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Clean Energy & Transportation
Hydrogen & Fuel Cells
Infrastructure & Energy Storage
Energy Storage & Technology
Mobility Systems & Analytics
Power & Energy Systems
Modeling and Optimization of Trip Level Energy Consumption and Charging Management for Connected Automated Electric Vehicles
Overview of Connected and Automated Electric Vehicles, Electric Vehicle Energy Consumption Modeling, Energy Efficiency Driving Technologies: Eco-Driving and Eco-Routing, Automatic Charging Decision Making for Connected and Automated Electric Vehicles
Charging Decision Making for Automated Electric Vehicles
Overview/Significance With the emerging of autonomous driving and its application in electric vehicles, the charging decision making should be taken over by the vehicles. Autonomous electric vehicles will require the capability to make charging decisions according to the battery energy state, the travel demand and also the available charging infrastructure. The introduction of autonomous driving technology would remove the challenge of co-locating charging infrastructure with driver destinations and presents a driver-free method for EVs to reach nearby charging stations. This will significantly change the charging behavior of electric vehicles. EV driver will no longer need to be present at charging stations for charging actions. Automated EVs can drive to nearby charging stations to perform charging actions by themselves when necessary. Meanwhile, connected vehicles technology makes electric vehicles have the capability to sense and obtain pertinent information from nearby charging station networks and then calculate the corresponding costs and availability for charging. All these promising capabilities bring new opportunities to construct an automatic charging decision making system and improve energy efficiency of charging actions for automatic electric vehicles. Abstract: Methodology This research introduces optimization tools and case studies on charging decision making for automated electric vehicles under personal usage scenario. The designed optimization tools aim to provide charging strategies, i.e. the optimal choice of charging station outside home and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. Abundant real world EV data is utilized to construct stochastic energy consumption prediction model under realistic driving conditions. Advanced dynamic programming technology is utilized to model and solve the potential multi-stage decision making process by considering realistic uncertainties of EV energy cost and dynamics of itinerary information. Large-scale simulation studies using Chicago itinerary data are demonstrated to illustrate energy benefits from automatic charging decision making for future autonomous EVs. Abstract: Results/Conclusions Optimal automatic charging decision making and its energy benefits have been studied for personal automated electric vehicles. A data-driven method based on large amount of real-world data and the corresponding real-time update algorithm have been proposed to construct multi-channel stochastic energy consumption prediction model. Based on the energy cost prediction, dynamic multi-stage charging decision-making models are established for optimal strategies during a daily itinerary. Derived charging strategies can minimize the monetary and energy cost of charging actions for autonomous vehicles. Simulations and case studies show the potential ability of autonomous electric vehicles to reduce the range anxiety and charging infrastructure dependency. Automatic charging decision making can mitigate the pain from electric vehicle charging necessity outside home. In conclusion, automated electric vehicles equipped with an appropriate automatic charging decision making system will help EV driver to relieve from heavy time cost of recharging, reduce the energy cost of performing charging actions and improve the energy efficiency in future electrified transportation system.
Analysis of Fast Charging Station Network for Electrified Ride-Hailing Services
Eric Wood, Clement Rames, Eleftheria Kontou, Yutaka Motoaki, John Smart, and Zhi Zhou
Today’s electric vehicle (EV) owners charge their vehicles mostly at home and seldom use public direct current fast charger (DCFCs), reducing the need for a large deployment of DCFCs for private EV owners. However, due to the emerging interest among transportation network companies to operate EVs in their fleet, there is great potential for DCFCs to be highly utilized and become economically feasible in the future. This paper describes a heuristic algorithm to emulate operation of EVs within a hypothetical transportation network company fleet using a large global positioning system data set from Columbus, Ohio. DCFC requirements supporting operation of EVs are estimated using the Electric Vehicle Infrastructure Projection tool. Operation and installation costs were estimated using real-world data to assess the economic feasibility of the recommended fast charging stations. Results suggest that the hypothetical transportation network company fleet increases daily vehicle miles traveled per EV with less overall down time, resulting in increased demand for DCFC. Sites with overhead service lines are recommended for hosting DCFC stations to minimize the need for trenching underground service lines. A negative relationship was found between cost per unit of energy and fast charging utilization, underscoring the importance of prioritizing utilization over installation costs when siting DCFC stations. Although this preliminary analysis of the impacts of new mobility paradigms on alternative fueling infrastructure requirements has produced several key results, the complexity of the problem warrants further investigation.
Energy Impact Evaluation for Eco-Routing and Charging of Autonomous Electric Vehicle Fleet: Ambient Temperature Consideration
Zonggen Yi, John Smart, Matthew Shirk
This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV's trip level energy management in a shared fleet. The decision-making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.
Energy Efficient Mobility Systems: The US DOE's Research on SMART Mobility - Advanced Fueling Infrastructure Pillar
Zonggen Yi, Matthew Shirk
This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.
Energy Efficient Mobility Systems: The US DOE's Research on SMART Mobility - Advanced Fueling Infrastructure Pillar
Energy Efficient Mobility Systems: Overview of the SMART Mobility - Advanced Fueling Infrastructure Pillar
Consumer Behavioral Adaption in EV Fast Charging Through Pricing
Yutaka Motoaki, Matthew Shirk
Despite recent developments surrounding fast electric vehicle charging and an ever-growing interest in research, little is known about how people actually use direct current fast chargers (DCFC) or how different pricing may affect their recharging behavior. Understanding consumer behavior in DCFC usage is critical to successful deployment of DCFC and economical pricing of the service usage. This paper analyzes real-world field data to examine DCFC usage in the United States. In particular, it examines changes in recharging behavior between periods when the charging service was free and when it was not. Results from this study show evidence that a flat-rate fee has a negative effect on the usage efficiency of DCFC stations.
INTERFUEL: EPAct AFV Compliance Analysis
FAST: Continuing the Focus on Data Quality
FAST: Using Federal Fleet Data for Decision-Making
Tim Raczek, Ron Stewart
FAST: FY 2018 Federal Fleet Data Call: Looking at Data Quality
Ron Stewart, Tim Raczek
FAST: FY 2018 Fleet Data Call: A Preliminary Review
FAST: FY 2018 Federal Fleet Data Call
Ron Stewart, Tim Raczek
Federal Automotive Statistical Tool: Asset-Level Data - Lessons Learned and Looking Forward
FAST and Asset-Level Data: Lessons Learned and Insights
FAST: Asset-Level Data Reporting
Concept Design of Active Shielding for Dynamic Wireless Charging of Light-duty EV
Bo Zhang, Richard Carlson, Shawn Salisbury, Charles Dickerson, Timothy Pennington, Lee Walker and Eric Dufek
Dynamic wireless charging of electric vehicles is a flexible and state of the art charging technology with the potential capability of enabling fully automated in-motion charging. With charging power increasing to more than 100 kW for light duty vehicles, electromagnetic field (EMF) emission becomes a critical challenge. Due to the high costs of ferrite materials, this paper proposes an active shielding solution with multiple canceling coils installed on the ground side to supplement ferrite passive shielding to ensure electromagnetic safety. Two canceling coils are designed on two sides of the ground side coil. The canceling coils are small in size and 180 degree opposite in phase to the ground coil. Simulation and modeling shows that the canceling coils can reduce EMF emission from 37.2 μT to 18.2 μT at 0.8 m during 100 kW operation with only 2.5% of ground side current flowing in the canceling coils. These results have been preliminarily verified by inductance measurements and magnetic field measurement at 1.1 m distance without canceling coils. By further increasing the canceling coils’ currents to 4%, EMF emission can also be mitigated at 200 kW, although the canceling coils’ shape, position, and phase angle can be further optimized to improve the three-dimensional field distribution.
A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations
Zonggen Yi, Don Scoffield, John Smart, Andrew Meintz, Myungsoo Jun, Manish Mohanpurkar, Anudeep Medam
The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve global coordination benefits. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of charging stations over multiple years. Case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.
Challenges of Future High Power Wireless Power Transfer for Light-Duty Electric Vehicles—Technology and Risk Management
Bo Zhang, Richard Carlson, John Smart, Eric Dufek, Boryann Liaw
Wireless power transfer is a state-of-the-art technology with the advantage of convenience and flexibility, as well as the capability of enabling fully automated charging. With power transfer levels increasing beyond 100 kW, many technical and risk management challenges emerge. Instead of focusing on the progress review of wireless power transfer technology, this paper discusses the future challenges and risks for high power wireless charging for light-duty electric vehicles. Aiming at 200 kW or higher wireless power transfer, we discuss technology and risk management challenges in the area of electromagnetic safety, resonant frequency determination, and cybersecurity risks in detail. As potential solutions to overcome these challenges, the latest technologies with an emphasis on advanced shielding solutions and recent progress on SiC converters from both industrial and academic institutions are reviewed, and some perspectives and propositions are provided.
A Data-Driven Framework for Residential Electric Vehicle Charging Load Profile Generation
Zonggen Yi, Don Scoffield
Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due to emerging electrified transportation. This paper introduces a data-driven framework of charging load profile generation for residential plug-in electric vehicles. Real world historical residential charging behavior data is utilized to construct empirical charging decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation is proposed to construct probability density functions for estimating charging duration based on parking duration. A generation algorithm considering parking time and travel demand dependency is introduced to generate residential charging behaviors. This framework is extensible to generate various charging load profiles and simulate varied residential charging scenarios under different number of households and charging rates. This will be crucial for designing and validating residential charging control strategies.
Potential for Plug-In Electric Vehicles to provide grid support services
F.G. Dias, Y. Luo, M. Mohanpurkar, R. Hovsapian, D. Scoffield
Since the introduction of Plug-in Electric Vehicles (PEVs), scientists have proposed leveraging PEV battery pack as distributed energy resources for the electric grid. PEV charging can be controlled not only to provide energy for transportation but also to provide grid services and to facilitate the integration of renewable energy generation. With renewable generation increasing at an unprecedented rate, most of which is non-dispatchable and intermittent, the concept of using PEVs as controllable loads is appealing to electric utilities. If incentivized suitably, this could serve as an additional driver for PEV adoption. It has been widely proposed that PEVs can provide valuable grid services, such as load shifting to provide voltage and frequency regulation. The objective this work is to address the degree to which PEVs can provide grid services and mutually benefit the electric utilities, PEV owners, and auto manufacturers.
Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles
Myriam Neaimeh, Shawn Salisbury, Graeme Hill, Philip Blythe, Don Scoffield, James Francfort
An appropriate charging infrastructure is one of the key aspects needed to support the mass adoption of battery electric vehicles (BEVs), and it is suggested that publically available fast chargers could play a key role in this infrastructure. As fast charging is a relatively new technology, very little research is conducted on the topic using real world datasets, and it is of utmost importance to measure actual usage of this technology and provide evidence on its importance to properly inform infrastructure planning. 90,000 fast charge events collected from the first large-scale roll-outs and evaluation projects of fast charging infrastructure in the UK and the US and 12,700 driving days collected from 35 BEVs in the UK were analysed. Using regression analysis, we examined the relationship between daily driving distance and standard and fast charging and demonstrated that fast chargers are more influential. Fast chargers enabled using BEVs on journeys above their single-charge range that would have been impractical using standard chargers. Fast chargers can help overcome perceived and actual range barriers and make BEVs more attractive to future users. At current BEV market share, there is a vital need for policy support to accelerate the development of fast charge networks.
Levelized Cost of Charging – A Detailed Assessment of Fuel Costs for Electric Vehicles in the United States
Brennan Borlaug, Shawn Salisbury, Mindy Gerdes, Matteo Muratori
Cost is a major driver of vehicle adoption and while much emphasis has been placed on the high purchase price associated with electric vehicles (EVs), it is important to also consider operating costs, including fuel. The cost to charge an EV varies depending on the price of electricity at different charging sites (home, workplace, public), vehicle use, by region and time-of-day, and for different charging power levels and equipment/installation costs. Despite this, most studies assume a single cost for EV charging. This paper provides a detailed assessment of the current levelized cost of charging (LCOC) in the United States, considering when, where, and how EVs are charged. The LCOC includes costs associated with the purchase and installation of charging equipment and retail electricity prices, derived from real-world utility tariffs. To contextualize the LCOC, we estimate lifetime fuel cost savings, comparing refueling costs for EVs to conventional gasoline vehicles over a 15-year time horizon.
Location-Allocation of Electric Vehicle Fast Chargers—Research and Practice
This paper conducts a comparative analysis of academic research on location-allocation of electric vehicle fast chargers into the pattern of the actual fast-charger allocation in the United States. The work aims to highlight the gap between academic research and actual practice of charging-station placement and operation. It presents evidence that the node-serving approach is, in fact, applied in the actual location-allocation of fast charging stations. However, little evidence suggests that flow-capturing, which has been much more predominantly applied in research, is being applied in practice. The author argues that a large-scale location-allocation plan for public fast chargers should be formulated based on explicit consideration of stakeholders, the objective, practical constraints, and underlining assumptions.
Cybersecurity Overview of Wireless Power Transfer (WPT) for Electrified Transportation
Consequence-driven Cybersecurity for High Power EV Charging Infrastructure
Richard Carlson, Ken Rohde
Technology Solutions to Mitigate Electricity Cost for Electric Vehicle DC Fast Charging
Matteo Muratori, Emma Elgqvist, Dylan Cutler, Joshua Eichman, Shawn Salisbury, Zachary Fuller, John Smart
Widespread adoption of alternative fuel vehicles is being hindered by high vehicle costs and refueling or range limitations. For plug-in electric vehicles, direct-current fast charging (DCFC) is proposed as a solution to support long-distance travel and relieve range anxiety. However, DCFC has also been shown to be potentially more expensive compared to residential or workplace charging. In particular, electricity demand charges can significantly impact electricity cost for fast charging applications. Here we explore technological solutions that can help reduce the electricity cost for electric vehicle fast charging. In particular, we consider thousands of electricity rates available in the United States and real-world vehicle charging load scenarios to assess opportunities to reduce the cost of DCFC by deploying solar photovoltaics (PV) panels and energy storage (battery), and implementing a co-location configuration where a DCFC station is connected to an existing meter within a commercial building. Results show that while the median electricity cost across more than 7,000 commercial retail rates remains less than $0.20/kWh for all charging load scenarios considered, cost varies greatly, and some locations do experience significantly higher electricity cost. Co-location is almost always economically viable to mitigate fixed cost and demand charges, but the relative benefit of co-locating diminishes as station size and utilization increase. Energy storage alone can help mitigate demand charges and is more effective at reducing costs for “peaky” or low-utilization loads. On the other hand, PV systems primarily help mitigate energy charges, and are more effective for loads that are more correlated with solar production, even in areas with lower solar resource. PV and energy storage can deploy synergistically to provide cost reductions for DCFC, leveraging their ability to mitigate demand and energy charges.
Extreme Fast Charging - Status and Implications
Tanvir Tanim, Eric Dufek, Michael Evans, Ryan Jackman, Matt Shirk, and Boryann Liaw
Considerations for DC Fast Charger Complex System Design
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