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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.

Goods movement and port related activities are a significant source of emissions in many large urban areas. Electrification of diesel cargo handling equipment is one method of reducing community exposure to these emissions, that also provides the potential for reducing greenhouse gas emissions. This study evaluated the performance of several pieces of zero emission cargo transfer equipment for a demonstration conducted at two terminal locations at the Port of Long Beach (POLB). This included the data logging of three battery-electric top handlers and one battery-electric yard tractor, as well as two baseline diesel top handlers and one diesel yard tractor. The battery-electric equipment typically operated about 5 hours per day, while using between 34 to 50% of the battery pack state of charge (SOC). In general, the battery-electric equipment was able to provide comparable hours of operation to the diesel equipment over a typical 8-hour shift.

Textbook engine thermodynamics predicts that SI (Spark Ignition) engine efficiency η is a function of both the compression ratio CR of the engine and the specific heat ratio γ of the working fluid. In practice the compression ratio of the SI engine is often limited due to “knock”. Knock is in large part the effect of end gases becoming too hot and auto-igniting. Knock results in increase in heat transfer to the walls which negatively affects efficiency. Not to mention damages to the piston. One way to lower the end-gas temperature is to cool the intake gas before inducting it into the combustion chamber. With colder intake gases, higher CR can be deployed, resulting in higher efficiencies. In this regard, we investigated the efficiency of a standard Waukesha CFR engine. The engine is operated in the SI engine mode, and was operated with two differing mixtures at different temperatures.

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.

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.

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.

Drivers continually require steering performance improvement, particularly in the area of feedback from the road. In this study, we develop a new electrically-assisted steering logic by 1) analyzing existing steering systems to determine key factors, 2) modeling an ideal steering system from which to obtain a desirable driver torque, 3) developing a rack force observer to faithfully represent road information and 4) building a feedback compensator to track the tuned torque. In general, the estimator uses the driver torque, assist torque and other steering system signals. However, the friction of the steering system is difficult to estimate accurately. At high speed, where steering feeling is very important, greater friction results in increased error. In order to solve this problem, we design two estimators generated from a vehicle model and a steering system model. The observer that uses two estimators can reflect various operating conditions by using the strengths of each method.

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.

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.

The measurement of SO2 levels in vehicle exhaust can provide important information in understanding the relative contribution of sulfur and sulfate from fuel vs. oil source to PM. For this study, a differential optical absorption spectrometer (DOAS) that can measure SO2 down to 20 ppbV in real-time was built and evaluated. The DOAS consisted of an extractive sampling train, a cylindrical sampling cell with a single-path design to minimize cell volume, a spectrometer, and a deuterium lamp light source with a UVC range of ∼200-230 nanometer (nm). Laboratory tests showed detection limits were approximately in the range of 12 to 15 ppbV and showed good linearity over SO2 concentration ranges of 20 to 953 ppbV. Interference tests showed some interference by NO and by NH3, at levels of 300 ppmV and 16.6 ppmV, respectively.

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.

Test and verification procedures are a vital aspect of the development process for embedded control systems in the automotive domain. Formal requirements can be used in automated procedures to check whether simulation or experimental results adhere to design specifications and even to perform automatic test and formal verification of design models; however, developing formal requirements typically requires significant investment of time and effort for control software designers. We propose Signal Template Library (ST-Lib), a uniform modeling language to encapsulate a number of useful signal patterns in a formal requirement language with the goal of facilitating requirement formulation for automotive control applications. ST-Lib consists of basic modules known as signal templates. Informally, these specify a characteristic signal shape and provide numerical parameters to tune the shape.

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).

Biofuels, such as ethanol and butanol, have been the subject of significant political and scientific attention, owing to concerns about climate change, global energy security, and the decline of world oil resources that is aggravated by the continuous increase in the demand for fossil fuels. This study evaluated the potential emissions impacts of different alcohol blends on a fleet of modern gasoline vehicles. Testing was conducted on a fleet of nine vehicles with different combinations of ten fuel blends over the Federal Test Procedure and Unified Cycle. The vehicles ranged in model year from 2007-2014 and included four vehicles with port fuel injection (PFI) fueling and five vehicles with direct injection (DI) fueling. The ten fuel blends included ethanol blends at concentrations of 10%, 15%, 20%, 51%, and 83% by volume and iso-butanol blends at concentrations of 16%, 24%, 32%, and 55% by volume, and an alcohol mixture giving 10% ethanol and 8% iso-butanol in the final blend.

Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.

This paper follows a cycle-simulation method for creating an engine performance map for an ethanol fueled boosted HCCI engine using a 1-dimensional engine model. Based on experimentally determined limits, the study defined operating conditions for the engine and performed a limited parameter sweep to determine the best efficiency case for each condition. The map is created using a 6-Zone HCCI combustion model coupled with a detailed chemical kinetic reaction mechanism for ethanol, and validated against engine data collected from a 1.9L 4-Cylinder VW TDI engine modified to operate in HCCI mode. The engine was mapped between engine speeds of 900 and 3000 rpm, 1 and 3 bar intake pressure, and 0.2 and 0.4 equivalence ratio, resulting in loads between idle and 14.0 bar BMEP. Analysis of a number of trends for this specific engine map are presented, such as efficiency trends, effects of combustion phasing, intake temperature, engine load, engine speed, and operating strategy.

Steady-state, transient and dithering characteristics of emission conversion efficiencies of three-way catalysts on natural gas IC engine were investigated experimentally on a single-cylinder CFR engine test bench. Steady-state runs were conducted as references for specific engine emission levels and corresponding catalyst capacities. The steady-state data showed that conversion of HC will be the major problem since conversion of HC was effective only for a very narrow range of exhaust mixture. Unsteady exploration runs with both lean-to-rich and rich-to-lean transitions were conducted. These results were interpreted with a time scale analysis, according to which a qualitative oxygen storage model was proposed featuring the difference between oxygen absorption and desorption rates on the palladium catalysts.

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.

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.

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.