring after the bus fleet and infrastructure have been deployed, resulting in low resource utilization or waste. We propose a mixed-integer programming model to consolidate charging station deployment and bus fleet management with the lowest possible life-cycle costs (LCCs), consisting of ownership, operation, maintenance, and emissions expenses, thereby narrowing the gap between optimal planning and operations. A tailored branch-and-price approach is further introduced to reduce the computational effort required for finding optimal solutions. Analytical results of a real-world case show that, compared with the current bus operational strategies and charging station layout, the LCC of one bus line can be decreased significantly by 30.4%. The proposed research not only performs life-cycle analysis but also provides transport authorities and operators with reliable charger deployment and bus schedules for single- and multi-line services, both of which are critical requirements for decision support in future transit systems with high electrification penetration, helping to accelerate the transition to sustainable mobility

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

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A Method of EV Detour-to-Recharge Behavior Modeling and Charging Station Deployment

Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a methodology of describing EV detouring behavior for recharging, and based on this, we adopt the extra driving length caused by detouring and the length of uncompleted route as the indicators of evaluating an EV charging station deployment plan. In this way, we can simulate EV behavior based on travel data (demand). Then, a genetic algorithm (GA) based EV charging station sitting optimization method is developed to obtain an effective plan. A detailed case study based on a 100-node 203-branch transportation network within a 30 km * 30 km region is included to test the effectiveness of our method. Insights from our method may be applicable for charging station planning in various transportation networks

Demand Calculation Method for Electric Vehicle Charging Station Locating and Deployment

To develop, plan, implement and operate the electric road mobility system, especially the charging infrastructure, the existing and potential demand must be revealed for several time horizons. Accordingly, the aim of the research was to elaborate a calculation method for electric vehicle charging demand and to determine the public charging infrastructure locating principles. The research questions were: how many and what kind of vehicles will be used; where, when and how long they will be charged; what aspects and how influence the charging station deployment. The number of charging points to be installed, the energy demand and capacity management parameters can be also determined using the revealed correlations. The calculation method is adaptable to any territorial unit and any time horizon. It is the basis of charging station locating methods, which is demonstrated through two novel geoinformatics applications

UBAT: On Jointly Optimizing UAV Trajectories and Placement of Battery Swap Stations

Unmanned aerial vehicles (UAVs) have been widely used in many applications. The limited flight time of UAVs, however, still remains as a major challenge. Although numerous approaches have been developed to recharge the battery of UAVs effectively, little is known about optimal methodologies to deploy charging stations. In this paper, we address the charging station deployment problem with an aim to find the optimal number and locations of charging stations such that the system performance is maximized. We show that the problem is NP-Hard and propose UBAT, a heuristic framework based on the ant colony optimization (ACO) to solve the problem. Additionally, a suite of algorithms are designed to enhance the execution time and the quality of the solutions for UBAT. Through extensive simulations, we demonstrate that UBAT effectively performs multi-objective optimization of generation of UAV trajectories and placement of charging stations that are within 8.3% and 7.3% of the true optimal solutions, respectively.Comment: Accepted for publication in ICRA, 202

Operational Methods for Charging of Electric Vehicles

The increasing number of electric vehicles induces a new relationship between the electric vehicles, transportation network and electric network. The deployment of the charging infrastructure is a prerequisite of the widespread of electric vehicles. Furthermore, the charging process and energy management have a significant influence on the operation of both the transportation and electric networks. Therefore, we have elaborated novel operational methods that support the deployment of charging infrastructure for electric cars and buses operating in public bus service, and the energy management. Weighted sum-models were developed to assess candidate sites for public charging stations. The mathematical model of public bus services was elaborated that supports the optimization of static charging infrastructure at bus stops and terminals without schedule adjustments. The flexibility and predictability of charging sessions were identified as the main differences between charging infrastructure deployment for cars and buses. Furthermore, the flows of energy, information and value have been revealed among the components of charging with a focus on commercial locations, which is the basis of energy flow optimization on the smart grid