Search Results

In order to improve the performance of automatic emergency steering and collision avoidance of intelligent vehicle, two automatic steering control methods under ideal model following control are proposed. The two ideal reference models are the reference model with zero sideslip angle of vehicle gravity center and the reference model with no phase-lag in vehicle lateral acceleration. The control system adopts the combination of outer loop and inner loop. In the design of the outer loop controller, the optimal control is used to get the steering wheel angle needed to avoid collision. The inner loop controller uses feedforward and feedback control to get the required front and rear wheel steering angles. Taking vehicle two degrees of freedom (DOF) lateral dynamics model as the research object, the vehicle collision avoidance reference trajectory is obtained through the fifth-degree polynomial.

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.

The vehicle axle gear whine noise and vibration are key issues for the automotive industry to design a quiet, reliable driveline system. The main source of excitation for this vibration energy comes from hypoid gear transmission error (TE). The vibration transmits through the flexible axle components, then radiates off from the surface of the housing structure. Thus, the design of hypoid gear pair with minimization of TE is one way to control the dynamic behavior of the vehicle axle system. In this paper, an approach to obtain minimum TE and improved dynamic response with optimal tooth profile modification parameters is discussed. A neural network algorithm, named Back Propagation (BP) algorithm, with improved Particle Swarm Optimization (PSO) is used to predict the TE if some tooth profile modification parameters are given to train the model.

The meshing noise of hypoid gear has a significant influence on driving axle system. It should be strictly controlled in order to reduce the whole vehicle noise. Meshing internal excitation of hypoid gear is a main source of vibration noise, closely connected with geometrical shape and meshing status. There is no comprehensive analysis on the impact of various contact patterns on vibration noise in previous studies. Therefore, the method for controlling contact characteristics of hypoid gears is studied in this paper, which includes adjusting the position and length of contact pattern, direction of contact trace and the theoretical transmission error. Also, a non-linear dynamic model with multi-freedom for the hypoid gear pair of the driving axle is established to evaluate the dynamic response of the gear pair. Then an example was carried out to improve the dynamic characteristic of hypoid gears by tooth profile modification.

This paper proposed a path coordination strategy for autonomous parking based on independently designed parking lot topological map. The strategy merges two types of paths at the three stages of path planning, to determinate mode switching timing between low-speed automated driving and automated parking. Firstly, based on the principle that parking spaces should be parallel or vertical to a corresponding path, a topological parking lot map is designed by using the point cloud data collected by LiDAR sensor. This map is consist of road node coordinates, adjacent matrix and parking space information. Secondly, the direction and lateral distance of the parking space to the last node of global path are used to decide parking type and direction at parking planning stage. Finally, the parking space node is used to connect global path and parking path at path coordination stage.

One-dimensional (1D) simulation tools, the computing speed of which is relatively fast, usually solve simple complexity problems. The solving process of 1D simulation is mostly based on one-dimensional dynamic equations and empirical laws and thus in some cases it cannot obtain a similar accuracy with the time-consuming three-dimensional (3D) simulation tools. The 1D-3D co-simulation, which combines the advantages of the two simulation tools while minimizes the disadvantages, is a method that integrates and runs the two simulation tools concurrently. The coupled simulation can offer a 3D analysis for which a detailed information is needed while offer system level information in the rest of the whole system where averaged results are sufficient. The approach not only minimizes the computational cost, but avoids demand for imposing accurate boundary conditions to the 3D simulation.

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.

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.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.

Minimizing the stress concentrations around cutouts in a plate is often a design problem, especially in the Aerospace industry. A problem of optimizing spatially varying fiber paths in a symmetric, linear orthotropic composite laminate with a cutout, so as to achieve minimum stress concentration under remote unidirectional tensile loading is of interest in this study. A finite element (FE) model is developed to this extent, which constraints the fiber angles while optimizing the fiber paths, proving essential in manufacturing processes. The idea to be presented could be used to derive fiber paths that would drastically reduce the Stress Concentration Factor (SCF) in a symmetric laminate by using spatially varying fibers in place of unidirectional fibers. The model is proposed for a four layer symmetric laminate, and can be easily reproduced for any number of layers.

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material.

The transient response characteristics of a vehicle under steering angle input are important evaluating indicators of vehicle handling stability. For a new developed vehicle, which was found that the transient response under steering angle input is too slow at high speed, a rigid-flexible coupling vehicle model is established in ADAMS/Car based on multi-body dynamics theory. Improvement measures are studied and put forward to improve the transient response characteristics of the vehicle. The sensitivities of transient response to various parameters are analyzed. The optimization method of adjusting the tire cornering stiffness and moving forward the mass center is adopted. The test data after improvement show that the response time of yaw velocity is shortened obviously. Meanwhile, the value of evaluation index in other tests remains basically unchanged.

Parameter identification of a math-based spark-ignition engine model is studied in this paper. Differential-algebraic equations governing the dynamic behavior of the engine combustion model are derived using a quasi-dimensional modelling scheme. The model is developed based on the two-zone combustion theory with turbulent flame propagation through the combustion chamber [1]. The system of equations includes physics-based equations combined with the semi-empirical Wiebe function. The GT-Power engine simulator software [2], a powerful tool for design and development of engines, is used to extract the reference data for the engine parameter identification. The models is GT-Power are calibrated and validated with experimental results; thus, acquired data from the software can be a reliable reference for engine validation purposes.

Physical rig testing of a vehicle is often undertaken to obtain experimental data that can be used to ensure a mathematical model is an accurate representation of the vehicle under study. Kinematics and Compliance (K&C) testing is often used for this purpose. The relationship between the hard point locations and compliance parameters, and K&C characteristics of a suspension system is complex, and so automating the process to correlate the model to the test data can make the exercise easier, faster and more accurate than hand tuning the model. In this work, such a process is developed. First, the model parameters are adjusted, next a simulation is run, before the results are read and post processed. This automation processed is used in conjunction with an optimization procedure to carry out the K&C correlation.

An electric vehicle model has been developed with four direct-drive in-wheel motors. A high-level vehicle stability controller is proposed, which uses the principles of fuzzy logic to determine the corrective yaw moment required to minimize the vehicle sideslip and yaw rate errors. A genetic algorithm has been used to optimize the parameters of the fuzzy controller. The performance of the controller is evaluated as the vehicle is driven through a double-lane-change maneuver. Preliminary results indicate that the proposed control system has the ability to improve the performance of the vehicle considerably.

A serpentine flow channel can be considered as neighboring channels connected in series, and is one of the most common and practical channel layouts for PEM fuel cells since it ensures the removal of liquid water produced in a cell with excellent performance and acceptable parasitic load. During the reactant flows along the flow channel, it can also leak or cross directly to the neighboring channel via the porous gas diffusion layer due to the high pressure gradient caused by the short distance. Such a cross flow leads to a larger effective flow area resulting in a substantially lower amount of pressure drop in an actual PEM fuel cell compared to the case without cross flow. In this work, an analytical solution is obtained for the cross flow in a PEM fuel cell with a serpentine flow channel based on the assumption that the velocity of cross flow is linearly distributed in the gas diffusion layer between two successive U-turns.

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).

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.

Many design problems are discovered often late in the development process, when design flexibility is limited. It is the art of the refinement engineers to find a solution to any unpredicted issues at this stage. The refinement process contains many hours of testing and requires many prototypes. Having an accurate experimental model of the system in this phase could reduce refinement time significantly. One of the areas that usually require refinement and tuning late in the design process is engine and body mounting systems. In this paper, we introduce a technique to optimize the mounting system of a vehicle for a given objective function using experimental/numerical analysis. To obtain an accurate model of the vehicle, we introduce an experimental procedure based upon the substructuring method. The method eliminates the need for any accurate finite element method of the vehicle. Experimental results of the implementation of this approach to a real vehicle are presented.