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Dynamic modelling of the contact between the tires of automobiles and the road surface is crucial for accurate and effective vehicle dynamic simulation and the development of various driving controllers. Furthermore, an accurate prediction of the rolling resistance is needed for powertrain controllers and controllers designed to reduce fuel consumption and engine emissions. Existing models of tires include physics-based analytical models, finite element based models, black box models, and data driven empirical models. The main issue with these approaches is that none of these models offer the balance between accuracy of simulation and computational cost that is required for the model-based development cycle. To address this issue, we present a volumetric approach to model the forces/moments between the tire and the road for vehicle dynamic simulations.

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

The present work is aimed at investigating the tribological factors influencing the LDH test. The material used was AKDQ cold-rolled bare steel, 0.82mm thick. The investigated factors included: test speed (0.833, 4.167, 6.667, and 8.333 mm/s), lubricant viscosity (4.5, 7.0, and 12.5 mm2/s), punch roughness (0.033 and 0.144 μm Ra), and test temperature (25 and 50 °C). Test speed and lubricant viscosity form a variation of the numerator of the Stribeck curve's x-axis (ηV). With ηV increasing from 4 to 120 mm3/s2 friction decreased, resulting in a 0.5 mm higher LDH. Increasing the punch roughness decreased friction producing an increase of 0.25 mm in the LDH. There appears to be an optimum roughness -- at which the roughness features act as lubricant reservoirs but the asperities do not break through the lubricant film -- resulting in minimum friction, therefore, maximum LDH.

Details of the development of metal transfer and friction were studied by drawing cold-rolled bare, galvannealed, electrogalvanized, and hot-dip galvanized strips with a mineral-oil lubricant of 30 cSt viscosity at 40 C, over a total distance of 2500 mm by three methods. An initial high friction peak was associated with metal transfer to the beads and was largest with pure zinc and smallest with Fe-Zn coatings. Insertion of a new strip disturbed the coating and led to the development of secondary peaks. Long-term trends were governed by the stability of the coating. Stearic acid added to mineral oil delayed stabilization of the coating and increased contact area and thus friction with pure zinc surfaces. The usual practice of reporting average friction values can hide valuable information on lubrication mechanisms and metal transfer.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

In electrified vehicle applications, the heat generated of lithium-ion (Li-ion) cells may significantly affect the vehicle range and state of health (SOH) of the pack. Therefore, a major design task is creation of a battery thermal management system with suitable control and cooling strategies. To this end, the thermal behavior of Li-ion cells at various temperatures and operating conditions should be quantified. In this paper, two different commercial pouch cells for plug-in hybrid electric vehicles (PHEVs) are studied through comprehensive thermal performance tests. This study employs a fractional factorial design of experiments to reduce the number of tests required to characterize the behavior of fresh cells while minimizing the effects of ageing. At each test point, the effects of ambient temperature and charge/discharge rate on several types of cell efficiencies and surface heat generation are evaluated.

A series plug-in hybrid electric powertrain with all-wheel drive is designed using real-world drive cycles as part of the EcoCAR 2 competition. A stock 2013 Chevrolet Malibu Eco is being re-engineered to reduce fuel consumption and emissions while improving consumer acceptability. Waterloo utilizes a 18.9 kWh A123 energy storage system (ESS), which powers two 105 kW TM4 traction motors. A 2.4 L LE9 General Motors coupled to a 105 kW TM4 motor provides range extending performance. Each step of the design process is discussed, including a novel approach to powertrain selection and controls requirement selection that uses real-world drive cycles. The mechanical integration and unique ESS design is also discussed.

The objective of this paper is to help students optimize project component selection or design by detailing, through two specific examples, the University of Waterloo's Alternative Fuels Team's (UWAFT's) successful design process. UWAFT successfully designed a fuel cell powered vehicle for the ChallengeX student engineering competition. The use of a formal, structured design process enabled this team to achieve great confidence in both the feasibility of their design and their ability to manifest the design. This design process is model-based whereby a parameterized software model is created. This paper hopefully assists students to overcome a common reluctance to implementing a model-based design process. After a component is constructed and tested, students can update their software model, which can help them assess the strength of their design.

A method to improve the computational efficiency of analyzing wheeled vehicle systems on three-dimensional (3-D) roads has been developed. This was accomplished by creating a technique to incorporate the tire on a 3-D road in a multibody dynamics model of the vehicle with an approach that formulates the governing equations using symbolic formulation. For general handling analysis performed on the vehicle, the tire forces and moments are determined using a tire model that represents the tire as a set of mathematical expressions. Since these expressions need numerical values to determine the forces and moments, a symbolic solution does not exist. Therefore, the evaluation of the tire forces and moments needs to be done during simulation. However, symbolic operations can be used when the governing equations are formulated to develop an efficient method to evaluate these forces.

This paper presents a road classifier based on the system response with consideration of the tire enveloping. The aim is to provide an easily applicable yet accurate road classification approach for automotive engineers. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. the sprung mass acceleration, unsprung mass acceleration, and rattle space, are calculated for a quarter vehicle model. The influence of parameter variations, vehicle velocity, and measurement noise on transfer functions are comprehensively analyzed to derive the most suitable system response thereafter. In addition, this paper proposes a vehicle speed correction mechanism to further improve the classification accuracy under complex driving conditions.

The present paper offers a status report on round-robin tests conducted with the participation of ten laboratories, with drawbead simulation (DBS) as the test method. The results showed that, in most laboratories, the coefficient of friction (COF) derived from the test is repeatable within an acceptable range of ±0.01. Repeatability between laboratories was less satisfactory. Five laboratories reported results within the desirable band, while some laboratories found consistently higher values. In one instance this could be traced to incomplete transfer of clamp forces to the load cell, in other instances inaccurate test geometry is suspected. Therefore, numerical values of COF from different laboratories are not necessarily comparable. Irrespective of these inter-laboratory variations, the relative ranking of lubricants was not affected, and data generated within one laboratory can be used for relative evaluations and for a resolution of production problems.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.

Robust lane marking detection remains a challenge, particularly in temperate climates where markings degrade rapidly due to winter conditions and snow removal efforts. In previous work, dynamic Bayesian networks with heuristic features were used with the feature distributions trained using semi-supervised expectation maximization, which greatly reduced sensitivity to initialization. This work has been extended in three important respects. First, the tracking formulation used in previous work has been corrected to prevent false positives in situations where only poor RANSAC hypotheses were generated. Second, the null hypothesis is reformulated to guarantee that detected hypotheses satisfy a minimum likelihood. Third, the computational requirements have been greatly reduced by computing an upper bound on the marginal likelihood of all part hypotheses upon generation and rejecting parts with an upper bound less likely than the null hypothesis.

It is imperative to have accurate tire models when trying to control the trajectory of a vehicle. With the emergence of autonomous vehicles, it is more important than ever before to have models that predict how the vehicle will operate in any situation. Many different types of tire models have been developed and validated, including physics-based models such as brush models, black box models, finite element-based models, and empirical models driven by data such as the Magic Formula model. The latter is widely acknowledged to be one of the most accurate tire models available; however, collecting data for this model is not an easy task. Collecting data is often accomplished through rigorous testing in a dedicated facility. This is a long and expensive procedure which generally destroys many tires before a comprehensive data set is acquired. Using a Vehicle Measurement System (VMS), tires can be modeled through on-road data alone.

In a conventional vehicle, the longitudinal shocks caused by the road obstacles cannot be effectively absorbed due to the fact that the longitudinal connections between the chassis and wheels are typically very stiff compared with the vertical strut where the regular spring is mounted. To overcome this limitation, a concept design of a planar suspension system (PSS) is proposed. The rather stiff longitudinal linkages are replaced by a spring-damping strut in a PSS so that the vibration along any direction in the wheel plane can be effectively isolated. For a vehicle with such suspension systems, the wheels can move forth and back with respect to the chassis. The wheelbase and load distribution at the front and rear wheels can change as a consequence of the implementation of the PSS on a vehicle. The planar system can induce changes in the vehicle dynamic behavior. This paper presents the overview introduction of a dynamic study of a vehicle with such suspension systems.

This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach.

Modern electrified vehicles rely on drivers to manually adjust control parameters to modify the vehicle's powertrain, such as regenerative braking strength selection or drive mode selection. However, this reliance on infrequent driver input may lead to a mismatch between the selected powertrain control modifiers and the true driving environment. It is therefore advantageous for an electric vehicle's powertrain controller to make online identifications of the current driving conditions. This paper proposes an online driving condition identification scheme that labels drive cycle intervals collected in real-time based on a clustering model, with the objective of informing adaptive powertrain control strategies. HDBSCAN and K-means clustering models are fitted to a data set of drive cycle intervals representing a full range of characteristic driving conditions.

The autoignition delay of a turbulent methane jet in a Diesel-like environment is calculated by the conditional moment closure(CMC) model. Methane is injected into hot air in a constant volume chamber under various temperatures and pressures. Detailed chemical reaction mechanisms are implemented with turbulence-chemistry interaction treated by the first order CMC. The CMC model solves the conditional mean species mass fraction and temperature equations with the source term given in terms of the conditional mean quantities. The flow and mixing field are calculated by the transient SIMPLE algorithm with the k -ε model and the assumed beta function pdf. The CMC equations are solved by the fractional step method which sequentially treats the transport and chemical reaction terms in each time step. The predictions in quiescent homogeneous mixture are presented to evaluate the effects of turbulence in jet ignition.

Vehicle weight reduction through the use of components made of magnesium alloys is an effective way to reduce carbon dioxide emission and improve fuel economy. In the design of these components, which are mostly under cyclic loading, notches are inevitably present. In this study, surface strain distribution and crack initiation sites in the notch region of AZ31B-H24 magnesium alloy notched specimens under uniaxial load are measured via digital image correlation. Predicted strains from finite element analysis using Abaqus and LS-DYNA material types 124 and 233 are then compared against the experimental measurements during quasi-static and cyclic loading. It is concluded that MAT_233, when calibrated using cyclic tensile and compressive stress-strain curves, is capable of predicting strain at the notch root. Finally, employing Smith-Watson-Topper model together with MAT_233 results, fatigue lives of the notched specimens are estimated and compared with experimental results.

Direct injected natural gas diesel engines are currently being developed. Numerical analyses results are presented for 20.0 MPa (≈ 3000 psia; 200 atm), 444 K, natural gas injection into 4.0 MPa cylinder air where the ambient turbulence field is representative of diesel engines. Two very important non-intuitive, observations are made. First, the seemingly reasonable spatially uniform velocity profile currently used at the injector exit is not appropriate, rather a double-hump profile is correct. Second, a spatially uniform, injector exit, temperature profile results in local temperature overestimates as large as 300 K. Considering the strong role of temperature on chemical kinetics, this second observation may have profound implications on the validity of conclusions reached using uniform exit profiles.