Electric Power Allocation in a Network of Fast Charging Stations

In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked by the closest charging station. Overall, the paper addresses key issues related to the efficient operation of a network of charging stations.Comment: Published in IEEE Journal on Selected Areas in Communications July 201

Location analysis of electric vehicle charging stations for maximum capacity and coverage

Electric vehicle charging facility location is a critical component of long-term strategic planning. Integration of electric vehicles into mainstream adoption has unique characteristics as it requires a careful investigation of both electric and transportation networks. In this paper, we provide an overview of recent approaches in location analyses of electric vehicle charging infrastructures. We review approaches from classical operations research for fast and slow charging stations. Sample formulations along with case studies are presented to provide insights. We discuss that classical methods are appropriate to address the coverage of charging networks which is defined as average time or distance to reach a charging station when needed. On the other hand, calculating required capacity, defined as the individual charging resources at each node, is still an open research topic. In the final part, we present stochastic facility location theory that uses queuing and other probabilistic approaches

Scheduling electric vehicle charging at park-and-ride facilities to flatten duck curves

In this paper, we explore present a scheduling framework for large-scale electric vehicle charging to flatten duck curves stemming from the imbalance between peak electricity demand and renewable energy production. This situation adds new constraints to power system operations and increases maintenance costs. The focus is on charging systems installed at park-and-ride facilities which are gaining popularity in metropolitan cities. The scheduling problem is modeled as an integer linear problem and various case studies are generated and solved using real-world collected data. The computational experiments show that significant savings can be achieved in reducing power system ramping requirements

Performance assessment of UK's cellular network for vehicle to grid energy trading : opportunities for 5G and beyond

The proliferation of plug-in electric vehicles (PEV) and advances in high-speed low latency communication networks redefine the relationships between electricity providers and end-users. A group of PEV owners, coordinated by an aggregator, can participate in ancillary energy markets to stabilize electricity grids and, in return, receive payments for services rendered. However, PEVs are typically parked across a dispersed region possibly with diverse signal strength and data rates. Vehicle-to-Grid (V2G) applications have tight latency (e.g., 500 ms to 2 seconds) and packet-loss requirements, hence, the supporting communication infrastructure should be carefully evaluated for real-world implementations. In this paper, we assess the performance of the internet-based 4G cellular network in the United Kingdom to evaluate these key metrics. We develop a low cost and easily deployable testbed platform to collect and analyze the latency and packet loss rate of different package sizes, transport protocols, and signal strengths. Due to the availability of hardware resources and city-wide coverage of 4G networks, a single parking lot to aggregator scenario is emulated. The results show that in most cases current 4G network can deliver packets less than 500ms which is required in fast frequency response applications in the UK. On the other hand, for more complex scenarios such as multi-aggregator to distributed clients, there is a need to use 5G and beyond to meet the latency requirements. To the best of authors' knowledge, this is the first study focusing on the field testing and assessment of an actual internet-based communication network for V2G applications

Quantifying the effects of communication network performance in vehicle-to-grid frequency regulation services

Recent advances in communication systems and the proliferation of plug-in electric vehicles (PEVs) hold a promise to support power systems operations with vehicle-to-grid (V2G) applications. However, such ancillary services have tight communication requirements (low-latency, high reliability) as the aggregated PEVs need to respond to market signals within seconds and bad communication system performance lead to financial losses. In this paper, we consider a frequency regulation application in which PEVs are charged and discharged according to actual market signals. We assume that a market operator sends signals through 4G/LTE network to an aggregator located at a parking lot, who, as a next step, delivers data packets to electric vehicle supply equipments (EVSEs) via a local Wi-Fi network. In the final phase, each EVSE communicates with PEV battery management unit via power line communications. By adopting communication delay and packet loss profiles from measurement and simulation studies, we examine the impacts of communication system performance on V2G performance. The results show that packet losses significantly lowers precision score, while there is a need for faster networks if multiple aggregators participate at the same time

Frequency and temperature dependence of the dielectric and AC electrical conductivity in (Ni/Au)/AlGaN/AIN/GaN heterostructures

Cataloged from PDF version of article.The dielectric properties and AC electrical conductivity (sigma(ac))of the (Ni/Au)/Al(0.22)Ga(0.78)N/AlN/GaN heterostructures, with and without the SiN(x) passivation, have been investigated by capacitance-voltage and conductance-voltage measurements in the wide frequency (5kHz-5 MHz) and temperature (80-400 K) range. The experimental values of the dielectric constant (epsilon'), dielectric loss (epsilon ''), loss tangent (tan delta), sigma(ac) and the real and imaginary part of the electric modulus (M' and M '') were found to be a strong function of frequency and temperature. A decrease in the values of epsilon' and epsilon '' was observed, in which they both showed an increase in frequency and temperature. The values of M' and M '' increase with increasing frequency and temperature. The sigma(ac) increases with increasing frequency, while it decreases with increasing temperature. It can be concluded, therefore, that the interfacial polarization can occur more easily at low frequencies and temperatures with the number of interface states density located at the metal/semiconductor interface. It contributes to the epsilon' and sigma(ac). (C) 2009 Elsevier B.V. All rights reserved

Stochastic modelling of fast DC charging stations with shared power modules

Fast DC charging stations are becoming increasingly necessary for wider electric vehicle uptake. In standard DC chargers, each charging unit has its own charging power and cannot be shared with another electric vehicle. Depending on the electric vehicle type, the maximum DC charging power varies (e.g.50 kW for small sedans and $\geq 100$ kW for SUVs) in parallel to battery chemistry and capacity. If the charger power is higher than the maximum charging capacity of an EV, then, charging resources are wasted for other vehicles which can accept high charging currents. On the other hand, recent advances in power electronics enable centralized inverters to supply power to multiple DC chargers and shift the load between them dynamically. To that end, we propose a stochastic model for a fast charging station in which the charging power modules are centrally located and electric vehicles are connected via external charging sockets. The charging station serves multi-class customers based on their charging power, random arrival and service durations. The system is modelled with a multi-rate Erlang loss system and a methodology to calculate the probability of meeting customer demand is presented. Case studies are presented to provide insights on how the station performs under varying station settings

Advancing state of charge management in electric vehicles with machine learning : a technological review

As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both the short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of the state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in the state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine the state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing the state of charge management efficiency and effectiveness for electric vehicles, offering critical insights. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry

Challenges and opportunities for car retail business in electric vehicle charging ecosystem

Mainstream electric vehicle adoption (EV) is essen- tial to decarbonise the transportation sector. Car retail business needs to be transformed in many countries to meet legislative requirements that ban the sale of new petrol and diesel cars. To that end, this paper surveys the opportunities and challenges for car dealerships who could become key players in trans- port electrification. The challenges are identified as deploying charging infrastructure at business sites, keeping EV batteries healthy until they are sold, and lack of standards and protocols for chargers and communication systems. On the other hand, right investments could make car dealers main players in the energy markets. A number of opportunities, including providing ancillary services and public charging access, as well as reuse of old EV batteries, are discussed in detail. Such considerations are critical in this early stage of designing charging stations for the future of net-zero economies