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In this paper, residual stress distributions in rectangular bars due to rolling or burnishing at very high rolling or burnishing loads are investigated by roll burnishing experiments and three-dimensional finite element analyses using ABAQUS. First, roll burnishing experiments on rectangular bars at two roller burnishing loads are presented. The results indicate the higher burnishing load induces lower residual stresses and the higher burnishing load does not improve fatigue lives. Next, in the corresponding finite element analyses, the roller is modeled as rigid and the roller rolls on the flat surface of the bar with a low coefficient of friction. The bar material is modeled as an elastic-plastic strain hardening material with a nonlinear kinematic hardening rule for loading and unloading.

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.

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.

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.

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.

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.

Automatic code generation from models is actively used at Caterpillar for powertrain and machine control development. This technology was needed to satisfy the industry's demands for both increased software feature content, and its added complexity, and a short turn-around time. A pilot development effort was employed initially to roll out this new technology and shape the deployment strategy. As a result of a series of successful projects involving rapid prototyping and production code generation, Caterpillar will deploy MathWorks modeling and code generation products as their department-wide production development capability. The data collected indicated a reduction of person hours by a factor of 2 to 4 depending on the project and a reduction of calendar time by a factor of greater than 2. This paper discusses the challenges, results, and lessons learned, during this pilot effort from the perspectives of both Caterpillar and The MathWorks.

When a manual transmission (MT) powertrain is subjected to high speeds and high torques, the vehicle driveshaft, and other components experience an increase in stored potential energy. When the engine and driveshaft are decoupled during an up or down shift, the potential energy is released causing clunk during the shift event. The customer desires a smooth shift thus reduction of clunk will improve experience and satisfaction. In this study, a six-speed MT, rear-wheel-drive (RWD) passenger vehicle was used to experimentally capture acoustic and vibration data during the clunk event. To replicate the in-situ results, additional data was collected and analyzed for powertrain component roll and pitch. A lumped parameter model of key powertrain components was created to replicate the clunk event and correlate with test data. The lumped parameter model was used to modify clutch tip-out parameters, which resulted in reduced prop shaft oscillations.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.

In comparison to the state of knowledge of standard internal combustion vehicles, there is relatively little known on how to best implement component sub-systems and best integrate these systems together to create a hybrid electric vehicle.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

A practical approach for evaluating and validating global system designs for Squeak and Rattle performance is proposed. Using simple slip and rattle models, actual sound and vibration data, and the fundamentals of audiological perception, analysis tools adapted from Chaos Theory are used to establish threshold levels of performance and identify system characteristics which are significant contributors to Squeak and Rattle. Focus on system design is maintained by using a simple rattle noise indicator and relating rattle events to levels of dynamic motion (acceleration, velocity, etc.). The threshold level is defined as the level of acceleration at which the system moves from a non-rattling state to a rattling state. The approach is demonstrated with a simple analytical model applied to an experimental structure under dynamic load.

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge.

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources.

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.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.