Stability Control of Electric Vehicles with In-wheel Motors

Recently, mostly due to global warming concerns and high oil prices, electric vehicles have attracted a great deal of interest as an elegant solution to environmental and energy problems. In addition to the fact that electric vehicles have no tailpipe emissions and are more efficient than internal combustion engine vehicles, they represent more versatile platforms on which to apply advanced motion control techniques, since motor torque and speed can be generated and controlled quickly and precisely. The chassis control systems developed today are distinguished by the way the individual subsystems work in order to provide vehicle stability and control. However, the optimum driving dynamics can only be achieved when the tire forces on all wheels and in all three directions can be influenced and controlled precisely. This level of control requires that the vehicle is equipped with various chassis control systems that are integrated and networked together. Drive-by-wire electric vehicles with in-wheel motors provide the ideal platform for developing the required control system in such a situation. The focus of this thesis is to develop effective control strategies to improve driving dynamics and safety based on the philosophy of individually monitoring and controlling the tire forces on each wheel. A two-passenger electric all-wheel-drive urban vehicle (AUTO21EV) with four direct-drive in-wheel motors and an active steering system is designed and developed in this work. Based on this platform, an advanced fuzzy slip control system, a genetic fuzzy yaw moment controller, an advanced torque vectoring controller, and a genetic fuzzy active steering controller are developed, and the performance and effectiveness of each is evaluated using some standard test maneuvers. Finally, these control systems are integrated with each other by taking advantage of the strengths of each chassis control system and by distributing the required control effort between the in-wheel motors and the active steering system. The performance and effectiveness of the integrated control approach is evaluated and compared to the individual stability control systems, again based on some predefined standard test maneuvers

Development of a Path-following and a Speed Control Driver Model for an Electric Vehicle

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.A two-passenger all-wheel-drive urban electric vehicle (AUTO21EV) with four in-wheel motors and an active steering system has been designed and developed at the University of Waterloo. In order to evaluate the handling and performance of such a vehicle in the design stage and analyze the effectiveness of different chassis control systems before implementing them in the real vehicle, the simulation of a large number of different open-loop and closed-loop test maneuvers is necessary. Thus, in the simulation environment, not only is a mathematical vehicle model needed for every test maneuver, but a driver model must also be designed to simulate the closed-loop test maneuvers. The role of the driver model is to calculate the control inputs required to successfully follow a predefined path. Such a driver model can be implemented as an inverse dynamics problem or by a representation of a driver that can look ahead, preview the path, and change the steering wheel angle and acceleration or brake pedal positions accordingly. In this regard, a path-following driver model is developed in this work with an advanced path previewing technique. In addition, a gain scheduling speed control driver model is developed for the AUTO21EV, which adjusts the drive torques of the wheels to minimize the deviation between the desired and actual vehicle speeds.Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada and agrant from AUTO21, a Canadian Network of Centres of Excellence

Development of an Advanced Fuzzy Active Steering Controller and a Novel Method to Tune the Fuzzy Controller

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.A two-passenger, all-wheel-drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors has been designed and developed at the University of Waterloo. An advanced genetic-fuzzy active steering controller is developed based on this vehicle platform. The rule base of the fuzzy controller is developed from expert knowledge, and a multi-criteria genetic algorithm is used to optimize the parameters of the fuzzy active steering controller. To evaluate the performance of this controller, a computational model of the AUTO21EV is driven through several standard test maneuvers using an advanced path-following driver model. As the final step in the evaluation process, the genetic-fuzzy active steering controller is implemented in a hardware- and operator-in-the-loop driving simulator to confirm its performance and effectiveness.Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada and agrant from AUTO21, a Canadian Network of Centres of Excellenc

Development of an Integrated Control Strategy Consisting of an Advanced Torque Vectoring Controller and a Genetic Fuzzy Active Steering Controller

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.The optimum driving dynamics can be achieved only when the tire forces on all four wheels and in all three coordinate directions are monitored and controlled precisely. This advanced level of control is possible only when a vehicle is equipped with several active chassis control systems that are networked together in an integrated fashion. To investigate such capabilities, an electric vehicle model has been developed with four direct-drive in-wheel motors and an active steering system. Using this vehicle model, an advanced slip control system, an advanced torque vectoring controller, and a genetic fuzzy active steering controller have been developed previously. This paper investigates whether the integration of these stability control systems enhances the performance of the vehicle in terms of handling, stability, path-following, and longitudinal dynamics. An integrated approach is introduced that distributes the required control effort between the in-wheel motors and the active steering system. Several test maneuvers are simulated to demonstrate the performance and effectiveness of the integrated control approach, and the results are compared to those obtained using each controller individually. Finally, the integrated controller is implemented in a hardware- and operator-in-the-loop driving simulator to further evaluate its effectiveness.Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada and agrant from AUTO21, a Canadian Network of Centres of Excellenc

Development of a Fuzzy Slip Control System for Electric Vehicles with In-wheel Motors

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.A two-passenger all-wheel drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors and an active steering system has been designed and developed at the University of Waterloo. A novel fuzzy slip control system is developed for this vehicle using the advantage of four in-wheel motors. A conventional slip control system uses the hydraulic brake system in order to control the tire slip ratio, which is the difference between the wheel center velocity and the velocity of the tire contact patch along the wheel plane, thereby influencing the longitudinal dynamics of a vehicle. The advantage of the proposed fuzzy slip controller is that it acts as an ABS system by preventing the tires from locking up when braking, as a TCS by preventing the tires from spinning out when accelerating. More importantly, the proposed slip controller is also capable of replacing the entire hydraulic brake system of the vehicle by automatically distributing the braking force between the wheels using the available braking torque of the in-wheel motors. In this regard, the proposed fuzzy slip controller guarantees the highest traction or braking force on each wheel on every road condition by individually controlling the slip ratio of each tire with a much faster response time. The performance of the proposed fuzzy slip controller is confirmed by driving the AUTO21EV through several test maneuvers using a driver model in the simulation environment. As the final step, the fuzzy slip controller is implemented in a hardware- and operator-in-the-loop driving simulator and its performance and effectiveness is confirmed.Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada(NSERC) and a grant from AUTO21, a Canadian Network of Centres of Excellence

Development of an Advanced Torque Vectoring Control System for an Electric Vehicle with In-Wheel Motors using Soft Computing Techniques

Replicated with permission by SAE Copyright © 2017 SAE International. Further distribution of this material is not permitted without prior permission from SAE.A two-passenger, all-wheel-drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors has been designed and developed at the University of Waterloo. A 14-degree-of-freedom model of this vehicle has been used to develop a genetic fuzzy yaw moment controller. The genetic fuzzy yaw moment controller determines the corrective yaw moment that is required to stabilize the vehicle, and applies a virtual yaw moment around the vertical axis of the vehicle. In this work, an advanced torque vectoring controller is developed, the objective of which is to generate the required corrective yaw moment through the torque intervention of the individual in-wheel motors, stabilizing the vehicle during both normal and emergency driving maneuvers. Novel algorithms are developed for the left-to-right torque vectoring control on each axle and for the front-to-rear torque vectoring distribution action. Several maneuvers are simulated to demonstrate the performance and effectiveness of the proposed advanced torque vectoring controller, and the results are compared to those obtained using the ideal genetic fuzzy yaw moment controller. The advanced torque vectoring controller is also implemented in a hardware- and operator-in-the-loop driving simulator to further evaluate its performance.Funding for this work was provided by the Natural Sciences and Engineering Research Council of Canada and agrant from AUTO21, a Canadian Network of Centres of Excellenc

Multi-Criteria Decision-Making for Optimization of Product Disassembly under Multiple Situations

With growing interest in recovering materials and subassemblies within consumer products at the end of their useful life, there has been an increasing interest in developing decision-making methodologies that determine how to maximize the environmental benefits of end-oflife (EOL) processing while minimizing costs under variable EOL situations. This paper describes a methodology to analyze how product designs and situational variables impact the Pareto set of optimal EOL strategies with the greatest environmental benefit for a given economic cost or profit. Since the determination of this Pareto set via enumeration of all disassembly sequences and EOL fates is prohibitively time-consuming even for relatively simple products, multi-objective genetic algorithms (GA) are utilized to rapidly approximate the Pareto set of optimal EOL tradeoffs between cost and environmentally conscious actions. Such rapid calculations of the Pareto set are critical to better understand the influence of situational variables on how disassembly and recycling decisions change under different EOL scenarios (e.g., under variable regulatory, infrastructure, or market situations). To illustrate the methodology, a case study involving the EOL treatment of a coffee maker is described. Impacts of situational variables on tradeoffs between recovered energy and cost in Aachen, Germany, and in Ann Arbor, MI, are elucidated, and a means of presenting the results in the form of a multi-situational EOL strategy graph is described. The impact of the European Union Directive regarding Waste Electric and Electronic Equipment (WEEE) on EOL trade-offs between energy recovery and cost was also considered for both locations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87210/4/Saitou31.pd

Student’s Behavioral Abnormalities in Markazi and Hamedan Provinces

Background and objectives: Behavioral anomalies in the behavior of a person with social values are defied as the extent of the inconsistency. Since students are the future managers of community behavioral health Therefore, it seems necessary. The purpose of this research is to develop the student’s behavior anomalies Atlantic and central provinces of Hamedan. Material and methods: This study is the objective, functional, and due to a combination of survey methods and causal comparative. Using a standard questionnaire forms Aachen Bach TRF and YSR were collected. The data in the fild and independent groups’t-test was used in the content analysis of qualitative analysis. Results: The results showed that the prevalence of behavioral disorders in the Markazi and Hamadan provinces are different (t=13.42, P>0.0001). The results showed that the prevalence of behavioral disorders in boys and girls was not a signifiant difference (t=-1.50, P<0.05). Conclusion: The prevalence of behavioral disorders in different groups according to sex, age and place of residence is different