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Ford's thermal management with batteries. Presenter Robert Taenaka, Ford Motor Co.

The Focus Electric is Ford�s first full-featured 5 passenger battery electric vehicle. The engineering team set our sights on achieving best-in-class function and efficiency and was successful with an EPA certified 1XX MPGe and range XXX then the facing competition allowing for a slightly lower capacity battery pack and larger vehicle without customer trade-off. We briefly overview the engineering method and technologies employed to deliver the results as well as sharing some of the functional challenges unique to this type of vehicle. Presenter Charles Gray, Ford Motor Co.

The presentation offers a brief history of the electric vehicle and parallels the realities of those early vehicles with the challenges and solutions of the electrified vehicles coming to market today. A technology evolution for every major component of these vehicles has now made this mode of transportation viable. The Focus Electric is Ford's first electric passenger car utilizing the advanced technology developments to meet the needs of electric car buyers in this emerging market. Presenter Charles Gray, Ford Motor Co.

Plug In Charging Systems are mainly responsible for transferring energy from the electric power grid into one or more vehicle energy storage devices (e.g. batteries). A satisfactorily operating Plug in Charging System has the following three key performance characteristics. First, the charge process starts up easily. Second, it completes the charge process within some expected time. Third, it charges efficiently so that excessive amounts of power are not wasted. When a Plug In Charging System malfunction exists and negatively affects one or more of these key performance criteria, it is the responsibility of the OBD monitoring system to identify the fault and notify the customer. The presentation will discuss the key performance characteristics described above and some of the diagnostic strategies used to detect faults. The discussion will also include an overview of MIL illumination and freeze frame storage capabilities.

New European emissions legislation (Euro5) specifies a limit for Particle Number (PN) emissions and therefore drives measurement of PN during vehicle development and homologation. Concurrently, the use of biofuel is increasing in the marketplace, and Euro5 specifies that reference fuel must contain a bio-derived portion. Work was carried out to test the effect of fuels containing different levels of Fatty Acid Methyl Ester (FAME) on particle number, size, mass and composition. Measurements were conducted with a Cambustion Differential Mobility Spectrometer (DMS) to time-resolve sub-micron particles (5-1000nm), and a Horiba Solid Particle Counting System (SPCS) providing PN data from a Euro5-compliant measurement system. To ensure the findings are relevant to the modern automotive business, testing was carried out on a Euro4 compliant passenger car fitted with a high-pressure common-rail diesel engine and using standard homologation procedures.

Gasoline turbocharged direct injection (GTDI) engines, such as EcoBoost™ from Ford, are becoming established as a high value technology solution to improve passenger car and light truck fuel economy. Due to their high specific performance and excellent low-speed torque, improved fuel economy can be realized due to downsizing and downspeeding without sacrificing performance and driveability while meeting the most stringent future emissions standards with an inexpensive three-way catalyst. A logical and synergistic extension of the EcoBoost™ strategy is the use of E85 (approximately 85% ethanol and 15% gasoline) for knock mitigation. Direct injection of E85 is very effective in suppressing knock due to ethanol's high heat of vaporization - which increases the charge cooling benefit of direct injection - and inherently high octane rating. As a result, higher boost levels can be achieved while maintaining optimal combustion phasing giving high thermal efficiency.

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

The compressive behavior of lithium-iron phosphate battery cells is investigated by conducting in-plane constrained compression tests and out-of-plane compression tests of representative volume element (RVE) specimens. The results for cell RVE specimens under in-plane constrained compression tests without pre-strains and with pre-strains in the out-of-plane direction indicate that the load carrying capacity is characterized by the buckling of cell specimens. As the pre-strain increases, the nominal compressive stress-strain curve becomes higher. The nominal stress-strain curves in the out-of-plane direction were also obtained and used to determine the elastic moduli for the elastic buckling analyses of the cell components in the cell RVE specimens with different pre-strains. Based on the elastic buckling analyses for a beam with different lateral constraints due to different pre-strains in the out-of-plane direction, the number of half waves and the buckling stresses were obtained.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

Torque controls for the engine and electric motors in a Powersplit HEV are keys to the success of balancing fuel economy, driveability, and battery power control. The electric variable transmission (EVT) offers an opportunity to let the engine operate at system-optimal fuel efficient points independently of any load. Existing work shows such a benefit can be realized through a decentralized control structure that translates the driver inputs to independent engine torque and speed control. However, our study shows that the decentralized control structures have a fundamental limitation that arises from the nonminimum phase (NMP) zero in the transfer function from the driver power command to the generator torque change rate, and thus not only is it difficult to obtain smooth generator torque but also it can cause violations on battery power limits during transients. Additionally, it adversely affects the driveability due to the generator torque transients reflected at the ring gear.

As automakers strategize approaches to sustainable vehicle technologies, alternative powertrains must be considered to reduce future fleet vehicle emissions and improve energy security. These alternative vehicles include different fuels and electrification. The ultimate for on-road CO2 reductions is a zero emission vehicle, which can be achieved by either a hydrogen fuel cell or battery electric vehicle. These vehicles would also require a renewable energy source to provide their propulsion energy in order to achieve maximum sustainability for both CO2 reduction and energy security. Renewable energy sources such as wind or solar result in heat or electricity that needs to be generated into an energy carrier such as hydrogen or stored in a battery. When examining these options based strictly on the efficiency path, previous analysis have concluded fuel cell vehicles may not be an appropriate suitability strategy in comparison to battery electric vehicles.

During the 1980s, the U.S. Environmental Protection Agency (EPA) incorporated the R factor into fuel economy calculations in order to address concerns about the impacts of test fuel property variations on corporate average fuel economy (CAFE) compliance, which is determined using the Federal Test Procedure (FTP) and Highway Fuel Economy Test (HFET) cycles. The R factor is defined as the ratio of the percent change in fuel economy to the percent change in volumetric heating value for tests conducted using two differing fuels. At the time the R-factor was devised, tests using representative vehicles initially indicated that an appropriate value for the R factor was 0.6. Reassessing the R factor has recently come under renewed interest after EPA's March 2013 proposal to adjust the properties of certification gasoline to contain significant amounts of ethanol.

A single-cylinder Direct Injection Spark Ignition (DISI) engine with optical access was used to investigate the effects of ethanol/gasoline blends on in-cylinder formation of particulate matter (PM) and fuel spray characteristics. Indolene was used as a baseline fuel and two blends of 50% and 85% ethanol (by volume, balance indolene) were investigated. Time resolved thermal radiation (incandescence/natural luminosity) of soot particles and fuel spray characteristics were recorded using a high speed camera. The images were analyzed to quantify soot formation in units of relative image intensity as a function of important engine operating conditions, including ethanol concentration in the fuel, fuel injection timing (250, 300 and 320° bTDC), and coolant temperature (25°C and 90°C). Spatially-integrated incandescence was used as a metric to quantify the level of in-cylinder PM formed at the different operating conditions.

Engine dynamometer testing was performed comparing fuels having different octane ratings and ethanol content in a Ford 3.5L direct injection turbocharged (EcoBoost) engine at three compression ratios (CRs). The fuels included midlevel ethanol “splash blend” and “octane-matched blend” fuels, E10-98RON (U.S. premium), and E85-108RON. For the splash blends, denatured ethanol was added to E10-91RON, which resulted in E20-96RON and E30-101 RON. For the octane-matched blends, gasoline blendstocks were formulated to maintain constant RON and MON for E10, E20, and E30. The match blend E20-91RON and E30-91RON showed no knock benefit compared to the baseline E10-91RON fuel. However, the splash blend E20-96RON and E10-98RON enabled 11.9:1 CR with similar knock performance to E10-91RON at 10:1 CR. The splash blend E30-101RON enabled 13:1 CR with better knock performance than E10-91RON at 10:1 CR. As expected, E85-108RON exhibited dramatically better knock performance than E30-101RON.

This paper presents engine dynamometer testing and modeling analysis of ethanol compared to gasoline at part load conditions where the engine was not knock-limited with either fuel. The purpose of this work was to confirm the efficiency improvement for ethanol reported in published papers, and to quantify the components of the improvement. Testing comparing E85 to E0 gasoline was conducted in an alternating back-to-back manner with multiple data points for each fuel to establish high confidence in the measured results. Approximately 4% relative improvement in brake thermal efficiency (BTE) was measured at three speed-load points. Effects on BTE due to pumping work and emissions were quantified based on the measured engine data, and accounted for only a small portion of the difference.

Ford Motor Company is researching and developing multiple propulsion strategies which include advanced gasoline engines, clean diesel, flexible fuel (ethanol blends up to E-85), hybrids and hydrogen propulsion, both in internal combustion (IC) engines and fuel cells. Hydrogen utilized as a transportation fuel is viewed as a long term solution as it is sustainable and clean when derived from renewable resources. The development and use of hydrogen IC engine (H2ICE) technology can readily be utilized to drive the transition strategy from the petroleum economy to the hydrogen economy. Because the “more conventional” H2ICE systems can be brought to market more quickly and in higher volume, business initiatives for hydrogen fueling infrastructure and other hydrogen complimentary required technologies can be realized sooner. To that end Ford has fully re-engineered a 6.8L Triton V-10 engine to run on hydrogen and power an E-450 shuttle van.

Until now no results have been available regarding the composition of evaporative emissions in a SHED test. In particular, for alcohol containing fuels, it is important to assess the relative percentage of alcohols and hydrocarbons in view of their different environmental impacts. This paper presents the results of a study conducted to determine the composition of the emissions from a number of multilayer coextruded plastic fuel tanks soaked in IE10 and CM15 test fuels. These emissions were analyzed for composition using a gas chromatography analytical method which employs a vapor trap and desorb sampling technique. In the case of CM15, methanol was found to account for as much as 50% of the overall evaporative emissions. This speciation method also allows estimation of how leakage and permeation contribute separately to the overall emissions.

A life-cycle assessment (LCA) has been developed to help compare the economic, environmental and energy (EEE) impacts of converting coal to automotive fuels in China. This model was used to evaluate the total economic cost to the customer, the effect on the local and global environments, and the energy efficiencies for each fuel option. It provides a total accounting for each step in the life cycle process including the mining and transportation of coal, the conversion of coal to fuel, fuel distribution, all materials and manufacturing processes used to produce a vehicle, and vehicle operation over the life of the vehicle. The seven fuel scenarios evaluated in this study include methanol from coal, byproduct methanol from coal, methanol from methane, methanol from coke oven gas, gasoline from coal, electricity from coal, and petroleum to gasoline and diesel. The LCA results for all fuels were compared to gasoline as a baseline case.

This paper is the first in a series of documents designed to record the progress of the SAE J2293 Task Force as it continues to develop and refine the communication requirements between Plug-In Electric Vehicles (PEV) and the Electric Utility Grid. In February, 2008 the SAE Task Force was formed and it started by reviewing the existing SAE J2293 standard, which was originally developed by the Electric Vehicle (EV) Charging Controls Task Force in the 1990s. This legacy standard identified the communication requirements between the Electric Vehicle (EV) and the EV Supply Equipment (EVSE), including off-board charging systems necessary to transfer DC energy to the vehicle. It was apparent at the first Task Force meeting that the communications requirements between the PEV and utility grid being proposed by industry stakeholders were vastly different in the type of communications and messaging documented in the original standard.

The Auto Industry is responding to the environment and energy conservation concerns by ramping up production of hybrid electric vehicles (HEV). As the initial hurdles of making the powertrain operate are overcome, challenges such as making the powertrain feel more refined and intuitive remain. This paper investigates one of the key parameters for delivering that refinement: engine RPM behavior. Ideal RPM behavior is explored and included in the design of a control system. System implications are examined with regard to the effect of engine RPM scheduling on Battery usage and vehicle responsiveness.