Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

This study analyzes the performance of a queue length-dependent overload control policy using a leaky bucket (LB) scheme. This queueing model is applied to the operation of a battery swapping and charging station for electric vehicles (EVs). In addition to the LB scheme, we propose two congestion control policies based on EV queue length thresholds. With these policies, the model determines both EV-arrival and battery-supply intervals, and these depend on the number of EVs waiting in the queue. The queue length distributions, including those at arbitrary epochs, are derived using embedded Markov chain and supplementary variable methods. Performance measures such as blocking probability and mean waiting time are investigated using numerical examples. We study the characteristics of the system using numerical examples and use a cost analysis to investigate situations in which the application of each congestion control policy is advantageous. Document type: Articl

Optimal Allocation of Changing Station for Electric Vehicle Based on Queuing Theory

Electric vehicle as the main development direction of the future automotive industry, has gained attention worldwide. The rationality of the planning and construction of the power station, as the foundation of energy supply, is an important premise for the development of electric vehicles. In full consideration of the electric demand and electricity consumption, this paper proposes a new construction mode in which charging station and centralized charging station are appropriately combined and presents a location optimization model. Not only can this model be applied to determine the appropriate location for the power station, but it can use the queuing theory to determine the optimal number of power equipment, with which we can achieve the minimum costs. Finally, taking a certain city as an example, the optimum plan for power station is calculated by using this model, which provides an important reference for the study of electric vehicle infrastructure planning

Toward Distributed Battery Switch Based Electro-Mobility Using Publish/Subscribe System

With the growing popularization of Electric Vehicle (EVs), Electro-mobility (in terms of where to charge EV) has become an increasingly important research problem in smart cities. One of the major concerns is the anxiety of EVs, as drivers may suffer from discomfort due to long charging time. In this paper, we leverage the battery switch technology to provide an even faster charging than plug-in charging service, by cycling switchable (fully recharged) batteries at Charging Stations (CSs). Upon that, a costefficient Publish/Subscribe (P/S) system is provisioned, to facilitate the design of distributed charging manner for privacy guarantee. The proposed communication framework utilizes Mobile Edge Computing (MEC)-functioned Road Side Units (RSUs) to bridge, process, and aggregate the information flow between CSs and EVs. We further design an advanced reservation-based charging system, in which the knowledge of EVs’ reservations is utilized to predict how likely a CS will be congested. This benefits to a smart transportation planning on where to charge, in order to improve charging comfort. Results show the advantage of our enabling technology comparing to other benchmark solutions, in terms of minimized waiting time for the battery switch (as the benefit for EV drivers), and a higher number of batteries switched (as the benefit for CSs)

Joint location and inventory models and algorithms for deployment of hybrid electric vehicle charging stations.

This thesis describes a study of a novel concept of hybrid electric vehicle charging stations in which two types of services are offered: battery swapping and fast level-3 DC charging. The battery swapping and fast-charging service are modeled by using the M/G/s/s model and the M/G/s/$\infty$ model, respectively. In particular, we focus on the operations of joint battery swapping and fast charging services, develop four joint locations and inventory models: two for the deployment of battery swapping service, two for the deployment of hybrid electric vehicle charging service. The first model for each deployment system considers a service-level constraint for battery swapping and hybrid charging service, whereas the second for each deployment system considers total sojourn time in stations. The objective of all four models is to minimize total facility setup cost plus battery and supercharger purchasing cost. The service level, which is calculated by the Erlang loss function, depends on the stockout probability for batteries with enough state of charge (SOC) for the battery swapping service and the risk of running out of superchargers for the quick charging service. The total sojourn time is defined as the sum of the service time and the waiting time in the station. Metaheuristic algorithms using a Tabu search are developed to tackle the proposed nonlinear mixed-integer optimization model. Computational results on randomly generated instances and on a real-world case comprised of 714,000 households show the efficacy of proposed models and algorithms

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles

Next generation of transportation in the form of electric vehicles relies on better operation and control of large battery packs. The individual modules in large battery packs generally do not have identical characteristics and may degrade differently due to manufacturing variability and other factors. Degraded battery modules waste more power, affecting the performance and economy for the whole battery pack. Also, such impact varies with different trip patterns. It will be cost effective if we evaluate the performance of the battery modules prior to replacing the complete battery pack. The knowledge of the driving cycle and battery internal resistance will help to make decision to replace the worst battery modules and directly cut down on user expenditure to replace the battery. Also, optimizing the performance of battery during the driving trip is the challenging task to achieve. The knowledge of energy prices of the grid, internal resistance of the lithium ion battery pack on the electric vehicle, the age of the battery and distance travelled by the electric vehicle are very important factors on which the cost of daily driving cycle is dependent. In near future, the energy consumed by the electric vehicles will create a major consumer market for the smart grids. The smart grid system is complemented by the renewable energy sources that contribute and support the grid. The electric vehicles are not only predicted as energy consumers but also as dynamic sources of energy. These vehicles can now travel more than 100 miles with a single charging cycle whereas average day to day commute is well below the maximum capacity of these vehicles. This leaves the driver with the extra energy on the battery pack which can be used later for supporting energy requirement from the grid. As we know that cells/modules in large battery packs do not have identical properties and these degrade at different rates during the course of their lifespan. It is beneficial for the user to quantify the amount of energy that can be used to support the grid. The improvement of the electric grid to the next generation infrastructure ie ‘Smart Grid’ will enable diverse opportunities to contribute the energy and balance the load on the grid. The information about the grid like price quality, load etc will be available to the people very easily. This information can be useful to make the energy grid more economical and environment friendly. We have used the information for price of energy on the grid to optimize the cost of daily driving cycle. The goal of this research is to accurately predict the battery behavior for the daily driving cycle. The prediction of battery behavior will help the driver to decide the optimum charging patterns, energy consumed during driving and the surplus energy available in the batteries. The prior knowledge of the battery behavior, price of the energy on the grid and the trip travel will help the driver to minimize the cost of travel on daily basis as well as throughout the life of the battery