Search Results

Improving vehicle response through advanced knowledge of traffic behavior can lead to large improvements in energy consumption for the single isolated vehicle. This energy savings across multiple vehicles can even be larger if they travel together as a cohort in harmonization. Additionally, if the vehicles have enough information about their immediate path of travel, and other vehicles’ in that path (and their respective critical forward-looking information), they can safely drive close enough to each other to share aerodynamic load. These energy savings can be upwards of multiple percentage points, and are dependent on several criteria. This analysis looks at criteria that contributes to energy savings for a cohort of vehicles in synchronous motion, as well as describes a study that allows for better understanding of the potential benefits of different types of cohorted vehicles in different platoon arrangements.

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO 2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea-SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design.

The USDOT and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, GM, Honda, Mercedes, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested communications-based vehicle safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

The United States Department of Transportation (USDOT) and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, General Motors, Honda, Mercedes-Benz, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested Vehicle-to-Vehicle (V2V) communications-based safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

The application of Model-Based Development (MBD) techniques for automotive control system and software development have become standard processes due to the potential for reduced development time and improved specification quality. In order to improve development productivity even further, it is imperative to introduce a systematic Verification and Validation (V&V) process to further minimize development time and human resources while ensuring control specification quality when developing large complex systems. Traditional methods for validating control specifications have been limited by control specification scale, structure and complexity as well as computational limitations restricting their application within a systematic model-based V&V process. In order to address these issues, Toyota developed Hierarchical Accumulative Validation (HAV) for systematically validating functionally structured executable control specifications.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

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.

Model-based development (MBD) is widely adopted in the automotive industry to address the development challenges presented by the ever increasing content and complexity of in-vehicle control systems. Thus it is imperative that MBD is used efficiently and effectively. With efficiency and effectiveness in mind, we list requirements for a model-based verification and validation environment and report on the evaluation of such an environment on a representative engine idle speed control feature.

The purpose of this report is to describe the process for the development of the automatically shifting manual transmission control system hardware and software to be used in the MTU Challenge X Equinox, a through-the-road parallel hybrid electric vehicle. The automatically shifting manual transmission was chosen for development, as it combines the ease of use of an automatic transmission with the fuel efficiency of a manual, while eliminating the parasitic losses in the torque converter and the transmission hydraulic pump. This report illustrates the process used to develop the software-in-the loop modeling that was developed for the initial proof of concept. In addition, it describes the development of the control strategy and hardware build for the prototype transmission. To begin the design process research was preformed on existing automatically shifting manuals and manual transmissions in general. From there vehicle subsystems were assembled using Simulink block diagrams.

The hardware and software for a prototype computer controlled cooling system for a diesel powered truck has been designed and tested. The basic requirements for this system have been defined and the control functions, previously investigated in a study using the computer simulation model, were incorporated into the software. Engine dynamometer tests on the MACK-676 engine, comparing the conventional cooling system and the computer controlled system, showed the following advantages of the computer controlled system: 1. The temperature level to which the engine warms up to at low ambient temperature, was increased. 2. The faster shutter response reduced the temperature peaks and decreased total fan activity time. 3. The faster fan response reduces fan engagement time which should improve truck fuel economy.

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles.

Large channel count data acquisition systems have seen increasing use in the acquisition and analysis of rotating machinery, these systems have the ability to generate very large amounts of data for analysis. The most common operating measurement made on powertrains or automobiles on the road or on dynamometers has become the order track measurement. Order tracking analysis can generate a very large amount of information that must be analyzed, both due to the number of channels and orders tracked. Analysis methods to efficiently analyze large numbers of Frequency Response Function (FRF) measurements have been developed and used over the last 20 years in many troubleshooting applications. This paper develops applications for several FRF based analysis methods as applied for efficient analysis of large amounts of order track data.

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route.

According to pedestrian crash data from 2010-2011 the U.S. General Estimates System (GES) and the Fatality Analysis Report System (FARS), more than 39% of pedestrian crash cases occurred at night and poor lighting conditions. The percentage of pedestrian fatalities in night conditions is over 77%. Therefore, evaluating the performance of pedestrian pre-collision systems (PCS) at night is an essential part of the pedestrian PCS performance evaluation. The Transportation Active Safety Institute (TASI) of Indiana University-Purdue University Indianapolis (IUPUI) is conducting research for the establishment of PCS test scenarios and procedures in collaboration with Toyota's Collaborative Safety Research Center. The objective of this paper is to describe the design and implementation of a reconfigurable road lighting system to support the pedestrian PCS performance evaluation for night road lighting conditions.

Automotive exhaust noise is one of the major sources of noise pollution and it is controlled by passive control system (mufflers) and active control system (loudspeakers and active control algorithm). Mufflers are heavy, bulky and large in size while loudspeakers have a working temperature limitation. Carbon nanotube (CNT) speakers generate sound due to the thermoacoustic effect. CNT speakers are also lightweight, flexible, have acoustic and light transparency as well as high operating temperature. These properties make them ideal to overcome the limitations of the current exhaust noise control systems. An enclosed, coaxial CNT speaker is designed for exhaust noise cancellation application. The development of a 3D multi-physics (coupling of electrical, thermal and acoustical domains) model, for the coaxial speaker is discussed in this paper. The model is used to simulate the sound pressure level, input power versus ambient temperature and efficiency.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques.