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As mass reduction becomes an increasingly important enabler for fuel economy improvement, having a robust structural development process that can comprehend Vehicle Dynamics-specific requirements is correspondingly important. There is a correlation between the stiffness of the body structure and the performance of the vehicle when evaluated for ride and handling. However, an unconstrained approach to body stiffening will result in an overly-massive body structure. In this paper, the authors employ loads generated from simulation of quasi-static and dynamic vehicle events in ADAMS, and exercise structural finite element models to recover displacements and deflected shapes. In doing so, a quantitative basis for considering structural vehicle dynamics requirements can be established early in the design/development process.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

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.

As the new features for driver assistance and active safety systems are growing rapidly in vehicles, the simulation within a virtual environment has become a necessity. The current active safety system consists of Electronic Control Units (ECUs) which are coupled to camera and radar sensors. Two methods of implementation exists, integrated sensors with control modules or separation of sensors form control modules. The subsystem integration testing poses new challenges for virtual environment for simulation of active safety features. The comprehensive simulation environment for integration testing consists of chassis controls, powertrain, driver assistance, body and displays controllers. Additional complexity in the system is the serial communication strategy. Multiple communication protocols such as GMLAN, LIN, standard CAN, and Flexray could be present within the same vehicle topology.

This research examined driver acceptance and behavior associated with Speed Limit and Curve Advisor systems, including influences on speed choice. Drivers experienced messages from an emulated Speed Limit and Curve Advisor system during a 2-hour public road drive. Driver tolerance for system errors and message conflicts was also studied by manipulating the accuracy of the information provided by the system. Messages were presented using either a Head-Up Display or on an in-dash Driver Information Center. Results indicate that drivers liked having speed limit information continuously available to them while driving, but the information provided by the Speed Limit Advisor did not significantly influence or alter drivers' speed choice or deceleration profiles in comparison to driving without the system.

An analysis of the first 35 back-over crashes reported by NHTSA's Special Crash Investigations unit was undertaken with two objectives: (1) to test a hypothesized classification of backing crashes into types, and (2) to characterize scenario-specific conditions that may drive countermeasure development requirements and/or objective test development requirements. Backing crash cases were sorted by type, and then analyzed in terms of key features. Subsequent modeling of these SCI cases was done using an adaptation of the Driving Reliability and Error Analysis Methodology (DREAM) and Cognitive Reliability and Error Analysis Methodology (CREAM) (similar to previous applications, for instance, by Ljung and Sandin to lane departure crashes [10]), which is felt to provide a useful tool for crash avoidance technology development.

Alternative implementations of a Rear Cross Traffic Alert (RCTA) system intended to actively notify drivers of the presence of rear cross-path traffic when backing were evaluated in naturalistic settings. The feature is one of several emerging technologies designed to assist drivers when backing - in this case, enhancing drivers' awareness of traffic approaching from the rear. The study allowed performance under a range of RCTA system driver interface implementations to be contrasted with conventional and wide Field of View (FOV) Rear Vision systems. Evaluations were conducted using a sample of 70 drivers under naturalistic settings and environments with repeated exposures to backing tasks. The study also made use of a staged conflict situation with a confederate vehicle in order to more precisely quantify driver behavior and system usage across drivers under controlled conflict situations.

Vehicle-to-vehicle (V2V) communication systems can enable a number of wireless-based vehicle features that can improve traffic safety, driver convenience, and roadway efficiency and facilitate many types of in-vehicle services. These systems have an extended communication range that can provide drivers with information about the position and movements of nearby V2Vequipped vehicles. Using this technology, these vehicles are able to communicate roadway events that are beyond the driver's view and provide advisory information that will aid drivers in avoiding collisions or congestion ahead. Given a typical communication range of 300 meters, drivers can potentially receive information well in advance of their arrival to a particular location. The timing and nature of presenting V2V information to the driver will vary depending on the nature and criticality of the scenario.

Incomplete vehicles are partially manufactured by an Original Equipment Manufacturer (OEM) and subsequently sold to and completed by a final-stage manufacturer. Section S8.8, Final-Stage Manufacturers and Alterers, of Federal Motor Vehicle Safety Standard (FMVSS) 126 states “Vehicle that are manufactured in two or more stages or that are altered (within the meaning of 49 CFR 567.7) after having been previously certified in accordance with Part 567 of this chapter, are not subject to the requirements of S8.1 through S8.5. Instead, all vehicles produced by these manufacturers on or after September 1, 2012, must comply with this standard.” The FMVSS 126 compliance of the completed vehicle can be certified in three ways: by the OEM provided no alterations are made to identified components (TYPE 1), conditionally by the OEM provided the final-stage manufacturer follows specific guidelines (TYPE 2), or by the final-stage manufacturer (TYPE 3).

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

This paper discusses observability of the vehicle states using different sensor configurations as well as fault-tolerant estimation of these states. The optimality of the sensor configurations is assessed through different observability measures and by using a 3-DOF linear vehicle model that incorporates yaw, roll and lateral motions of the vehicle. The most optimal sensor configuration is adopted and an observer is designed to estimate the states of the vehicle handling dynamics. Robustness of the observer against sensor failure is investigated. A fault-tolerant adaptive estimation algorithm is developed to mitigate any possible faults arising from the sensor failures. Effectiveness of the proposed fault-tolerant estimation scheme is demonstrated through numerical analysis and CarSim simulation.

This paper presents the implementation of an off-line optimized torque vectoring controller on an electric-drive vehicle with four in-wheel motors for driver assistance and handling performance enhancement. The controller takes vehicle longitudinal, lateral, and yaw acceleration signals as feedback using the concept of state-derivative feedback control. The objective of the controller is to optimally control the vehicle motion according to the driver commands. Reference signals are first calculated using a driver command interpreter to accurately interpret what the driver intends for the vehicle motion. The controller then adjusts the braking/throttle outputs based on discrepancy between the vehicle response and the interpreter command.

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.

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 battery system in the Chevrolet Volt is very complex and must balance a variety of performance criteria, including the safety of vehicle occupants and other users. In order to assure a thorough approach to battery system safety, a system safety engineering process was applied and found to provide a useful framework. This methodical approach began with the preliminary hazard analysis and continued through requirements definition, design development and, finally, validation. Potentially hazardous conditions related directly to functional safety (for example, charge control) and primary physical safety (for example, short circuit conditions) can all be addressed in this manner. Typical battery abuse testing, as well as newly defined limit testing, supported the effort. Extensive documentation, traceability and peer reviews helped to verify that all issues were addressed.

Model-based control strategies are important for meeting the dual objective of maximizing NOx reduction and minimizing NH3 slip in urea-SCR catalysts. To be implementable on the vehicle, the models should capture the essential behavior of the system, while not being computationally intensive. This paper discusses the adequacy of two different reduced order SCR catalyst models and compares their performance with a higher order model. The higher order model assumes that the catalyst has both diffusion and reaction kinetics, whereas the reduced order models contain only reaction kinetics. After describing each model, its parameter identification and model validation based on experiments on a Navistar I6 7.6L engine are presented. The adequacy of reduced order models is demonstrated by comparing the NO, NO2 and NH3 concentrations predicted by the models to their concentrations from the test data.

This study documents a method developed for dynamically measuring occupant pocketing during various low-speed rear impact, or “whiplash” sled tests. This dynamic pocketing measurement can then be related to the various test parameters used to establish the performance rating or compliance results. Consumer metric and regulatory tests discussed within this paper as potential applications of this technique include, but are not limited to, the Insurance Institute for Highway Safety (IIHS) Low Speed Rear Impact (LSRI) rating, Federal Motor Vehicle Safety Standard (FMVSS) 202a, and European New Car Assessment Program (EURO-NCAP) whiplash rating. Example metrics are also described which may be used to assist in establishing the design position of the head restraint and optimize the balance between low-speed rear impact performance and customer comfort.

To realize better fuel economy benefits from transmissions, car makers have started the application of torque converter clutch control in second gear and beyond, resulting in greater demand on the torque converter clutch (TCC) and its control system. This paper focuses on one aspect of the control of the torque converter clutch to improve fuel economy and faster response of the transmission. A TCC is implemented to control the slip between the pump and turbine of the torque converter, thereby increasing its energy transfer efficiency and increasing vehicle fuel economy. However, due to the non-linear nature of the torque converter fluid coupling, the slip feedback control has to be very active to handle different driver inputs and road-load conditions, such as different desired slip levels, changes in engine input torques, etc. This non-linearity requires intense calibration efforts to precisely control the clutch slip in all the scenarios.

A simulation methodology is developed for the Constant Radius Constant Speed (CRCS) analysis to predict the ISO4138 [1] road test performance. The CRCS analysis can be used to predict the vehicle steady-state handling characteristics such as understeer, rear cornering compliance, and roll gradient, etc. The Yaw-Rate Control methodology is applied to replace the traditional driver-in-the-loop path-following approaches. Comparing to the path-following approaches, the proposed method is simpler to use, more efficient, accurate, and robust.

The imperative to get to the market faster with new and better products, has determined all automotive OEM to rethink their product development cycle, and, as a result, many hardware based processes were replaced and/or augmented with virtual, software based ones. However, the virtualization itself does not guaranties better and faster products. In the area of vehicle dynamics, we concentrate on improving the multi-body model development process, facilitating comprehensive virtual testing, and verifying the robustness of the design. The authors present a highly flexible and efficient environment that encourages, enforces, and facilitates model sharing, reusing of components, and parallelization of vehicle dynamics simulations, developed on top of an existing commercial off-the-shelf engineering software application.