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Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e., the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 9 years. The initial document, SAE J2578, was published in 2002. SAE J2578 has been valuable as a Recommended Practice for FCV development with regard to the identification of hazards and the definition of countermeasures to mitigate these hazards such that FCVs can be operated in the same manner as conventional gasoline internal combustion engine (ICE)-powered vehicles. SAE J2578 is currently being revised so that it will continue to be relevant as FCV development moves forward. For example, test methods were refined to verify the acceptability of hydrogen discharges when parking in residential garages and commercial structures and after crash tests prescribed by government regulation, and electrical requirements were updated to reflect the complexities of modern electrical circuits which interconnect both AC and DC circuits to improve efficiency and reduce cost.

Automotive manufacturers continue to move further toward powertrain electrification. There are already many hybrid electric vehicles on the market that are based on a variety of system architectures. Ford Motor Company has investigated a new Dual Drive configuration that promises to overcome some of the attribute deficiencies associated with current architectures. The primary objective of this development project was to demonstrate the fuel economy potential of this system in a vehicle. To accomplish this objective, the team used an internally developed, formal Controls Development Process (CDP) for the control system design and validation. This paper describes the development of the vehicle control system in the context of this process.

In recent years, the design of automotive components and assemblies have resulted in an over-reliance on advanced CAE tools especially the Finite Element Analysis. An emphasis on cost reduction and commonization of components in automotive industry has made it necessary to use the CAE tools in innovative ways. Use of FEA as a effective product development tool can be greatly enhanced if it provides a high degree of correlation with physical tests, thereby greatly limiting the investment in expensive prototypes and testing. This paper will discuss a robustness based methodology to realize effective correlation of finite element models with actual physical tests on automotive seat structure assembly, at a component, sub-system, and systems level. Based on a parameter design approach, the various factors that affect the degree of correlation between CAE models and physical tests will be described.

In-cylinder ion sensing is a subject of interest due to its application in spark-ignited (SI) engines for feedback control and diagnostics including: combustion knock detection, rate and phasing of combustion, and mis-fire On Board Diagnostics (OBD). Further advancement and application is likely to continue as the result of the availability of ignition coils with integrated ion sensing circuitry making ion sensing more versatile and cost effective. In SI engines, combustion knock is controlled through closed loop feedback from sensor metrics to maintain knock near the borderline, below engine damage and NVH thresholds. Combustion knock is one of the critical applications for ion sensing in SI engines and improvement in knock detection offers the potential for increased thermal efficiency. This work analyzes and characterizes the ionization signal in reference to the cylinder pressure signal under knocking and non-knocking conditions.

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 hazard analysis for the Stop-Start control strategy of a Micro-Hybrid vehicle with manual gearbox is presented. The strategy allows for stops in gear and in neutral; this leads to specific hazards. Implications for the architecture of the electronic control unit, the software architecture, and the development process, especially software testing, are discussed.

With increasingly stringent emissions regulations and concurrent requirements for enhanced engine thermal efficiency, a comprehensive characterization of the automotive gasoline fuel spray has become essential. The acquisition of accurate and repeatable spray data is even more critical when a combustion strategy such as gasoline direct injection is to be utilized. Without industry-wide standardization of testing procedures, large variablilities have been experienced in attempts to verify the claimed spray performance values for the Sauter mean diameter, Dv90, tip penetration and cone angle of many types of fuel sprays. A new SAE Recommended Practice document, J2715, has been developed by the SAE Gasoline Fuel Injection Standards Committee (GFISC) and is now available for the measurement and characterization of the fuel sprays from both gasoline direct injection and port fuel injection injectors.

To achieve optimal axial and bending crush performance using dual phase steels for components designed for crash energy absorption and/or intrusion resistance applications, the cross sections of the components need to be optimized. In this study, Altair HyperMorph™ and HyperStudy® optimization software were used in defining the shape design variables and the optimization problem setup, and non-linear finite element code LS-DYNA® software was used in crush simulations. Correlated crash simulation models were utilized and the square cross-section was selected as the baseline. The optimized cross-sections for bending and axial crush performance resulted in significant mass and cost savings, particularly with the application of dual phase steels.

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.

This paper discusses the optimization of an automotive hydraulic steering pump pulley design for improved in-vehicle pump NVH performance. Levels of steering pump whine noise heard inside a vehicle were deemed objectionable. Vehicle and component transfer path analyses indicated that the dominant noise path for the whine noise was airborne in nature. Subsequent experimental modal analysis indicated that the steering pump pulley was a major contributor to the amount of radiated noise produced by the pump/pulley system. CAE analysis was used to further analyze the dynamic behavior of the pulley and develop an optimized design with decreased noise radiation efficiency. The results predicted with the CAE analysis were verified in-vehicle, resulting in a vehicle with acceptable steering pump whine noise performance.

Progress in the design and application of the magneto-elastic torque sensor to automotive drivetrain systems has taken the technology from the concept level to the point where it is considered production feasible. The latest generation of the sensors shows promising results regarding both the capabilities and applications to powertrain controls. Sensor designs, electronics and packaging layout are maturing. Well-defined component specifications and requirements are becoming available. The sensor utilities for real-time shift analysis and friction element control are established through vehicle-level investigation to demonstrate the production feasibility of the technology for transmission torque sensing.

An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described. Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations.

This paper studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed.

Software systems on electronically controlled diesel truck engines typically provide diagnostic features to enable the engine mechanic to identify and debug system problems. As future systems become more sophisticated, so will the diagnostic requirements. The advantages of serviceability and accuracy found in todays electronic systems must not be allowed to degrade due to this increased sophistication. One method of maintaining a high level of serviceability and accuracy is to place an even greater priority on diagnostics and servicing in the initial design phase of the product than is done today. In particular, three major goals of future diagnostic systems should be separation of component failures from system failures, prognostication of failures and analysis of engine performance. This paper will discuss a system to realize these goals by dividing the diagnostic task into the Electronic System Diagnostics, Engine System Diagnostics and the Diagnostic Interface.

The paper reports test results for accuracy of pre-crash speed, brake, and accelerator pedal position data recorded in a new family of Ford RCM EDR’s under steady state conditions. The authors drove 2 test vehicles at 3 different speeds from 48 to 113 km/h (30 to 70 mph), and artificially created EDR events so pre-crash data would be stored. The authors collected RCM data and PCM data. A GPS based Racelogic VBOX was used to measure speed and record CAN bus information real time. Maximum error, average error, and 98% confidence intervals are reported for RCM to VBOX and PCM to VBOX. Accelerator pedal position accuracy and brake on/off reporting latency of the RCM to CAN bus data and/or auxiliary brake switches are documented.

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 System Usability Scale (SUS) is used across industries, to evaluate a product’s ease of use. As the automotive industry increases its digital footprint, the SUS has found its application as a simple and reliable assessment of various in-vehicle human machine interfaces. These evaluations cover a broad scope and it is important to design studies with participant fatigue, study time, and study cost in mind. Reducing the number of items in the SUS questionnaire could save researchers time and resources. The SUS is a ten-item questionnaire that can measure usability and learnability, depending on the system. These ten questions are highly correlated to each other suggesting the SUS score can be determined with fewer items. Thus, the focus of this paper is two-fold: using principal component analysis (PCA) to determine the dimensionality of SUS and using this finding to reduce variables and build a regression equation for SUS scores for in-vehicle human machine interfaces.

With the rapid development of artificial intelligence, the autonomous vehicles (AV) have attracted considerable attention in the automotive industry. However, different factors negatively impact the adoption of the AVs, delaying their successful commercialization. Accretion of atmospheric icing, especially wet snow, on AV sensors causes blockage on their lenses, making them prone to lose their sight, in turn, increasing potential chances of accidents. In this study, two different designs are proposed in order to prevent snow accretion on the lenses of AVs via air flow across the lens surface. In both designs, lenses made of plain glass and superhydrophobic coated glass surfaces are tested. While some researchers have shown promise of water repellency on superhydrophobic surfaces, more snow accretion is observed on the superhydrophobic surfaces, when compared to the plain glass lenses.