Leveraging electric vehicles as a resiliency solution for residential backup power during outages
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Transportation system simulation is a widely accepted approach to evaluate the impact of transport policy deployment. In developing a transportation system deployment model, the energy impact of the model is extremely valuable for sustainability and validation. It is expected that different penetration levels of Connected-Autonomous Vehicles (CAVs) will impact travel behavior due to changes in potential factors such as congestion, miles traveled, etc. Along with such impact analyses, it is also important to further quantify the regional energy impact of CAV deployment under different factors of interest. The objective of this paper is to study the energy consumption of electrified vehicles in the future for different penetration levels of CAVs deployment in the City of Chicago. The paper will further provide a statistical analysis of the results to evaluate the impact of the different penetration levels on the different electrified powertrains used in the study.
The proliferation of Connected and Autonomous Vehicles (CAVs) has necessitated the development of efficient, reliable communication methods between vehicles and their surroundings. This study presents a novel methodology, the VECTOR system, that addresses this need by converting dynamic vehicle data into a binary code and displaying it on an LED panel. The system involves a three-step process of data collection, polynomial fitting of velocity data, and encoding the polynomial parameters into binary form using a Cyclic Redundancy Check (CRC). The study also explores the practical application of this system by conducting experiments with modified Lincoln MKZ hybrids, demonstrating the feasibility and efficiency of this approach. The paper presents both the methodology and results of these experiments, further expanding upon the potential applications and implications for CAV technology. By comparing the final accuracy, we find this approach can achieve 80% of the original velocity data.
Vehicles with advanced driver assistance systems (ADAS) like adaptive cruise control (ACC) could use connected (V2X) data to improve fuel economy Eco driving generates an energy efficient speed profile by minimizing acceleration events SwRI demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks by using nearby vehicle and traffic signal information How does eco driving impact vehicle emissions and aftertreatment (AT) systems?
The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.
As electric vehicle (EV) adoption accelerates, vehicle-to-grid (V2G) strategies offer advantages over unmanaged charging (V0G) by enhancing grid stability, reducing fleet operation costs, and supporting integration of variable generation resources. This research develops a day-ahead optimization framework linked with agent-based simulations to evaluate coordinated V2G participation by fleet and personal EVs under 5 energy-pricing settings in Austin, Texas. Three scenarios (V0G, fleet-only V2G, and joint-V2G) are examined, considering real-time price and grid profiles, health-damage costs, and operational constraints for both fleet and personal EVs. Results show how V2G scenarios shift fleet EV charging to mid-day while enabling strategic battery-discharge during evening peaks, mitigating grid stress and lowering EV energy costs. V2G delivers close to 80% energy-cost savings for a 2000-EV fleet in Austin on grid-stressed days, with 55% lower charging pollutant outputs. Joint-V2G amplifies system-level benefits by complementing fleet discharge, but smart-charging equipment costs can offset those benefits.
Electric vehicle managed charging can benefit all consumers by supporting grid planning, operation, and reliability – especially complementing high-renewable systems.
Electric vehicles (EVs) are expected to drastically increase residential electricity consumption and could provide a significant source of flexible demand. Aggregating smart EV charge controllers with other smart home devices through a home energy management system can lead to more optimal outcomes that benefit homeowners, utilities, and grid operators. Control strategies should consider occupant convenience by accounting for the need for fully charged EVs near the EV departure time. In this paper, we develop an EV charging framework that accounts for occupant convenience using OCHRE, a residential energy model, and foresee, a home energy management system. We simulate a community with high EV penetration and show that integrated, smart EV charging reduces peak demand and smooths night-time energy consumption. Simulation results show that the proposed control strategy nearly eliminates peak period EV charging and reduces the daily peak demand from EVs by 23%.
Electric vehicles are seeing growing adoption. However, challenges with range anxiety persists. While charging infrastructure is anticipated to expand, EV charger systems have not been as robust to challenges. This paper discusses potential outage conditions associated with EV charging and presents new technology in development to improve EV charging resilience.
Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling. DESS is a text-based software package that provides a flexible framework for building and solving systems of ODEs. This allows DESS to be included as a dependency for automotive powertrain models that require a variety of solvers and solver configurations. Seven explicit ODE solver methods have been implemented in DESS: Euler’s, Heun’s, midpoint, Ralston’s, classic Runge-Kutta, Bogacki-Shampine, and Cash-Karp. These represent five fixed-step methods and two adaptive-step methods. This paper shows that the solver implementations increase accuracy and computational efficiency compared to Euler's method when modeling a system of three thermal masses in Rust. DESS also includes features designed for modeling component-based physical systems. Users can define relationships between nodes in their system, which the package then translates into a system of equations, leading to simpler and more intuitive code. In the case of a three-thermal-mass system, the user can specify node thermal properties (e.g., thermal capacitance), how nodes are interconnected, and thermal conductance between nodes rather than providing a system of equations. The core contribution from this work is an open-source, text-based Rust package with ODE solvers for automotive powertrain modeling to support cost-free, fast, and accurate simulation.
This presentation is an overview of the technical standards governing electric vehicle supply equipment and was presented to support the development of the Lao PDR electric vehicle market.
Wireless charging of an electric vehicle (EV) is an emerging technology that enables noncontact charging, which has advantages in terms of convenience and flexibility. However, the embedded charging pads under and on the ground expose the charging coils, ferrite shielding, and related components to a potentially severe external environment. To study the electromagnetic shielding performance under such extreme temperature conditions, ferrite shielding material's permeability characteristics are tested under temperatures as low as -50°C to as high as 240°C. Referring to the official recorded lowest and highest temperature of the United States, in this paper, it is assumed that the operation temperature ranges from -50°C to 57°C as the coldest winter and hottest summer environment for WPT application. Electromagnetic performance, shielding, and associated stray magnetic emissions are simulated by using three-dimensional (3D) transient simulations to study the impact of low and high temperature on electromagnetic shielding performance. Based on ferrite testing data, from -50°C low temperature to 57°C high temperature, the ferrite's relative permeability ranges from 2550 to 3727. The simulation study indicates that there won't be significant impact on the EM shielding performance for WPT operated under those severe environment temperature conditions.
This paper introduces the Highly Integrated Vehicle Ecosystem (HIVE), a transportation modeling tool developed by within the Center for Integrated Mobility Sciences (CIMS) group at the National Renewable Energy Laboratory (NREL). HIVE is an agent-based supply/demand model for Mobility on Demand (MoD) which mixes agent-based modeling and centralized dispatch for automated and human-driven fleets and ride hail passengers. Research questions using HIVE span multiple categories, including intelligent fleet planning (assessing fleet, battery, and infrastructure investment decisions), intelligent fleet control (charge management, vehicle dispatching) and strategic business model decision-making (depot-based full-time drivers versus gig-based drivers, human-driven versus automated). The components of the HIVE model are explained and then HIVE is demonstrated in a case study using demand data from the New York City Taxi & Limousine Commission data set.
The United States has witnessed multiple attempts to improve fuel economy and reduce pollutant emissions in the transportation sector, guided chiefly via technological interventions by original equipment manufacturers (OEMs) and by specific policies of the U.S. Federal Government, like Corporate Average Fuel Economy (CAFE) norms (Burnham et al., 2006; U.S. DoT, 2013). These efforts have resulted in the growing adoption of alternatives to conventional materials, fuels, and vehicle propulsion technologies across various transportation modes, while also helping to lower global pollution (greenhouse gas, or GHG, emissions). Yet, a holistic evaluation of the ecofriendliness of these alternatives, particularly their energy use and emissions, merits detailed focus on their entire life-cycle, and thereby, on the life-cycle of associated transport modes. This is vital since alternative fuels and materials can differ vastly in energy sources and production methods employed for their processing vis-à-vis their existing counterparts — and this difference causes significant variation in their respective upstream emissions.
Lithium metal batteries have great advantages over state-of-the-art lithium ion batteries in terms of energy density and cost, which present huge opportunities for long-range and low-cost electric vehicles in the future. However, the automotive industry has critical requirements on specific characteristics of battery cells, including but not limited to cycle life, working temperature range, fast discharge–charge capability, volume change, safety, and so on. In this Focus Review, we discuss both the cell- and system-level requirements and challenges of high-energy-density lithium metal batteries for future electrical vehicle applications and highlight some recent key progress in these aspects. Furthermore, our main objective is to identify key strategies on how to solve these challenges and inspire more research and development effort to accelerate the practical applications of lithium metal batteries.
This work presents the detailed design of an outer rotor motor and integrated drive for passenger electric vehicles. The motor uses non-heavy rare earth permanent magnets and operates at a top speed of 20,000rpm. In-slot ceramic heat exchangers are utilized for the cooling of the windings. The external rotor configuration of the motor leaves a significant amount of space in the stator bore, which is leveraged for the integration of the power electronics. A high speed external rotor motor creates a number of mechanical, thermal and assembly challenges and this work presents detailed thermal, mechanical and rotordynamics analysis.
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.