Dynamic Modelling and Stability Controller Development for Articulated Steer Vehicles

In this study, various stability control systems are developed to remove the lateral instability of a conventional articulated steer vehicle (ASV) during the oscillatory yaw motion or “snaking mode”. First, to identify the nature of the instability, some analyses are performed using several simplified models. These investigations are mainly focused on analyzing the effects of forward speed and of two main subsystems of the vehicle, the steering system and tires, on the stability. The basic insights into the stability behavior of the vehicle obtained from the stability analyses of the simplified models are verified by conducting some simulations with a virtual prototype of the vehicle in ADAMS. To determine the most critical operating condition with regard to the lateral stability and to identify the effects of vehicle parameters on the stability, various studies are performed by introducing some modifications to the simplified models. Based on these studies, the disturbed straight-line on-highway motion with constant forward speed is recognized as the most critical driving condition. Also, the examinations show that when the vehicle is traveling with differentials locked, the vehicle is less prone to the instability. The examinations show that when the vehicle is carrying a rear-mounted load having interaction with ground, the instability may happen if the vehicle moves on a relatively good off-road surface. Again, the results gained from the analyses related to the effects of the vehicle parameters and operating conditions on the stability are verified using simulations in ADAMS by making some changes in the virtual prototype for any case. To stabilize the vehicle during its most critical driving condition, some studies are directed to indicate the shortcomings of passive methods. Alternative solutions, including design of different types of stability control systems, are proposed to generate a stabilizing yaw moment. The proposed solutions include an active steering system with a classical controller, an active torque vectoring device with a robust full state feedback controller, and a differential braking system with a robust variable structure controller. The robust controllers are designed by using simplified models, which are also used to evaluate the ability to deal with the uncertainties of the vehicle parameters and its variable operating conditions. These controllers are also incorporated into the virtual prototype, and their capabilities to stabilize the vehicle in different operating conditions and while traveling on different surfaces during the snaking mode are shown

A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers

Razavian, R. S., Azad, N. L., & McPhee, J. (2013). A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers. International Journal of Electric and Hybrid Vehicles, 5(3), 177. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2013.057604We have developed a battery hardware-in-the-loop (HIL) setup, which can expedite the design and evaluation of power management controllers for hybrid electric vehicles (HEVs) in a novel cost- and time-effective manner. The battery dynamics have a significant effect on the HEV power management controller design; therefore, physical batteries are included in the simulation loop for greater simulation fidelity. We use Buckingham's Pi Theorem in the scaled-down battery HIL setup to reduce development and testing efforts, while maintaining the flexibility and fidelity of the control loop. In this paper, usefulness of the setup in parameter identification of a simple control-oriented battery model is shown. The model is then used in the power management controller design, and the real-time performance of the designed controller is tested with the same setup in a realistic control environment. Test results show that the designed controller can accurately capture the dynamics of the real system, from which the assumptions made in its design process can be confidently justified.Financial support for this research has been provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Toyota, and Maplesoft

An optimal gear-shifting strategy for heavy trucks with trade-off study between trip time and fuel economy

© Inderscience Enterprises Ltd. The original source of publication is available at InderScience. Zhao, X. x., Ing, A. h., Azad, N. l., & McPhee, J. (2015). An optimal gear-shifting strategy for heavy trucks with trade-off study between trip time and fuel economy. International Journal of Heavy Vehicle Systems, 22(4), 356–374. https://doi.org/10.1504/IJHVS.2015.073205We show how the fuel efficiency of heavy mining trucks can be improved by optimising the gear-shifting strategy. Using characteristic tests of the diesel engine, a high-fidelity model of a mining truck was built in MapleSim and a consistent low-order model was developed in Matlab. Dynamic programming was used to optimise the low-order model of the specialised off-road 30-tonne truck over a fixed route in a mining area. There were two competing objectives: fuel use and trip time, which were combined in a single objective function using weighting coefficients. A Pareto curve was created to analyse the effect of the weights on the fuel use and trip time. Applying the control strategy obtained from dynamic programming to the high-fidelity model, it is estimated that 40,000 L of fuel can be saved annually for a mine that produces 110 kilotonnes of coal per day

Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks

Predicting pedestrians' trajectories is a crucial capability for autonomous vehicles' safe navigation, especially in spaces shared with pedestrians. Pedestrian motion in shared spaces is influenced by both the presence of vehicles and other pedestrians. Therefore, effectively modelling both pedestrian-pedestrian and pedestrian-vehicle interactions can increase the accuracy of the pedestrian trajectory prediction models. Despite the huge literature on ways to encode the effect of interacting agents on a pedestrian's predicted trajectory using deep-learning models, limited effort has been put into the effective selection of interacting agents. In the majority of cases, the interaction features used are mainly based on relative distances while paying less attention to the effect of the velocity and approaching direction in the interaction formulation. In this paper, we propose a heuristic-based process of selecting the interacting agents based on collision risk calculation. Focusing on interactions of potentially colliding agents with a target pedestrian, we propose the use of time-to-collision and the approach direction angle of two agents for encoding the interaction effect. This is done by introducing a novel polar collision grid map. Our results have shown predicted trajectories closer to the ground truth compared to existing methods (used as a baseline) on the HBS dataset.Comment: Accepted for publication as a conference paper in IEEE Intelligent Transportation Systems Conference (ITSC), 202

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle's interactions with the pedestrians on pedestrians' future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.Comment: Published in IEEE Transactions on Intelligent Transportation System

Parametric Importance Analysis and Design Optimization of a Torque Converter Model Using Sensitivity Information

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.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 parametric importance analysis is performed on the model: the model parameters are arranged according to their effect on the amount loss of energy during the operation of the torque converter. The radii of the pump, turbine and stator, the density of the hydraulic fluid and the exit angle of the vanes of the stator were found to have the most significant effects on the model. Using the sensitivity information, a design optimization problem is defined and solved to obtain a set of parameter values that minimizes the energy lost during the torque converter operation.Financial support for this work has been provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Toyota, and Maplesoft

Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles

Razavian, R. S., Taghavipour, A., Azad, N. L., & McPhee, J. (2012). Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles. International Journal of Electric and Hybrid Vehicles, 4(3), 260. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2012.050501We propose a real-time optimal controller that will reduce fuel consumption in a series hybrid electric vehicle (HEV). This real-time drive cycle-independent controller is designed using a control-oriented model and Pontryagin's minimum principle for an off-line optimisation problem, and is shown to be optimal in real-time applications. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions, but the amount of information is reduced. Although the controller design procedure explained here is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to find the supervisory controller for a series HEV with an ultra-capacitor. To evaluate the performance of the model-based controller, it is coupled to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. The simulation results show that the simplified control-oriented model is accurate enough in predicting real vehicle behaviour, and final fuel consumption can be reduced using the model-based controller. Such a reduction in HEVs fuel consumption will significantly contribute to nationwide fuel saving.The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and Maplesoft for their support of this research

Optimal Gear-Shifting of a Wet-Type Two-Speed Dual-Brake Transmission for an Electric Vehicle

In improving the efficiency of powertrain systems and ride comfort for electric vehicles (EVs), the transmission model is required to enable more accessible and more straightforward control of such vehicles. In this study, a wet-type, two-speed, dual-brake transmission system, as well as a new electromechanical clutch actuator, is presented for EVs. A new coordinated optimal shifting control strategy is then introduced to avoid sharp jerks during shifting processes in the transmission system. Based on a state-space model of the electromechanical clutch actuator and dual-brake transmission, we develop a linear quadratic regulator strategy by considering ride comfort and sliding friction work to obtain optimal control trajectories of the traction and shifting motors under model-based control. Simulations and bench tests are carried out to verify the performance of the proposed control laws. Results of the proposed coordinated control strategy show that noticeable improvements in terms of vehicle jerk and friction energy loss are achieved compared with an optimal control scheme only for the shifting motor as the input