Charging Autonomous Electric Vehicle Fleet for Mobility-on-Demand Services: Plug in or Swap out?

This paper compares two prevalent charging strategies for electric vehicles, plug-in charging and battery swapping, to investigate which charging strategy is superior for electric autonomous mobility-on-demand (AMoD) systems. To this end, we use a queueing-theoretic model to characterize the vehicle waiting time at charging stations and battery swapping stations, respectively. The model is integrated into an economic analysis of the electric AMoD system operated by a transportation network company (TNC), where the incentives of passengers, the charging/operating shift of TNC vehicles, the operational decisions of the platform, and the planning decisions of the government are captured. Overall, a bi-level optimization framework is proposed for charging infrastructure planning of the electric AMoD system. Based on the proposed framework, we compare the socio-economic performance of plug-in charging and battery swapping, and investigate how this comparison depends on the evolving charging technologies (such as charging speed, battery capacity, and infrastructure cost). At the planning level, we find that when choosing plug-in charging, increased charging speed leads to a transformation of infrastructure from sparsely distributed large stations to densely distributed small stations, while enlarged battery capacity transforms the infrastructure from densely distributed small stations to sparsely distributed large stations. On the other hand, when choosing battery swapping, both increased charging speed and enlarged battery capacity will lead to a smaller number of battery swapping stations. At the operational level, we find that improved charging speed leads to increased TNC profit when choosing plug-in charging, whereas improved charging speed may lead to smaller TNC profit under battery swapping. The above insights are validated through realistic numerical studies

Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging

The impact of service and government-policy attributes on consumer preferences for electric vehicles in China

This research focuses on the effects of different types of service attributes and context-based government policies, along with product attributes, on Chinese consumers’ adoption of electric vehicles (EVs). Based on a stated choice experiment involving over 1,000 respondents in different cities of China, a mixed logit (MXL) model shows that typical product attributes are consistently important for potential car buyers, but that charging service has a mixed effect, depending on the level of service provision and speed. Specifically, the availability of a home charging facility has the strongest influence on consumers’ choice to purchase EVs, and the service speed of public fast service stations is also significant. In relation to government policies, this study finds that in addition to government subsidy, free licensing policy for EVs is very attractive for consumers, compared to the lottery-based licensing for conventional petrol vehicles (PVs). We find that Chinese consumers have the highest willingness to pay for obtaining a free vehicle license for EVs (106,144 RMB on average) and being permitted to install a home charging post (91,039 RMB on average). Our findings imply the importance of considering consumers’ perceived inconvenience associated with using EVs compared to buying and using conventional PVs. Furthermore, policy makers should consider the heterogeneous preference towards EVs when designing intervention policies in the Chinese market

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

Implementations of electric vehicle system based on solar energy in Singapore assessment of lithium ion batteries for automobiles

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 142-150).In this thesis report, both quantitative and qualitative approaches are used to provide a comprehensive analysis of lithium ion (Li-ion) batteries for plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) from technological and economical perspectives. Five key factors including power density, energy density, safety, durability, and cost are employed to compare four types of Li-ion batteries. Utility analysis indicates that all the Li-ion batteries are able to satisfy both power density and energy density targets, but only two of them are able to meet safety and durability requirements. Currently, the main challenge for their automotive application is cost reduction, since the cheapest LiFePO₄ battery costs $247.8/kWh which is 1.65 times the cost target established by USABC. Economical values of PHEV and BEV are presented from an end user's point of view. Various sensitivity analysis have been used to identify the impact of key factors such as battery pack cost reduction, driving distance, gasoline price, and government subsidizations on cost effectiveness of PHEV and BEV. Results show that$4,270 and $7,726 of U.S. government subsidizations to an individual user are needed for PHEV and BEV to breakeven. Lastly, the lithium ion battery based electric vehicle systems have also been evaluated in the implementation models in Singapore. The conclusion is that it is not feasible to adopt electric vehicle system in Singapore under current government incentives.by Haitao Fu.M.Eng

Spatial-temporal domain charging optimization and charging scenario iteration for EV

Environmental problems have become increasingly serious around the world. With lower carbon emissions, Electric Vehicles (EVs) have been utilized on a large scale over the past few years. However, EVs are limited by battery capacity and require frequent charging. Currently, EVs suffer from long charging time and charging congestion. Therefore, EV charging optimization is vital to ensure drivers’ mobility. This study first presents a literature analysis of the current charging modes taxonomy to elucidate the advantages and disadvantages of different charging modes. In specific optimization, under plug-in charging mode, an Urgency First Charging (UFC) scheduling policy is proposed with collaborative optimization of the spatialtemporal domain. The UFC policy allows those EVs with charging urgency to get preempted charging services. As conventional plug-in charging mode is limited by the deployment of Charging Stations (CSs), this study further introduces and optimizes Vehicle-to-Vehicle (V2V) charging. This is aim to maximize the utilization of charging infrastructures and to balance the grid load. This proposed reservation-based V2V charging scheme optimizes pair matching of EVs based on minimized distance. Meanwhile, this V2V scheme allows more EVs get fully charged via minimized waiting time based parking lot allocation. Constrained by shortcomings (rigid location of CSs and slow charging power under V2V converters), a single charging mode can hardly meet a large number of parallel charging requests. Thus, this study further proposes a hybrid charging mode. This mode is to utilize the advantages of plug-in and V2V modes to alleviate the pressure on the grid. Finally, this study addresses the potential problems of EV charging with a view to further optimizing EV charging in subsequent studies

Capacity analysis in different systems exploiting mobility of VANETs

Improving road safety and traffic efficiency has been a long-term endeavor for not only government but also automobile industry and academia. After the U.S. Federal Communication Commission (FCC) allocated a 75 MHz spectrum at 5.9 GHz for vehicular communications, the vehicular ad hoc network (VANET), as an instantiation of the mobile ad hoc network (MANET) with much higher node mobility, opens a new door to combat the road fatalities. In VANETs, a variety of applications ranging from safety related (e.g. emergency report, collision warning) to non-safety-related (e.g. infotainment and entertainment) can be enabled by vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications. However, the flourish of VANET still hinges fully understanding and managing the challenges that the public concerns, for example, capacity and connectivity issues due to the high mobility of vehicles. In this thesis, we investigate how vehicle mobility can impact the performance in three important VANET-involved systems, i.e., pure VANET, VANET-enhanced intelligent transportation systems (ITS), and fast electric vehicle (EV) charging systems. First, in pure VANET, our work shows that the network data-traffic can be balanced and the network throughput can be improved with the help of the vehicle mobility differentiation. Furthermore, leveraging vehicular communications of VANETs, the mobility-aware real-time path planning can be designed to smooth the vehicle traffic in an ITS, through which the traffic congestion in urban scenarios can be effectively relieved. In addition, with the consideration of the range anxiety caused by mobility, coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. To this end, we try to answer the following questions: Q1) How to utilize mobility characteristics of vehicles to derive the achievable asymptotic throughput capacity in pure VANETs? Q2) How to design path planning for mobile vehicles to maximize spatial utility based on mobility differentiation, in order to approach vehicle-traffic capacity in a VANET-enhanced ITS? Q3) How to develop the charging strategies based on mobility of electric vehicles to improve the electricity utility, in order to approach load capacities of charging stations in VANET-enhanced smart grid? To achieve the first objective, we consider the unique features of VANETs and derive the scaling law of VANETs throughput capacity in the data uploading scenario. We show that in both free-space propagation and non-free-space propagation environments, the achievable throughput capacity of individual vehicle scales as $\Theta (\frac{1}{{\log n}}) with$n$denoting the population of a set of homogenous vehicles in the network. To achieve the second objective, we first establish a VANET-enhanced ITS, which incorporates VANETs to enable real-time communications among vehicles, road side units (RSUs), and a vehicle-traffic server in an efficient way. Then, we propose a real-time path planning algorithm, which not only improves the overall spatial utilization of a road network but also reduces average vehicle travel cost for avoiding vehicles from getting stuck in congestion. To achieve the third objective, we investigate a smart grid involved EV fast charging system, with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits VANETs to support real-time communications among RSUs and highly mobile EVs for real-time vehicle mobility information collection or charging decision dispatch. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. In summary, the analysis developed and the scaling law derived in$Q1$of this thesis is practical and fundamental to reveal the relationship between the mobility of vehicles and the network performance in VANETs. And the strategies proposed in$Q2$and$Q3$ of the thesis are meaningful in exploiting/leveraging the vehicle mobility differentiation to improve the system performance in order to approach the corresponding capacities