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Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

This paper considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle.

The CRC Fuels for Advanced Combustion Engines (FACE) Working Group has provided a matrix of experimental diesel fuels for use in studies on the effects of three parameters, Cetane number (CN), aromatics content, and 90 vol% distillation temperature (T90), on combustion and emissions characteristics of advanced combustion strategies. Various types of fuel analyses and engine experiments were performed in well-known research institutes. This paper reviews a collection of research findings obtained with these nine fuels. An extensive collection of analyses were performed by members of the FACE working group on the FACE diesel fuels as a means of aiding in understanding the linkage between fuel properties and combustion and emissions performance. These analyses included non-traditional chemical techniques as well as established ASTM tests. In a few cases, both ASTM tests and advanced analyses agreed that some design variables differed from their target values when the fuels were produced.

Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts. ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle.

This paper studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

Conventional methods of vehicle operation for Plug-in Hybrid Vehicles first discharge the battery to a minimum State of Charge (SOC) before switching to charge sustaining operation. This is very demanding on the battery, maximizing the number of trips ending with a depleted battery and maximizing the distance driven on a depleted battery over the vehicle's life. Several methods have been proposed to reduce the number of trips ending with a deeply discharged battery and also eliminate the need for extended driving on a depleted battery. An optimum SOC can be maintained for long battery life before discharging the battery so that the vehicle reaches an electric plug-in destination just as the battery reaches the minimum operating SOC. These “Just-in-Time” methods provide maximum effective battery life while getting virtually the same electricity from the grid.

Diesel particulate samples were collected from a light duty engine operated at a single speed-load point with a range of biodiesel and conventional fuel blends. The oxidation reactivity of the samples was characterized in a laboratory reactor, and BET surface area measurements were made at several points during oxidation of the fixed carbon component of both types of particulate. The fixed carbon component of biodiesel particulate has a significantly higher surface area for the initial stages of oxidation, but the surface areas for the two particulates become similar as fixed carbon oxidation proceeds beyond 40%. When fixed carbon oxidation rates are normalized to total surface area, it is possible to describe the oxidation rates of the fixed carbon portion of both types of particulates with a single set of Arrhenius parameters. The measured surface area evolution during particle oxidation was found to be inconsistent with shrinking sphere oxidation.

This paper presents the damping effectiveness of free-layer damping materials through standard Oberst bar testing, solid plate excitation (RTC3) testing, and prediction through numerical schemes. The main objective is to compare damping results from various industry test methods to performance in an automotive body structure. Existing literature on laboratory and vehicle testing of free-layer viscoelastic damping materials has received significant attention in recent history. This has created considerable confusion regarding the appropriateness of different test methods to measure material properties for damping materials/treatments used in vehicles. The ability to use the material properties calculated in these tests in vehicle CAE models has not been extensively examined. Existing literature regarding theory and testing for different industry standard damping measurement techniques is discussed.

Fuel cell-powered electric vehicles (FCPEV) require an energy storage device to start up the fuel cells and to store the energy captured during regenerative braking. Low-voltage (12 V) batteries are preferred as the storage device to maintain compatibility with the majority of today's automobile loads. A dc/dc converter is therefore needed to interface the low-voltage batteries with the fuel cell-powered higher-voltage dc bus system (255 V ∼ 425 V), transferring energy in either direction as required. This paper presents a soft-switched, isolated bi-directional dc/dc converter developed at Oak Ridge National Laboratory for FCPEV applications. The converter employs dual half-bridges interconnected with an isolation transformer to minimize the number of switching devices and their associated gate drive requirements. Snubber capacitors including the parasitic capacitance of the switching devices and the transformer leakage inductance are utilized to achieve zero-voltage switching (ZVS).

The traditional method of dynamic characterization of elastomers used in industry has largely been based on sinusoidal input excitation. Discrete frequency sine wave signals at specified amplitudes are used to excite the elastomer in a step-sine sweep fashion. This paper will examine new methods of characterization using various broadband input excitations. These different inputs include continuous sine sweep (chirp), shaped random, and acquired road profile data. Use of these broadband data types is expected to provide a more accurate representation of conditions seen in the field, while helping to eliminate much of the interpolation that is inherent with the classic discrete step-sine technique. Results of the various input types are compared in this paper with those found using the classic discrete step-sine input.

The voltage source inverter (VSI) possesses several drawbacks that make it difficult to meet the requirements of automotive applications for inverter volume, lifetime, and cost. The VSI requires a very high performance dc bus capacitor that is costly and bulky. Other characteristics of the VSI not only negatively impact its own reliability but also that of the motor as well as motor efficiency. These problems could be eliminated or significantly mitigated by the use of the current source inverter (CSI). The CSI doesn't require any dc bus capacitors but uses three small ac filter capacitors and an inductor as the energy storage component, thus avoiding many of the drawbacks of the VSI. The CSI offers several inherent advantages that could translate into a substantial reduction in inverter cost and volume, increased reliability, a much higher constant-power speed range, and improved motor efficiency and lifetime.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading.

Measuring axial exhaust species concentration distributions within a wall-flow aftertreatment device provides unique and significant insights regarding the performance of complex devices like the SCR-on-filter. In this particular study, a less complex aftertreatment configuration which includes a DOC followed by two uncoated partial flow filters (PFF) was used to demonstrate the potential and challenges. The PFF design in this study was a particulate filter with alternating open and plugged channels. A SpaciMS [1] instrument was used to measure the axial NO2 profiles within adjacent open and plugged channels of each filter element during an extended passive regeneration event using a full-scale engine and catalyst system. By estimating the mass flow through the open and plugged channels, the axial soot load profile history could be assessed.

A high voltage battery is an essential part of hybrid electric vehicles (HEVs). It is imperative to precisely estimate the state of charge (SOC) and state of health (SOH) of battery in real time to maintain reliable vehicle operating conditions. This paper presents a method of estimating SOC and SOH through the incorporation of current integration, voltage translation, and Ah-throughput. SOC estimation utilizing current integration is inadequate due to the accumulation of errors over the period of usage. Thus voltage translation of SOC is applied to rectify current integration method which improves the accuracy of estimation. Voltage translation data is obtained by subjecting the battery to hybrid pulse power characterization (HPPC) test. The Battery State of Health was determined by semi-empirical model combined with accumulated Ah-throughput method. Battery state of charge was employed as an input to estimate damages accumulated to battery aging through a real-time model.

This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller.