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-of-life (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 trade-offs 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., undervariable 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 trade-offs 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.

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.

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.

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.

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