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This paper explores the technical and economic opportunities and issues that exist to provide demand response to the electricity grid from an aggregated network of electric vehicle supply equipment. Using data from the roll out of a large scale electric vehicle charging network in the U.S., and a review of information on the current demand response programs on offer in the U.S., issues such as the recruitment of electric vehicle charging units and users, the required control and communication capabilities of electric vehicle supply equipment and back-office network, and an example of the associated revenue potential are discussed. Finally, a number of recommendations for an aggregated network of electric vehicle supply equipment to provide demand response to the electricity grid are presented.

The U.S. Department of Energy’s Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles, including testing hybrid electric vehicle batteries when both the vehicles and batteries are new and at the conclusion of 160,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Hyundai Sonata Hybrid (VIN KMHEC4A47BA003539). Battery testing was performed by Intertek Testing Services NA. The Idaho National Laboratory and Intertek collaborate on the Advanced Vehicle Testing Activity for the Vehicle Technologies Program of the U.S. Department of Energy.

As energy storage system (ESS) technology advances, vehicle testing in both laboratory and on-road settings is needed to characterize the performance of state-of-the-art technology and also identify areas for future improvement. The Idaho National Laboratory (INL), through its support of the U.S. Department of Energy’s (DOE) Advanced Vehicle Testing Activity (AVTA), is collaborating with ECOtality North America and Oak Ridge National Laboratory (ORNL) to conduct on-road testing of advanced ESSs for the Electric Drive Advanced Battery (EDAB) project. The project objective is to test a variety of advanced ESSs that are close to commercialization in a controlled environment that simulates usage within the intended application with the variability of on-road driving to quantify the ESS capabilities, limitations, and performance fade over cycling of the ESS. To accommodate on-road testing of a wide range of ESS size, mass, and intended applications, the EDAB testbed was constructed on a mid-sized pickup truck chassis. This truck was converted into a Series Plug-In Hybrid Electric Vehicle (PHEV) which enables vehicle operation consistent with any electrified vehicle. Sophisticated software algorithms were prepared and integrated into the testbed to emulate the physical characteristics and ESS demands of the intended application during on-road operation. This emulation is vital for proper ESS operation since the testbed is larger and heavier than the vehicle for which the ESS is typically designed. On-road testing is conducted over a range of ambient temperatures and driving route types ranging from ‘stop-and-go’ city driving to constant-speed highway driving. Battery laboratory cycling with standard test procedures has been conducted throughout all phases of testing to corroborate the on-road data and accurately measure the ESS degradation. The first ESS to be tested is the Type I EV Pack manufactured by EnerDel, Inc. The ESS has a Li-ion chemistry, with a mixed-oxide cathode and amorphous hard carbon anode and a rated capacity of 70 Ah (at a C/3 rate). Due to the sealed enclosure, there is no internal thermal management system (TMS). The intended application for this ESS is for a small EV. This paper will report on current results of energy consumption, city vs. highway proportions, battery throughput, and laboratory testing results. The results illustrate the performance of the unit under test and the degradation throughout. The end-of-test criteria are 100,000 miles, three years of operation, or a 23% decrease in battery capacity, whichever occurs first.

Design optimization is performed by presenting a systematic method to obtain the optimal operating conditions of a Proton Exchange Membrane (PEM) fuel cell system targeted towards a vehicular application. The fuel cell stack model is a modified version of the semi-empirical model introduced by researchers at the Royal Military College of Canada and one that is widely used by industry. Empirical data obtained from tests of PEM fuel cell stacks are used to determine the empirical parameters of the fuel cell performance model. Based on this stack model, a fuel cell system model is built in MATLAB. Included in the system model are heat transfer and gas flow considerations and the associated Balance of Plant (BOP) components. The modified ADVISOR vehicle simulation tool is used to integrate the New York City Cycle (NYCC) drive cycle and vehicle model to determine the power requirements and hence the load cycle of the fuel cell system for a low-speed fuel cell hybrid electric vehicle (LSFCHEV). The optimization of the powerplant of this vehicle type is unique. The vehicle model has been developed in the work to describe the characteristics and performance of an electric scooter, a simple low-speed vehicle (LSV). The net output power and system exergetic efficiency of the system are maximized for various system operating conditions using the weighted objective function based on the load cycle requirement. The method is based on the coupling of the fuel cell system model with three optimization algorithms (a) sequential quadratic programming (SQP); (b) simulated annealing (SA); and (c) genetic algorithm (GA). The results of the optimization provide useful information that will be used in future study on control algorithms for LSFCHEVs. This study facilitates research on more complex fuel cell system modeling and optimization, and provides a basis for experimentation to verify the fuel cell system model.Copyright © 2006 by ASME

Further improvements of hybrid electric vehicle (HEV) with advanced, next generation powertrain design require design optimization using advanced computer powertrain modeling tools. This work explores new hybrid powertrain architectures using advanced multi-physics modeling tools, and vehicle performance simulation based design optimization. The optimization problem is formulated using fuel consumption minimization as design objective, and battery SOC and driving cycle as functional constraints. The multi-physics and hybrid vehicle modeling tools from Dymola are used to model and simulate the vehicle performance and fuel consumption of a template commercial hybrid electric vehicle with similar powertrain structure as the 2008 GM Chevrolet Tahoe Hybrid SUV. The optimization tool box in Dymola is used to carry out the preliminary control strategy optimization. This study provides guidelines for introducing more advanced hybrid powertrain architectures and control algorithms for HEVs.

This document reports the work performed under Task 1.2.1.1: 'The development of a charge protocol to take advantage of off- and on-peak demand economics at facilities'. The work involved in this task included understanding the experimental results of the other tasks of SOW-5799 in order to take advantage of the economics of electricity pricing differences between on- and off-peak hours and the demonstrated charging and facility energy demand profiles. To undertake this task and to demonstrate the feasibility of plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) bi-directional electricity exchange potential, BEA has subcontracted Electric Transportation Applications (now known as ECOtality North America and hereafter ECOtality NA) to use the data from the demand and energy study to focus on reducing the electrical power demand of the charging facility. The use of delayed charging as well as vehicle-to-grid (V2G) and vehicle-to-building (V2B) operations were to be considered.