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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).

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

Torque converters are used as coupling devices in automobile powertrains involving automatic transmissions. Efficient modeling of torque converters capturing various modes of operation is important for powertrain design and simulation, (Hroval and Tobler 1, Ishihara and Emori 2) optimization and control applications. Models of torque converters are available in various commercial simulation packages, Hadi et. al. 3. The information about the effect of model parameters on torque converter performance is valuable for any design operation. In this paper, a symbolic sensitivity analysis of a torque converter model will be presented. Direct differentiation (Serban and Freeman 4) is used to generate the sensitivity equations which results in equations in symbolic form. By solving the sensitivity equations, the effect of a perturbation of the model parameters on the behavior of the system is determined.

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.

A fuel cell hybrid powertrain design is implemented and optimized by the University of Waterloo Alternative Fuels Team for the ChallengeX competition. A comprehensive set of bench-top and in-vehicle validation results are used to generate accurate fuel cell vehicle models for SIL/HIL control strategy testing and tuning. The vehicle is brought to a “99% buy-off” level of production readiness, and a detailed crashworthiness analysis is performed. The vehicle performance is compared to Vehicle Technical Specifications (VTS).

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.

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.

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 discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo’s self-driving vehicle project. The powertrain of Waterloo’s vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle’s power management strategy cannot be altered, so a novel approach to grey-box modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle’s torque control module and estimate the distribution of torque between the powertrain’s internal combustion engine and electric motors. The vehicle’s power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach.

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.

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.

Historically slip in a CVT was regarded as destructive. The reason for this was that slip was not controllable and since it is unstable always resulted in damage to the variator. Recent publications suggest that limited amounts of slip in a pushbelt type variator can be allowed [6]. This opens the door to other strategies for lowering the powerconsumption of CVT's. Not only can slip be used for optimizing variator efficiency [1], actuation efficiency can also be greatly improved. If the safety margin is eliminated, the clamping force can be reduced by more than 25%. This can be directly translated into a 25% decrease in actuation power. Shifting behaviour is also influenced by slip [3]. This effect can be used to greatly reduce the power needed for fast shifting during emergency stopping, tip-shifting and kickdown actions. Using this strategy the force needed for shifting is reduced, and with it the power needed from the actuation system is reduced.

This paper presents a novel Power Take Off system, using a Continuously Variable Transmission for power levels up to 45 kW. The system, intended for driving auxiliary equipment on distribution trucks, is developed in order to comply with new restrictions concerning exterior noise, fuel consumption and emissions as well as to improve the performance of the driven equipment.

By using the ability to shift to high overdrive ratios, a CVT equipped vehicle can outperform the fuel economy of its counterpart with conventional automatic transmission. A further signi cant reduction of fuel consumption can be obtained by reducing transmission power loss. The main sources of power loss are well known to be losses inside the V-belt variator and losses caused by driving the hydraulic pump. Signi - cant reduction of these losses will increase the attractiveness of the V-belt type CVT. This paper rst analyses the most important loss contributions present in a reference transmission and how they depend on actuation and control properties, like pump driving power, over-clamping and variator slip. Based on this analysis, actuation and control improvements will be proposed. The effciency increase is presented, that can be expected when the hydraulic pump is replaced by a servo-electromechanical actuation system, and when variator slip control is introduced.

Apart from enabling continuous ratio change under load, the Continuously Variable Transmission (CVT) offers, between limits, the ability to choose engine rotational speed independently of vehicle speed. Here lies a potential efficiency benefit, because the engine can operate more fuel efficiently. There are unfortunately considerable power losses within the CVT itself, causing many CVT equipped cars to be less fuel-efficient than cars with a manual transmission. The internal losses are caused for a substantial part by the CVT’s hydraulic actuation. An electromechanical CVT pulley actuation system was designed to overcome the hydraulic power loss and hence improve CVT efficiency. Spindles are driven from the fixed world through epicyclic gearings by electric motors that are placed outside the transmission housing in a cool environment. A mechanical link between the adjustment mechanisms on the two shafts provides energy exchange, thus lowering shifting power demand and actuator size.

For analysis, control design and testing of an electromechanically actuated metal V-belt type CVT, a simulation model is built. Due to its complexity and nonlinear behavior, this model is not suitable for control design. To use control design techniques like H1 or μ-synthesis, linear transfer functions from all inputs to all outputs must be known. Using approximate realization techniques, step responses simulated with the model are analyzed. In this way, a linearized state-space representation of the system is obtained. The transfer functions show resonances around 8−10 [Hz], depending on the CVT ratio. Using MIMO-control, closed loop bandwidths that could be obtained are up to 10 [Hz] for ratio control and 15 [Hz] for slip control.

This paper concentrates on the design and construction aspects of a transmission for a mid-class passenger car with internal combustion engine. The transmission, consisting of a Continuously Variable Transmission (CVT) with a Van Doorne V-belt, a planetary gear set and a compact steel flywheel is used to prove the concept of mechanical torque assist. The design goal is to obtain a proof of concept transmission with maximal efficiency, using proven transmission technology. With the developed so-called Zero Inertia CVT, the fuel economy of the car is improved by operating the engine at its fuel optimal operating line. To achieve a good vehicle acceleration response, the flywheel assists the powertrain mechanically. Low-cost transmission technology is used to upgrade the CVT design (gears, bearings and a steel rotor). The test results, regarding the functionality and the efficiency of the transmission, will be available in due time.

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.