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For automotive applications, it is necessary to maximize the fuel conversion efficiency of a PEM direct-methanol fuel cell (DMFC) over the broadest possible dynamic range of power. The research reported here critically examines the efficiency of the DMFC stack when operated over a broad power range. This research establishes a basis for a control strategy that simultaneously: optimizes DMFC fuel conversion efficiency versus power level, leads into a system level optimization of efficiency vs. power, and provides an operational strategy for controlling a direct-methanol fuel cell for maximum fuel efficiency from minimum to maximum power demand. First, there is an explanation of the experimental conditions used to obtain the DMFC experimental data that is reported and analyzed. Next the DMFC methanol crossover phenomenon is discussed and characterized. Then the conceptual framework for the optimization of fuel conversion efficiency is presented.

For studying the effects of injection system properties and combustion chamber conditions on the penetration lengths of both the liquid and the vapor phase of fuel injectors in Diesel engines, a holistic injection model was developed, combining hydraulic and spray modeling into one integrated simulation tool. The hydraulic system is modeled by using ISIS (Interactive Simulation of Interdisciplinary Systems), a one dimensional in–house code simulating the fuel flow through hydraulic systems. The computed outflow conditions at the nozzle exit, e.g. the dynamic flow rate and the corresponding fuel pressure, are used to link the hydraulic model to a quasi–dimensional spray model. The quasi–dimensional spray model uses semi–empirical 1D correlation functions to calculate spray angle, droplet history and droplet motion as well as penetration lengths of the liquid and the vapor phases. For incorporating droplet vaporization, a single droplet approach has been used.

To provide a feasible transitional solution from all-by-human driving style to fully autonomous driving style, this paper proposed concept and its control algorithm of a robust lane-keeping ‘co-pilot’ system. In this a semi-autonomous system, Learning based Model Predictive Control (LBMPC) theory is employed to improve system's performance in target state tracking accuracy and controller's robustness. Firstly, an approximate LTI model which describes driver-vehicle-road closed-loop system is set up and real system's deviations from the LTI system resulted by uncertainties in the model are regarded as bounded disturbance. The LTI model and bounded disturbances make up a nominal model. Secondly, a time-varying model which is composed of LTI model and an ‘oracle’ component is designed to observe the possible disturbances numerically and it is online updated using Extended Kalman Filter (EKF).

Optimizing/maximizing regen braking in a hybrid electric vehicle (HEV) is one of the key features for increasing fuel economy. However, it is known [1] that maximizing regen braking by braking the rear axle on a low friction surface results in compromising vehicle stability even in a vehicle which is equipped with an ESP (Enhanced Stability Program). In this paper, we develop a strategy to maximize regen braking without compromising vehicle stability. A yaw rate stability control system is designed for a hybrid electric vehicle with electric rear axle drive (ERAD) and a “hang on” center coupling device which can couple the front and rear axles for AWD capabilities. Nonlinear models of the ERAD drivetrain and vehicle are presented using bond graphs while a high fidelity model of the center coupling device is used for simulation.

A torque control policy for four-wheel drive road-going vehicles is developed, based on the use of a compact variable ratio unit (VRU) located at each wheel. Since the appropriate hardware is not yet available, a computer model is developed to examine what gear ratio range and frequency response might be required of the hardware to allow for improved performance and stability over current four-wheel drive systems. A comparison is then made to a front-wheel drive (FWD), rear-wheel drive (RWD) and four-wheel drive (4WD) to determine the effectiveness of the derived control policy.

A novel ambient dilution tunnel has been designed, tested and employed to measure the emissions from active parked regenerations of Diesel Particulate Filters (DPFs) for 2007 and 2010 certified heavy duty diesel trucks (HDDTs). The 2007 certified engine had greater regulated emissions than the 2010 certified engine. For a fully loaded 2007 DPF there was an initial period of very large mass emissions, which was then followed by very large number of small particle emissions. The Particle Size Distribution, PSD, was distributed over a large range from 10 nm to 10 μm. The parked regenerations of the 2010 DPF had a much lower initial emission pattern, but the second phase of large numbers of small particles was very similar to the 2007 DPF. The emission results during regeneration have been compared to total emissions from recent engine dynamometer testing of 2007 and 2010 DPFs, and they are much larger.

An investigation into two new control strategies for the vehicle Anti-lock Braking System (ABS) are made for a possible replacement of current non-optimal slip control methods. This paper applies two techniques in order to maximize the braking force without any wheel locking. The first considers the power dissipated by the brake actuator. This power method does not use slip to construct its reference signal for control. A heuristic approach is taken with this algorithm where one searches for the maximum power dissipated. This can open up easier implementation of regenerative braking concurrently with ABS on an electro-hydraulic braking system. Parameter scheduling is explored in this algorithm. The second algorithm employs the use of perturbation based Extremum Seeking Control (ESC) to provide a reference slip and a Youla controller in a negative feedback loop.

The Oppenheim Correlation (OPC) is a new empirical algorithm, which allows a simple estimate of heat losses to the wall during the combustion in IC-engine. In present paper the results of different applications of OPC will be shown. Even if there are still several needs and ideas for further research it can be stated, that the OPC is a promising possibility of modeling the wall heat losses and due to its simplicity it has to be recommended to the engine community. The OPC can be used not only for didactics purposes, but also for quick simulation of wall heat losses and eventually for the on-line regulation of the cooling system.

AN ENGINEERING evaluation of six automobile head-on collision experiments is presented for impact speeds ranging from 21 to 52 mph. An analysis of the relative collision performances of unit-body and frame-type construction is made. Anthropometric dummy subjects facilitate determination of force systems for restrained and unrestrained motorists, their dynamic and kinetic responses to impact, and the causative factors associated with motorist injury production.* The systems of instrumentation which enabled a comprehensive analysis to be made from an event lasting only 0.25 sec are briefly presented.

The car as a self-contained microcosm is undergoing radical changes due to the advances of electronic technology. We need to rethink what a "car'' really is and the role of electronics in it. Electronics is now essential to control the movements of a car, of the chemical and electrical processes taking place in it, to entertain the passengers, to establish connectivity with the rest of the world, to ensure safety. What will an automobile manufacturer's core competence become in the next few years? Will electronics be the essential element in car manufacturing and design? We will address some of these issues and we will present some important developments in the area of system design that can strongly impact the way in which a car is designed.

The fact that, in our times, the execution of the exothermic process of combustion (‘heat release”) remains virtually uncontrolled is astonishing. Upon an attempt to rationalize this anomaly on historical grounds, technological means to rectify this astounding state of affairs are presented. They are based on the premise that, in the course of this process, the cylinder-piston enclosure is, in effect, a full-fledged chemical reactor. The salient feature of control is then active intervention into chemical reaction by turbulent jets. Principal elements of the control system are, as in any feedback mechanism, (1) sensors, (2) actuators and (3) a governor. The object of the first is to measure the profile of pressure - the useful output of the process. The second consists of a set of turbulent jet generators for injection of fuel and its mixing with air, as well as for ignition.

Quantifying Hybrid Electric Vehicle (HEV) emissions and fuel consumption is a difficult problem for a number of different reasons: 1) HEVs can be configured in significantly different ways (e.g., series or parallel); 2) the Auxiliary Power Unit (APU) can consist of a wide variety of engines, fuel types, and sizes; and 3) the APU can be operated very differently depending on the energy management system strategy and the type of driving that is performed (e.g., city vs. highway driving). With the future increase of HEV penetration in the vehicle fleet, there is an important need for government agencies and manufacturers to determine HEV emissions and fuel consumption. In this paper, several critical issues associated with HEV emissions and fuel consumption are identified and analyzed, using a sophisticated set of HEV and emission simulation modeling tools.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

To meet pulse power mode component cooling application needs, we developed, fabricated and tested a concept to use energy storage material and phase change material to enhance the heat dissipation of a conventional heat sink. Test results demonstrated the ESM/PCM heat sink has unique thermal performance. Under the same working condition, the peak temperature of ESM/PCM heat sink is 1.5°C lower than of a conventional heat sink. An optimized design can lead to a significant weight reduction for the heat sink in applications with high peak load and low duty power cycle power.

Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed adaptive SMC improves the controller robustness against major implementation imprecisions including sampling and quantization. The application of the proposed technique is demonstrated on the engine cold start emission control problem in a mid-size passenger car. The cold start controller is first designed in a single-input single-output (SISO) structure with three separate sliding surfaces, and then is redesigned based on a multiinput multi-output (MIMO) SMC design technique using nonlinear balanced realization.

In this paper, we propose algorithms for cost minimization of physical wires that are used to connect electronic devices in the vehicle. The wiring cost is one of the most important drivers of electrical architecture selection. Our algorithms perform wire routing from a source device to a destination device through harnesses, by selecting the optimized wire size. In addition, we provide optimized splice allocation with limited constraints. Based on the algorithms, we develop a tool which is integrated into an off-the-shelf optimization and workflow system-level design tool. The algorithms and the tool provide an efficient, flexible, scalable, and maintainable approach for cost analysis and architecture selection.

This paper presents an energy-optimal deceleration planning system (EDPS) to maximize regenerative energy for electrified vehicles on deceleration events perceived by map and navigation information, machine vision and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while in real time considering preceding vehicles. A practical force balance relationship based on an electrified powertrain is explicitly utilized for building a cost function of the associated optimal control problem. The optimal inputs are parameterized on each computation node from a set of available deceleration profiles resulting from a deceleration time model which are configured by real-world test drivings.

We assessed the engine-out emissions of an ultra-low sulfur diesel (ULSD) and a neat hydrogenated vegetable oil (HVO) from a light-duty diesel truck equipped with common rail direct injection. The vehicle was tested at least twice on each fuel using the LA-92 drive cycle and at steady-state conditions at 30 mph and 50 mph at different loads. Results showed reductions in the engine-out total hydrocarbon (THC), carbon monoxide (CO), nitrogen oxide (NOx), and particulate emissions with HVO. The reductions in soot mass, solid particle number, and particulate matter (PM) mass emissions with HVO were due to the absence of aromatic and polyaromatic hydrocarbon compounds, as well as sulfur species, which are known precursors of soot formation. Volumetric fuel economy, calculated based on the carbon balance method, did not show statistically significant differences between the fuels.

Evaluating real-world hazards associated with perception subsystems is critical in enhancing the performance of autonomous vehicles. The reliability of autonomous vehicles perception subsystems are paramount for safe and efficient operation. While current studies employ different metrics to evaluate perception subsystem failures in autonomous vehicles, there still exists a gap in the development and emphasis on engineering requirements. To address this gap, this study proposes the establishment of engineering requirements that specifically target real-world hazards and resilience factors important to AV operation, using High-Definition Maps, Global Navigation Satellite System, and weather sensors. The findings include the need for engineering requirements to establish clear criteria for a high-definition maps functionality in the presence of erroneous perception subsystem inputs which enhances the overall safety and reliability of the autonomous vehicles.

The relationship between ethanol and iso-butanol fuel concentrations and vehicle particulate matter emissions was investigated. This study utilized a gasoline direct injection (GDI) flexible fuel vehicle (FFV) with wall-guided fueling system tested with four fuels, including E10, E51, E83, and an iso-butanol blend at a proportion of 55% by volume. Emission measurements were conducted over the Federal Test Procedure (FTP) driving cycle on a chassis dynamometer with an emphasis on the physical and chemical characterization of particulate matter (PM) emissions. The results indicated that the addition of higher ethanol blends and the iso-butanol blend resulted in large reductions in PM mass, soot, and total and solid particle number emissions. PM emissions for the baseline E10 fuel were characterized by a higher fraction of elemental carbon (EC), whereas the PM emissions for the higher ethanol blends were more organic carbon (OC) in nature.