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Estimated engine torque is an important parameter used by automotive systems for automated transmission and clutch control. Heavy-duty engine and transmission manufacturers widely use SAE J -1939 based ECU torque calculation based on mass air/fuel flow steady state maps created during calibration of the engine for this purpose. As an alternative, to enhance the accuracy of this important control variable, a virtual flywheel torque sensor (VFTS) was developed. It measures the engine torque based on the harmonics of the instantaneous flywheel speed signal. Initial dynamometer testing showed the VFTS estimated torque values exhibited a maximum inaccuracy of 12% of the actual measured torque over the range of conditions tested. In this paper we report the results of on road truck testing of the VFTS. A loaded heavy truck with a gross vehicle weight rating of 80,000 pounds was used.

Liquefied petroleum gas (LPG), like many other alternative fuels, has witnessed increased adoption in the last decade, and its use is projected to rise as stricter emissions regulations continue to be applied. However, much of its use is limited to dual fuel applications, gaseous phase injection, light-duty passenger vehicle applications, or scenarios that require conversion from gasoline engines. Therefore, to address these limitations and discover the most efficient means of harnessing its full potential, more research is required in the development of optimized fuel injection equipment for liquid port and direct injection, along with the implementation of advanced combustion strategies that will improve its thermal efficiency to the levels of conventional fuels.

Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed.

There are numerous activities in the automotive industry in which a vehicle drives a pre-defined route multiple times such as portable emissions measurement systems testing or real-world electric vehicle range testing. The speed profile is not the same for each drive cycle due to uncontrollable real-world variables such as traffic, stoplights, stalled vehicles, or weather conditions. It can be difficult to compare each run accurately. To this end, this paper presents a method to compare and quantify the repeatability of real-world on-road vehicle driving schedules using dynamic time warping (DTW). DTW is a well-developed computational algorithm which compares two different time-series signals describing the same underlying phenomenon but occurring at different time scales. DTW is applied to real-world, on-road drive cycles, and metrics are developed to quantify similarities between these drive cycles.

Research on alternative fuels has made significant progress as demands for cleaner and more efficient engine operation intensifies. Liquefied petroleum gas (LPG) can offer a potential alternative fuel route in the Diesel fuel dominated heavy-duty transportation sector due to its low cost, high anti-knock limit relative to gasoline, and reduced emission levels. In this work, experimental investigations are performed to study the effects of LPG compositions on performance, emissions, and combustion behavior of a spark-ignited (SI) cooperative fuel research (CFR) engine under stoichiometric conditions. Four LPG blends (chemically pure propane, a representative US blend, HD-5, and a representative European blend) representing the present LPG market are chosen. The impact of fuel composition is studied under different compression ratios (CR), ranging from 7:1 to 10:1 with one-unit increments, and at constant engine speed, intake manifold air pressure (IMAP) and 50% burn crank angle (CA50).

Automotive companies are constantly looking to increase the fuel efficiency, shift quality, passenger comfort, and to reduce wear and tear on the components. Most of these aspects depend on the accuracy of torque used for transmission control, which determines the required operational gear position at a given speed and road conditions. Currently, SAE J-1939 CAN bus torque estimation relies on steady state maps that are generated during the calibration of the engine for different speeds and loads. In this paper we report the development of a Virtual Flywheel Torque Sensor (VFTS) useful for real time torque measurement based on an engine speed harmonics analysis. The VFTS uses a signal from the flywheel speed sensor to estimate the flywheel angular acceleration, which and provides a proportional torque value which corresponds to torque at the flywheel.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

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.

Application of predictive optimal energy management strategies to improve fuel economy in hybrid electric vehicles is an active subject of research. Acceleration events during a drive cycle provide particularly attractive opportunities for predictive optimal energy management because of their high energy cost and limited variability, which enables optimal control trajectories to be computed in advance. In this research, dynamic-programming derived optimal control matrices are implemented during a drive cycle on a validated model of a 2010 Toyota Prius to simulate application of pre-computed control to improve fuel economy over a baseline model. This article begins by describing the development of the vehicle model and the formulation of optimal control, both of which are simulated over the New York City drive cycle to establish baseline and upper-limit fuel economies. Then, optimal control strategies are computed for acceleration events in the drive cycle.

In some studies, a relationship has been observed between increasing ethanol content in gasoline and increased particulate matter (PM) emissions from vehicles equipped with spark ignition engines. The fundamental cause of the PM increase seen for moderate ethanol concentrations is not well understood. Ethanol features a greater heat of vaporization (HOV) than gasoline and also influences vaporization by altering the liquid and vapor composition throughout the distillation process. A droplet vaporization model was developed to explore ethanol’s effect on the evaporation of aromatic compounds known to be PM precursors. The evolving droplet composition is modeled as a distillation process, with non-ideal interactions between oxygenates and hydrocarbons accounted for using UNIFAC group contribution theory. Predicted composition and distillation curves were validated by experiments.

Developing a complete understanding of the structure and behavior of the near-wall region (NWR) in reciprocating, internal combustion (IC) engines and of its interaction with the core flow is needed to support the implementation of advanced combustion and engine operation strategies, as well as predictive computational models. The NWR in IC engines is fundamentally different from the canonical steady-state turbulent boundary layers (BL), whose structure, similarity and dynamics have been thoroughly documented in the technical literature. Motivated by this need, this paper presents results from the analysis of two-component velocity data measured with particle image velocimetry near the head of a single-cylinder, optical engine. The interaction between the NWR and the core flow was quantified via statistical moments and two-point velocity correlations, determined at multiple distances from the wall and piston positions.

Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle.

Previous research has demonstrated an increase in Fuel Economy (FE) using an optimal controller based on limited foreknowledge using methods such as Engine Equivalent Minimization Strategy (ECMS) and Stochastic Dynamic Programming (SDP) with stochastic error in the prediction signal considerations. This study seeks to quantify the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality assuming optimal control. In this research, a hill pattern and route type identification scenario control subjected to varying prediction signal quality is selected for in depth study. This paper describes the development of a baseline Toyota Prius Hybrid Vehicle (HV) simulation models, real world drive cycles and real-world disturbances, and an optimal controller incorporating a prediction of vehicle power requirements.

Aerodynamic performance testing of heavy duty fans involve complicated test setups with specialized equipment and measurement systems as summarized in ANSI 210-07 standard. This paper describes virtual testing and simulation methods to obtain the fan aerodynamic performance data using commercial Computational Fluid Dynamics (CFD) software. Two different virtual test environments were used during the analysis. The first one is a virtual test chamber which is constructed based on the actual fan system installation. The second one is a virtual flow tube which approximates a fan flow test set-up as outlined in ANSI 210-07. The virtual fan is created from (i) the laser scan of the actual fan and (ii) the design specifications of the fan. The virtual test conditions simulate the actual test arrangement by imposing free boundary at flow inlet/outlet and proper fan rotation. The aerodynamic flow rate is controlled by a variable orifice located at the virtual test chamber outlet.

When pickup trucks are driven on concrete paved freeways, freeway hop shake is a major complaint. Freeway hop shake occurs when the vehicle passes over the concrete joints of the freeway which impose in-phase harmonic road inputs. These road inputs excite vehicle modes that degrade ride comfort. The worst shake level occurs when the vehicle speed is such that the road input excites the vehicle 1st bending mode and/or the rear wheel hop mode. The hop and bending mode are very close in frequency. This phenomenon is called freeway hop shake. Automotive manufacturers are searching for ways to mitigate freeway hop shake. There are several ways to reduce the shake amplitude. This paper documents a new approach using hydraulic body mounts to reduce the shake. A full vehicle analytical model was used to determine the root cause of the freeway hop shake.

This study involves an application of Principal Component Analysis (PCA) conducted in support of a Design for Six Sigma (DFSS) project. Primary focus of the project is to optimize seat parameters that influence Low Speed Rear Impact (LSRI) whiplash performance. During the DFSS study, the project team identified a need to rank order critical design factors statistically and establish their contribution to LSRI performance. It is also required to develop a transfer function for the LSRI rating in terms of test response parameters that can be used for optimization. This statistical approach resulted in a reliable transfer function that can applied across all seat designs and enabled us to separate vital few parameters from several many.

Homogeneous Charge Compression Ignition (HCCI) combustion shows a high potential of reducing both fuel consumption and exhaust gas emissions. Many works have been devoted to extend the HCCI operation range in order to maximize its fuel economy benefit. Among them, fuel injection strategies that use fuel reforming to increase the cylinder charge temperature to facilitate HCCI combustion at low engine loads have been proposed. However, to estimate and control an optimal amount of fuel reforming in the cylinder of an HCCI engine proves to be challenging because the fuel reforming process depends on many engine variables. It is conceivable that the amount of fuel reforming can be estimated since it correlates with the combustion phasing which in turn can be measured using a cylinder pressure sensor.

General Motors recently demonstrated two driveable test vehicles powered by a Homogeneous Charge Compression Ignition (HCCI) engine. HCCI combustion has the potential of a significant fuel economy benefit with reduced after-treatment cost. However, the biggest challenge of realizing HCCI in vehicle applications is controlling the combustion process. Without a direct trigger mechanism for HCCI's flameless combustion, the in-cylinder mixture composition and temperature must be tightly controlled in order to achieve robust HCCI combustion. The control architecture and strategy that was implemented in the demo vehicles is presented in this paper. Both demo vehicles, one with automatic transmission and the other one with manual transmission, are powered by a 2.2-liter HCCI engine that features a central direct-injection system, variable valve lift on both intake and exhaust valves, dual electric camshaft phasers and individual cylinder pressure transducers.

The widely used Chen correlation, consisting of two parts, forced convection and nucleate boiling, was examined with the previously reported experimental data taken in both a flow-loop apparatus and a standard IC engine on subcooled flow boiling for a 50–50 ethylene glycol-water mixture. Comparisons between the Chen correlation and the flow-loop data suggested that a correction factor is required only on the forced convection component of the Chen correlation. The Chen correlation with the correction factor of 2.0 tolerably predicts the heat fluxes and the wall temperatures not only in the flow-loop apparatus but also in the coolant passages of the IC engine.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.