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The conversion design strategy, and emissions and performance results for a dedicated propane, vapour injected, 1995 Dodge Dakota truck are reported. Data is obtained from the University of Waterloo entry in the 1997 Propane Vehicle Challenge. A key feature of the design strategy is its focus on testing and emissions while preserving low engine speed power for drivability. Major changes to the Dakota truck included the following: installation of a custom shaped fuel tank, inclusion of a fuel temperature control module, addition of a vaporizer and a fuel delivery metering unit, installation of a custom vapour distribution manifold, addition of an equivalence ratio electronic controller, inclusion of a wide range oxygen sensor, addition of an exhaust gas recirculation cooler and installation of thermal insulation on the exhaust system. A competition provided natural gas catalyst was used.

Lithium-ion (Li-ion) batteries and issues related to their thermal management and safety have been attracting extensive research interests. In this work, based on a recent thermal chemistry model, the phenomena of thermal runaway induced by a transient internal heat source are computationally investigated using a three-dimensional (3D) model built in COMSOL Multiphysics 5.3. Incorporating the anisotropic heat conductivity and typical thermal chemical parameters available from literature, temperature evolution subject to both heat transfer from an internal source and the activated internal chemical reactions is simulated in detail. This paper focuses on the critical runaway behavior with a delay time around 10s. Parametric studies are conducted to identify the effects of the heat source intensity, duration, geometry, as well as their critical values required to trigger thermal runaway.

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (e.g. rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference images stored in the cloud.

A new method is demonstrated for rating the “severity” of a hypoid gear set duty cycle (revolutions at torque) using the intercept of T-N curve to support gearset selection and sizing decision across vehicle programs. Historically, it has been customary to compute a cumulative damage (using Miner's Rule) for a rotating component duty cycle given a T-N curve slope and intercept for the component and failure mode of interest. The slope and intercept of a T-N curve is often proprietary to the axle manufacturer and are not published. Therefore, for upfront sizing and selection purposes representative T-N properties are used to assess relative component duty cycle severity via cumulative damage (non-dimensional quantity). A similar duty cycle severity rating can also be achieved by computing the intercept of the T-N curve instead of cumulative damage, which is the focus of this study.

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

In this work, a new air hybrid engine is introduced in which two throttles are used to manage the engine load in three modes of operation i.e. braking, air motor, and conventional mode. The concept includes an air tank to store pressurized air during braking and rather than a fully variable valve timing (VVT) system, two throttles are utilized. Use of throttles can significantly reduce the complexity of air hybrid engines. The valves need three fixed timing schedules for the three modes of operation. To study this concept, for each mode, the results of engine simulations using GT-Power software are used to generate the operating maps. These maps show the maximum braking torque as well as maximum air motor torque in terms of air tank pressure and engine speed. Moreover, the resulting maps indicate the operating conditions under which each mode is more effective. Based on these maps, a power management strategy is developed to achieve improved fuel economy.

Recently the use of digital 3D profilometry in the automotive industries has become increasingly popular. The effective techniques for 3D shape measurement, especially for the measurement of complicated structures, have become more and more significant. Different optical inspective methods, such as 3D profilometry, laser scanning and Coordinate-Measuring Machine (CMM), have been applied for 3D shape measurement. Among these methods, 3D profilometry seems to be the fastest and inexpensive method with considerably accurate result, and it has simple setup and full field measuring ability compared with other techniques. In this paper, a novel calibration method for 3D-profilometry will be introduced. In this method, a multiple-step calibration procedure is utilized and best-fit calibration curves are obtained to improve measurement accuracy. A recursive algorithm is used for data evaluation, along with calibration data.

In vehicles equipped with conventional Electric Power Steering (EPS) systems, the steering effort felt by the driver can be unreasonably low when driving on slippery roads. This may lead inexperienced drivers to steer more than what is required in a turn and risk losing control of the vehicle. Thus, it is sensible for tire-road friction to be accounted for in the design of future EPS systems. This paper describes the design of an auxiliary EPS controller that manipulates torque delivery of current EPS systems by supplying its motor with a compensation current controlled by a fuzzy logic algorithm that considers tire-road friction among other factors. Moreover, a steering system model, a nonlinear vehicle dynamics model and a Dugoff tire model are developed in MATLAB/Simulink. Physical testing is conducted to validate the virtual model and confirm that steering torque decreases considerably on low friction roads.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

A real-time control-oriented mean value engine plant model that includes engine thermals and cold starts is developed for a Toyota Prius 2015 plug-in hybrid engine in Modelica and MapleSim and validated experimentally. The model consists of an engine block model, intake and exhaust manifold models, and a throttle model. An advantage of the engine block model is the ability to compute the frictional Mean Effective Pressure during engine cold starts from calculated air, oil, and coolant temperatures at various locations in the engine block. Traditionally, engine thermals are modelled utilizing thermal resistances and capacitors. The proposed model utilizes linear graph theory with terminal equations to study the topology of the different components that affect engine thermals, including engine head, liner, coolant, and oil sump.

Due to the high power and energy density and also relative safety, lithium ion batteries are receiving increasing acceptability in industrial applications especially in transportation systems with electric traction such as electric vehicles and hybrid electric vehicles. In this regard, to ensure performance reliability, accurate modeling of calendar life of such batteries is a necessity. In fact, potential failure of Li-ion battery packs remains a barrier to commercialization. Battery pack life is a critical feature to warranty and maintenance planning for hybrid vehicles, and will require adaptive control systems to account for the loss in vehicle range, and loss in battery charge and discharge efficiency. Failure not only results in large replacement costs, but also potential safety concerns such as overheating or short circuiting which may lead to fires.

Experimental studies of soot particles showed that the intensity ratio of amorphous and graphite layers measured by Raman spectroscopy correlates to soot oxidation reactivities, which is very important for regeneration of the diesel particulate filters and gasoline particulate filters. This physical mechanism is absent in all soot models. In the present paper, a novel two-layer soot model was proposed that considers the amorphous and graphite layers in the soot particles. The soot model considers soot inception, soot surface growth, soot oxidation by O2 and OH, and soot coagulation. It is assumed that amorphous-type soot forms from fullerene. No soot coagulation is considered in the model between the amorphous- and graphitic-types of soot. Benzene is taken as the soot precursor, which is formed from acetylene. The model was implemented into a commercial CFD software CONVERGE using user defined functions. A diesel engine case was simulated.

Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

In this study, application of differential braking strategy to remove the oscillatory instability or snaking behavior of an articulated steer vehicle is presented. First, a linearized model of the vehicle is described that is used to represent the equations of motion in the state-space form. Then, this model is utilized for designing a sliding mode controller to adjust the differential braking on the rear axle to stabilize the vehicle during the snaking. The performance of the resulting active control system is evaluated in different driving conditions by using the linearized model. Finally, the control system is incorporated into a virtual prototype of the vehicle in ADAMS, and its operation is examined. The results from the linear model analysis and simulations in ADAMS are reasonably consistent.

A three dimensional IR-TRACC (Infrared Telescope Rod for Assessment of Chest Compression) was designed for the Test Device for Human Occupant Restraint (THOR) in recent years to measure chest deflections. Due to the design intricateness, the deflection calculation from the measurements is sophisticated. An algorithm was developed in this paper to calculate the three dimensional deflections of the chest. The algorithm calculates the compression and also converts the results to the local spine coordinate system so that it can correlate with the Post Mortem Human Subject (PMHS) measurements for injury calculation. The method was also verified by a finite element calculation for accuracy, comparing the calculation from the corresponding model output and the direct point to point measurements. In addition, the IR-TRACC calibration methods are discussed in this paper.

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML within software has on the ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to better accommodate ML.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.