Search Results

This paper outlines a real-time hierarchical control allocation algorithm for multi-axle land vehicles with independent hub motor wheel drives. At the top level, the driver’s input such as pedal position or steering wheel position are interpreted into desired global state responses based on a reference model. Then, a locally linearized rigid body model is used to design a linear quadratic regulator that generates the desired global control efforts, i.e., the total tire forces and moments required track the desired state responses. At the lower level, an optimal control allocation algorithm coordinates the motor torques in such a manner that the forces generated at tire-road contacts produce the desired global control efforts under some physical constraints of the actuation and the tire/wheel dynamics. The performance of the proposed control system design is verified via simulation analysis of a 3-axle heavy vehicle with independent hub-motor drives.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

Current military operations in Iraq and Afghanistan are unique because the battlefield can be described as a non-linear, asymmetrical environment. Units operate in zones that are susceptible to enemy contact from any direction at any time. The response to these issues has been the addition of add-on armor to HMMWV's and other tactical vehicles. The retro-fitting of armor to these vehicles has resulted in many accidents due to rollover and instability. The goal of this paper is to determine possible causes of the instability and rollover of up-armored tactical vehicles and to develop simulation tools that can analyze the steady-state and transient dynamics of the vehicles. Models and simulations include a steady-state rollover scenario, analysis of understeer gradient, and a transient handling analysis that uses models of both a human driver and a vehicle to analyze vehicle response to an obstacle avoidance maneuver.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Decreasing the propensity for rollover during steady state cornering of tractor semi-trailers is a key advantage to the trucking industry. This will be referred to as “increasing the lateral stability during steady state cornering” and may be accomplished by changes in design and loading variables which influence the behavior of a vehicle. To better understand the effects of such changes, a computer program was written to optimize certain design variables and thus maximize the lateral acceleration where an incipient loss of lateral stability occurs. The vehicle model used in the present investigation extends that developed by Law [1] and presented in Law and Janajreh [2]. The original model included the effects of tire flexibility, nonlinear roll-compliant suspensions, and fifth wheel lash. This model was modified to include (a) additional effects of displacement due to both lateral and vertical tire flexibility, and (b) provisions for determining “off-tracking”.

An investigation of the vertical dynamics of a tractor semi-trailer traversing a random road profile was conducted. This paper presents the development of a 14 degree-of-freedom (DOF), dynamic ride model of a tractor semi-trailer. It is based on work previously conducted by Vaduri and Law [1] and Law et al [2]. The DOFs include: (a) vertical displacements of each of the five axles, the tractor frame, the engine on its mounts, the cab on its suspension, and the driver's seat; (b) pitch displacements of the trailer with respect to the tractor, the cab, and the rigid tractor frame; and, (c) the first bending or beaming modes of the tractor and trailer frames. The model also incorporates suspension friction, and tire non-uniformities. The simulation of the model is conducted using MATLAB software.

Understanding the parameters which influence the tendency for a heavy truck to exhibit rollover is of paramount importance to the trucking industry. Multiple parameters influence the vehicle’s motion, and the ability to determine how each affects the vehicle as a system would be an indispensable tool for the design of such vehicles. To be able to perform such predictions and analysis, models and a computer simulation were created to allow the examination of changes in design parameters in such vehicles. The vehicle model was originally developed by Law [1] and presented in Law and Janajreh [2]. The model was extended further by Lawson [3, 4] to include (a) the effects of the torsional compliance of both the tractor and trailer, and (b) tanker trailers with various levels of liquid fill. In the present paper, both the tractor and trailer compliances were studied independently to determine their influences on the rollover stability of the vehicle.

As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS.

Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle.

Autonomous vehicles (AV) are sophisticated systems comprising various sensors, powerful processors, and complex data processing algorithms that navigate autonomously to their respective goals. Out of several functions performed by an AV, one of the most important is developing situational intelligence to predict collision-free future trajectories. As an AV operates in environments consisting of various entities, such as other AVs, human-driven vehicles, and static obstacles, developing situational intelligence will require a collaborative approach. The recent developments in artificial intelligence (AI) and deep learning (DL) relating to AVs have shown that DL-based models can take advantage of information sharing and collaboration to develop such intelligence.

Military ground vehicles operate in off-road environments traversing different terrains under various environmental conditions. There has been an increasing interest towards autonomous off-road vehicle navigation, leading to the needs of terrain traversability assessment through sensing. These methods utilized data-driven approaches on classical robotic perception sensing modalities (RGB cameras, Lidar, and depth cameras) positioned in front of ground vehicles in order to observe approaching terrain. Classical robotic sensing modalities, though effective for describing environment geometry and object detection and tracking, aren’t able to directly observe features related to compaction and moisture content which have significant effects on the moduli properties governing terrain mechanics. These methods then become very specialized to specific regions and environmental conditions which are inevitably subject to change.

Several emerging technologies hold great promise to improve the 360-degree awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. As a result, industry is challenged to evaluate new technologies in a way that is objective and allows the comparison of different systems in a consistent manner. This research aims to explore the methods currently in use, identify relevant factors not presently incorporated in standard procedures, and recommend best practices to accomplish an overall measurement system that can quantify performance beyond simply the field of view of a driver visibility system. We introduce a new metric, “Clarity of View,” that incorporates several important factors for visibility systems including: gap acceptance accuracy, image detection time, and distortion.

Electrification of off-road vehicle powertrains can increase mobility, improve energy efficiency, and enable new utility by providing high amounts of electrical power for auxiliary devices. These vehicles often operate in extreme temperature conditions at low ground speeds and high power levels while also having significant cooling airpath restrictions. The restrictions are a consequence of having grilles and/or louvers in the airpath to prevent damage from the operating environment. Moreover, the maximum operating temperatures for high voltage electrical components, like batteries, motors, and power-electronics, can be significantly lower than those of the internal combustion engine. Rejecting heat at a lower temperature gradient requires higher flow rates of air for effective heat exchange to the operating environment at extreme temperature conditions.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

Automotive and truck manufacturers are introducing electric propulsion systems into their ground vehicles to reduce fossil fuel consumption and harmful tailpipe emissions. The mobility shift to electric motors requires a compact thermal management system that can accommodate heat dissipation demands with minimum energy consumption in a confined space. An innovative cooling system design, emphasizing passive cooling methods coupled with a small liquid system, using a thermal bus architecture has been explored. The laboratory experiment features an emulated electric motor interfaced to a thermal cradle and multiple heat rejection pathways to evaluate the transfer of generated heat to the ambient surroundings. The thermal response of passive (e.g., carbon fiber, high thermal conductivity material, thermosyphon) and active cooling systems are investigated for two operating scenarios.

Semantic segmentation is an integral component in many autonomous vehicle systems used for tasks like path identification and scene understanding. Autonomous vehicles must make decisions quickly enough so they can react to their surroundings, therefore, they must be able to segment the environment at high speeds. There has been a fair amount of research on semantic segmentation, but most of this research focuses on achieving higher accuracy, using the mean intersection over union (mIoU) metric rather than higher inference speed. More so, most of these semantic segmentation models are trained and evaluated on urban areas instead of off-road environments. Because of this there is a lack of knowledge in semantic segmentation models for use in off-road unmanned ground vehicles.

Off-road vehicles are increasingly adopting hybrid and electric powertrains for improved mobility, range, and energy efficiency. However, their cooling systems consume a significant amount of energy, affecting the vehicle’s operating range. This study develops a predictive controller for the battery thermal management system in an autonomous electric tracked off-road vehicle. By analyzing the system dynamics, the controller determines the optimal preview horizon and controller timestep. Sensitivity analysis is conducted to evaluate temperature tracking and energy consumption. Compared to an optimal controller without preview, the predictive controller reduces energy consumption by 55%. Additionally, a relationship between cooling system energy consumption and battery size is established. The impact of the preview horizon on energy consumption is examined, and a tradeoff between computational cost and optimality is identified.