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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).

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.

The development of a comprehensive battery simulator is essential for future improvements in the durability, performance and service life of lithium-ion batteries. Although simulations can never replace actual experimental data, they can still be used to provide valuable insights into the performance of the battery, especially under different operating conditions. In addition, a single-cell model can be easily extended to the pack level and can be used in the optimization of a battery pack. The first step in building a simulator is to create a model that can effectively capture both the voltage response and thermal behavior of the battery. Since these effects are coupled together, creating a robust simulator requires modeling both components. This paper will develop a battery simulator, where the entire battery model will be composed of four smaller submodels: a heat generation model, a thermal model, a battery parameter model and a voltage response model.

Energy management strategies greatly influence the power performance and fuel economy of series hybrid electric tracked bulldozers. In this paper, we present a procedure for the design of a power management strategy by defining a cost function, in this case, the minimization of the vehicle’s fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, a dynamic programming (DP) algorithm is utilized to determine the optimal control actions for a series hybrid powertrain, and this can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that this procedure results in approximately a 7% improvement in fuel economy.

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.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The automobile industry has been undergoing a transition from fossil fuels to a low emission platform due to stricter environmental policies and energy security considerations. Electric vehicles, powered by lithium-ion batteries, have started to attain a noticeable market share recently due to their stable performance and maturity as a technology. However, electric vehicles continue to suffer from two disadvantages that have limited widespread adoption: charging time and energy density. To mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range. This work seeks to compare various powertrains, including: combined power battery electric vehicles (BEV) (zinc-air and lithium-ion battery), zero emission fuel cell vehicles (FCV)), conventional gasoline powered vehicles (baseline internal combustion vehicle), and ICE engine extended range hybrid electric vehicle.

The drive to improve and optimize hybrid vehicle performance is increasing with the growth of the market. With this market growth, the automotive industry has recognized a need to train and educate the next generation of engineers in hybrid vehicle design. The University of Waterloo Alternative Fuels Team (UWAFT), as part of the EcoCAR 3 competition, has developed a control strategy for a novel parallel-split hybrid architecture. This architecture features an engine, transmission and two electric motors; one pre-transmission motor and one post-transmission motor. The control strategy operates these powertrain components in a series, parallel, and all electric power flow, switching between these strategies to optimize the energy efficiency of the vehicle. Control strategies for these three power flows are compared through optimization of efficiencies within the powertrain.

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.

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.

A predictive model of a specific vehicle was modeled in the system-level physical modeling tool, MapleSim, for performance and fuel consumption prediction of a full vehicle powertrain, driving a multi-body chassis model with tire models. The project also includes investigation into overall fuel efficiency and effect on vehicle handling for different drive cycles. The goals of this project were to investigate: 1) the relationships between the forces at tire/road interfaces during various drive cycles and the fuel efficiency of a vehicle, and 2) the interaction between the powertrain and the chassis of the vehicle. To accomplish these goals, a complete vehicle model was created in the lumped-parameter physical modeling tool, MapleSim. A great deal of effort has gone into using real parameters and to assure that some mathematical rigour has been employed in its development.

The recent increase in oil prices and environmental concerns have attracted various research efforts on hybrid electric vehicles (HEVs) which provide promising alternatives to conventional engine-powered vehicles with better fuel economy and fewer emissions. To speed up the design and prototyping processes of new HEVs, a method that automatically generates mathematics equations governing the vehicle system response in an optimized symbolic form is desirable. To achieve this goal, we employed MapleSimTM, a new physical modeling tool developed by Maplesoft Inc., to develop the multi-domain model of a series-HEV, utilizing the symbolic computing algorithms of Maple software package to generate an optimized set of governing equations. The HEV model consists of a mean-value internal combustion engine (ICE), a chemistry-based Ni-MH battery pack, and a multibody vehicle model. Simulations are then used to demonstrate the performance of the developed HEV system.

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.