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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.

As Electric and Hybrid Electric Vehicles (EVs and HEVs) become more prevalent, there is a need to change the power source from gasoline on the vehicle to electricity from the grid in order to mitigate requirements for onboard energy storage (battery weight) as well as to reduce dependency on oil by increasing dependency on the grid (our coal, gas, and renewable energy instead of their oil). Traditional systems for trains and buses rely on physical contact to transfer electrical energy to vehicles in motion. Until recently, conventional magnetically coupled systems required a gap of less than a centimeter. This is not practical for vehicles of the future.

The question of whether increasing the fuel economy of light-duty natural gas fueled vehicles can improve their economic competitiveness in the U.S. market, and help the US Department of Energy meet stated goals for such vehicles is explored. Key trade-offs concerning costs, exhaust emissions and other issues are presented for a number of possible advanced engine designs. Projections of fuel economy improvements for a wide range of lean-burn engine technologies have been developed. It appears that compression ignition technologies can give the best potential fuel economy, but are less competitive for light-duty vehicles due to high engine cost. Lean-burn spark ignition technologies are more applicable to light-duty vehicles due to lower overall cost. Meeting Ultra-Low Emission Vehicle standards with efficient lean-burn natural gas engines is a key challenge.

Nyquist plots are a classical means to visualize a complex vibration frequency response function. By graphing the real and imaginary parts of the response, the dynamic behavior in the vicinity of resonances is emphasized. This allows insight into how modes are coupling, and also provides a means to separate the modes. Mathematical models such as Nyquist analysis are often embedded in frequency analysis hardware. While this speeds data collection, it also removes this visually intuitive tool from the engineer’s consciousness. The behavior of a single degree of freedom system will be shown to be well described by a circle on its Nyquist plot. This observation allows simple visual examination of the response of a continuous system, and the determination of quantities such as modal natural frequencies, damping factors, and modes shapes. Vibration test data from an auto rickshaw chassis are used as an example application.

A GT-SUITE vehicle-aftertreatment model has been developed to examine the cold-start emissions reduction capabilities of a Vacuum Insulated Catalytic Converter (VICC). This converter features a thermal management system to maintain the catalyst monolith above its light-off temperature between trips so that most of a vehicle’s cold-start exhaust emissions are avoided. The VICC thermal management system uses vacuum insulation around the monoliths. To further boost its heat retention capacity, a metal phase-change material (PCM) is packaged between the monoliths and vacuum insulation. To prevent overheating of the converter during periods of long, heavy engine use, a few grams of metal hydride charged with hydrogen are attached to the hot side of the vacuum insulation. The GT-SUITE model successfully incorporated the transient heat transfer effects of the PCM using the effective heat capacity method.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.

The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

The emission of excessive quantities of NOx when the automobile air conditioner is turned on has received a fair amount of attention in recent years. Since NOx is a smog precursor, it is important to understand the reasons for this jump in emissions especially on hot sunny days when air conditioner usage is at a maximum. A simple thermodynamic model is used to demonstrate how the torque from a typical air conditioner compressor is mainly related to the ambient temperature. The compressor's on-off cycling patterns are also characterized. Since the compressor significantly loads the engine, it affects fuel economy and emissions. The key independent variable that we employ to represent engine load is fuel rate. The correlations between engine-out NOx emissions and fuel rate are shown for a number of light duty vehicles and trucks. From these, a physical model for engine-out NOx emissions (with and without air conditioning) is presented.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

Naturally aspirated HCCI operation is typically limited to medium load operation (∼ 5 bar net IMEP) by excessive pressure rise rate. Boosting can provide the means to extend the HCCI range to higher loads. Recently, it has been shown that HCCI can achieve loads of up to 16.3 bar of gross IMEP by boosting the intake pressure to more than 3 bar, using externally driven compressors. However, investigating HCCI performance over the entire speed-load range with real turbocharger systems still remains an open topic for research. A 1 - D simulation of a 4 - cylinder 2.0 liter engine model operated in HCCI mode was used to match it with off-the-shelf turbocharger systems. The engine and turbocharger system was simulated to identify maximum load limits over a range of engine speeds. Low exhaust enthalpy due to the low temperatures that are characteristic of HCCI combustion caused increased back-pressure and high pumping losses and demanded the use of a small and more efficient turbocharger.

The friction and wear characteristics of three commercially-available, electrolytic coatings for aluminum engine cylinder bores were compared to those of cast iron liners. A Ni/SiC electrocomposite, a hard anodized treatment, and a Plasma Electrolytic Oxidation (PEO) coating were investigated. ASTM standard test method G133-95, non-firing test method, for linearly reciprocating sliding wear was modified to use segments of piston rings and cylinder liners. Tests were conducted using Mr. Goodwrench™ 5W30 as a lubricant at room temperature. The normal force was 150N, the reciprocating frequency was 15Hz, the stroke length was 8mm, and the test duration was 60 minutes. Kinetic friction coefficients ranged from 0.1 to 0.22, typical of boundary lubrication. The Ni/SiC and cast iron samples exhibited the lowest friction. The wear resistance of the Ni/SiC coating was superior to that of cast iron.

Laser-induced incandescence is used to measure time-resolved diesel particulate emissions for two lean NOx trap regeneration strategies that utilize intake throttling and in-cylinder fuel enrichment. The results show that when the main injection event is increased in duration and delayed 13 crank-angle degrees, particulate emissions are very high. For a repetitive pattern of 3 seconds of rich regeneration followed by 27 seconds of NOx-trap loading, we find a monotonic increase in particulate emissions during the loading intervals that approaches twice the initial baseline particulate level after 1000 seconds. In contrast, particulate emissions during the regeneration intervals are constant throughout the test sequence.

Plenoptic particle tracking velocimetry (PTV) shows great potential for three-dimensional, three-component (3D3C) flow measurement with a simple single-camera setup. It is therefore especially promising for applications in systems with limited optical access, such as internal combustion engines. The 3D visualization of a plenoptic imaging system is achieved by inserting a micro-lens array directly anterior to the camera sensor. The depth is calculated from reconstruction of the resulting multi-angle view sub-images. With the present study, we demonstrate the application of a plenoptic system for 3D3C PTV measurement of engine-like air flow in a steady-state engine flow bench. This system consists of a plenoptic camera and a dual-cavity pulsed laser. The accuracy of the plenoptic PTV system was assessed using a dot target moved by a known displacement between two PTV frames.

Semi-volatile species in the exhaust can condense on the primary particulate matter (PM) forming significant secondary PM mass downstream1. We developed a new thermographic technique to measure the volatility of a particle population. The instrument is called vapor-particle separator (VPS)2. A two-parameter model was used to interpret the thermographic data3. These two parameters define volatilization potential and thermodynamic capacity of the particles. The volatization potential delineates the unique particle volatility, while the thermodynamic capacity illustrates the work required to eliminate the particles. The thermodynamic capacity is found much smaller for small particles than that for large particles.