Browse Topic: Vehicle to grid (V2G)
ABSTRACT Electric vehicle (EV) aggregation to provide vehicle-to-grid (V2G) services is a topic that has generated research into the economics and viability of using EVs for more than transportation, but little has been demonstrated to this point. This is especially true of using bidirectional power flows to move energy to the grid from EVs or to provide variable charge and discharge control. Our work focuses on implementing bi-directional functionality to demonstrate both V2G services and islanded microgrid support. The use of an intelligent microgrid controller combined with an EV aggregator provides new control capabilities for EV participation as energy storage devices
Modern automotive industry field is recently moving to more electrification level, so the presence of Battery Electric Vehicles (BEVs) is constantly increasing, along with charging technology evolution. Typically, BEVs do not use a significant portion of their battery’s capacity in day-to-day travel, which means their most valuable asset, the battery, sits idle during most of its life. Vehicle to Load (V2L) feature enables the transfer of energy from vehicle to the external loads (like utility tools, dryer, camping equipment or any other electrical appliance) which is connected to the power socket present in the Power Panel to perform AC Discharging. V2L technology lets consumers get more energy from a vehicle, even when it is turned off, improving consumer appeal. Bottomline, consumers can use this on-board Power Panel like a normal portable generator. More specifically, this paper will explore a scalable V2L architecture design with on-board Smart Power Panel technology, requested to
CASE VP Jay Joseph outlines dramatic cost reductions in fuel-cell systems, the move into stationary power, and new models for mobile and residential energy. Is the long-promised “hydrogen economy” still 15 years away, as it reportedly has been for… more than 15 years? Or is it just around the corner? SAE Media traveled to Honda's U.S. campus in Torrance, California, to see the company's latest progress. This was the introduction of Honda's zero-emission stationary fuel-cell power station, which now is in service as a backup power source for the company's data center. Honda's FCX was the the world's first production fuel-cell vehicle when it debuted in 2002. Since then the company's hydrogen developments have continued. Honda began collaborating on fuel-cell systems in 2013 and the two OEMs share a fuel-cell manufacturing joint venture. The Torrance event also presented the opportunity to speak with Jay Joseph, Honda's VP of Connected, Autonomous, Shared and Electrified (CASE
Interoperability and ‘smart’ energy management are vital for meeting EV charging demand. The clock is ticking for the automotive industry to meet looming “greener” energy deadlines, which will come into effect at the end of the decade. Achieving widescale adoption of electric vehicles (EVs) and meeting the mandates will require significant changes. One area that needs more attention is how to power the transition to an electric future. With the demand for electricity expected to grow nearly 20% by 2050 due to EVs and other clean tech initiatives, the grid is under immense pressure. With the aging infrastructure already creaking, expecting it to support this growth is not feasible using the established electricity value chain: generation, transmission, distribution, and consumption. Successfully powering the transition requires utilities and the broader ecosystem to collaborate and look at energy capacity in new ways
Transportation electrification is much needed as it can help to reduce the consumption of petroleum fuels. At the same time importance of the charging system to energize electric vehicles is also growing. Currently AC level 1 charging (120V, <2KW) and AC level 2 Charging (240V, <10KW) are used to charge the electric vehicle in residential and workplaces. The off-board chargers have significance as they can charge the vehicles in less time like gas/petrol stations. These off-board charging stations are comprised of two power conversion stages. One is for the rectification process along with power factor correction to obtain DC output from the input utility grid and DC/DC stage to get the regulated DC voltage from the rectifier output. One can reduce the charging time by increasing the output charging power at the power conversion stage. Hence, the present work deals with a novel DC-DC converter topology for fast charging applications and the novelty lies in the Electric vehicle charging
The article presents the results of an experimental analysis of the possibility of gaining electricity to external loads from the Hybrid Electric Vehicle powertrain. The tests were carried out on a vehicle with a series-parallel hybrid drive system, where a mode of charging a battery at standstill is possible. The analysis was aimed at determining the feasibility of using a hybrid vehicle as a stationary source of electricity in the Vehicle-to-Load, Vehicle-to-Home, and in emergency applications even as Vehicle-to-Grid application. The tests consisted in loading the High-Voltage battery of the car with an external load of several different values. In the first approach, receivers intended for 230V AC power were used, but also tolerant to DC power supply with a voltage in the range of 200-250V. The operating parameters of the vehicle's hybrid drive system were recorded, as well as the amount of energy supplied to the receivers from the system. Particular attention was paid to the
This paper explores the efficacy and efficiency of a system for the effective location of electric gridlines during daytime and night-time by the onboard and offboard transceivers of UAV through vehicle to infrastructure communication. The usage of electric gridlines in urban areas helps to extend the range of the UAVs by charging the onboard battery using an extended arm. The same arm can also be used for direct propulsion of the motors onboard UAV, thereby minimizing the reliance on battery. UAVs with advanced Image processing algorithms are utilized in the inspection of the electric grid lines themselves in the Power industry. The camera based algorithms are not effective during night-time when the gridlines are near invisible. This can be mitigated by evaluating light in other spectral ranges, but this would add to the load of the UAV. We propose a system which combines multiple information sources and helps locate the gridlines for range extension, specifically for the delivery of
Bi-directional charging is a value-added feature that seems certain to help accelerate EV adoption. Although other automakers have talked about the potential for electric vehicles (EVs) to use their increasingly powerful batteries for purposes beyond propelling the vehicle, it was Ford that brought the capability known as bi-directional charging to prominence when it revealed details of its new F-150 Lightning last May. A subsequent high-visibility marketing campaign dramatically showed the Lightning using its bi-directional charging capability to power a sizeable home during a power outage. Now, just months before the Lightning hits dealer showrooms, the outsized response to the Lightning - Ford has twice doubled the truck's projected annual production volume to a current 150,000 units - and the gee-whiz nature of its Intelligent Backup Power capability may accelerate the industry's march toward bi-directional charging as a standard feature for most EVs. Although there is specific
This SAE Recommended Practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing the performance of automated driving system (ADS) during an event that meets the trigger threshold criteria specified in this document. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify a common ADS data logger record format as applicable for motor vehicle applications. Automated driving systems (ADSs) perform the complete dynamic driving task (DDT) while engaged. In the absence of a human “driver,” the ADS itself could be the only witness of a collision event. As such, a definition of the ADS data recording is necessary in order to standardize information available to the accident reconstructionist. For this purpose, the data elements defined herein supplement the SAE J1698-1 defined EDR in order to facilitate the determination of the background and events leading up to a
Vehicle-to-Grid (V2G) technology is capable of providing grid services from electric vehicles (EVs). To do so, it imposes more demanding engineering design requirements on electric vehicle supply equipment (EVSE). To provide grid services, bidirectional power flow and accumulated energy between grid and EVs must be metered and uploaded to a remote server participating in electricity markets. Requirements, including accuracy and latency of power and energy data, are crucial parameters. In this article, an overview is provided of a built-in metering module, now designed and built into an EVSE charging station. Design decisions and performance include measurement approaches, communication to a higher-level module, and the standards that must be met
Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone. The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term
Idle Stop-and-go (ISG), also known as Auto Stop/Start, is a fuel saving technology common to many modern vehicles that enables the engine to shut down when the vehicle comes to a stop. Although it may help with fuel efficiency, many drivers in the North American market find the feature to be an annoyance due to hesitation in vehicle re-launch and engine shudder during stop or restart. This paper introduces the usage of traffic signal phase and timing (SPaT) information for controlling the activation of ISG with the goal of reducing driver complaints and increasing acceptance of the function. Previous studies proposed the utilization of Advanced Driver Assistance System (ADAS) to introduce adaptability in powertrain controls to traffic situation changes. For instance, when a vehicle stops and the engine shuts off, the controller monitors the movement of the preceding vehicle using ADAS sensors and restarts the engine when the front launches, prior to the driver releasing the brake pedal
Green Light Optimized Speed Advisory (GLOSA) systems have the objective of providing a recommended speed to arrive at a traffic signal during the green phase of the cycle. GLOSA has been shown to decrease travel time, fuel consumption, and carbon emissions; simultaneously, it has been demonstrated to increase driver and passenger comfort. Few studies have been conducted using historical cycle-by-cycle phase probabilities to assess the performance of a speed advisory capable of recommending a speed for various traffic signal operating modes (fixed-time, semi-actuated, and fully-actuated). In this study, a GLOSA system based on phase probability is proposed. The probability is calculated prior to each trip from a previous week’s, same time-of-day (TOD) and day-of-week (DOW) period, traffic signal controller high-resolution event data. By utilizing this advisory method, real-time communications from the vehicle to infrastructure (V2I) become unnecessary, eliminating data-loss related
Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position
Items per page:
50
1 – 50 of 99