Electric vehicles use electric drive units (EDUs) to develop forces required for propulsion and braking. This paper considers a powertrain architecture with one EDU on the front axle and a second clutched EDU on the rear axle. Within these EDUs are permanent-magnet synchronous machines and their current-regulating power converters. The goal is to command the clutch and power converters to produce a desired force at a specified speed while minimizing power loss, to maximize range. A model-based formulation of this optimization problem is presented, which allows investigation of how model parameter changes influence the optimization results. The merits of this approach are demonstrated symbolically and numerically.

This paper poses and solves trajectory optimization problems arising in autonomous electric vehicles. Using a detailed physics-based model of the electric drive unit and vehicle, optimal trajectories are computed that balance the trade-off between time and energy, and comparisons with more traditional polynomial trajectories are provided.

We pose and solve a two-stage smart charging (SC) problem for a single residence equipped with an electric vehicle (EV), battery energy storage system (BESS), and solar photovoltaic panels. We study the differences between an example rule-based operation of BESS and our SC problem, seeking to minimize homeowner costs and perform peak-shaving. Furthermore, we compare unregulated EV charging and our two-stage SC algorithm. We apply our models to a single residence in the Southern US with historical home demand and solar generation data under a time-of-use (TOU) rate plan to study cost changes alongside aggregate demand for one year. We determined our two-stage SC solution to be more effective at minimizing electricity costs and demand spikes under a TOU rate plan when compared to the rule-based operation of BESS and unregulated EV charging.

This paper leverages phased array techniques to determine the positions of multiple reflectors in 3-D space using ultrasonic sensing. The design of a low-cost sensor, consisting of a single narrowband ultrasonic transducer and a receiver array consisting of a small number of MEMS microphones, is discussed. Aspects of system design related to the sensor, analog signal chain, and digital processing are presented. A processing algorithm is described to simultaneously determine the 3-D positions of all the reflectors present in the sensor’s workspace. Further, the processing algorithm is implemented on a low-cost microcontroller and requires low memory resources (64-70 kilobytes). Experiments demonstrate the performance achieved and the detection of multiple objects in the sensor’s workspace. A computation cost analysis is performed exhibiting the rapid execution of the algorithm. High processing throughput (20-45 Hz) is achieved making the sensor suitable for applications such as workspace mapping and the tracking of multiple reflectors.

This paper leverages machine learning principles in combination with an exploratory procedure to estimate the unknown stiffness of a grasped object in a single degree of freedom gripper mechanism. The object stiffness is estimated using regression; sensor measurements of actuator position and actuator current are the regressor inputs, and object stiffness is the regressor output. The proposed approach reduces overall system complexity and cost compared to systems that use tactile sensors to estimate object stiffness. Several datasets are generated using a physics-based simulation model of the system. Regressors are trained on data produced by the simulation model, and their performance is analyzed under variations on simulation model parameters including stick-slip friction parameters; the influence of the actuation command signal used to direct the exploratory procedure is also considered. These regressors are then experimentally evaluated on a representative single degree of freedom mechanism, demonstrating worst-case stiffness estimation errors (based on datasheet spring stiffness values) of 3.4%.

Flexural Ultrasonic Transducers (FUTs) exhibit sustained residual vibrations after their excitation has ceased. This results in a long decaying tail in the FUT’s terminal voltage affecting the Time of Flight estimation and ranging capability for reflectors situated near the FUT. Time optimal control principles are applied to design the excitations to accelerate damping, improve ToF estimation, and improve ranging near the FUT without compromise in the strength of the transmitted pressure wave. Numerical solutions for the control problem are obtained using Nonlinear Programming. An algorithm is described to operate Pulse Width Modulation peripherals found on commercial off-the-shelf microcontroller units to realize the excitation signals at the output of H-Bridge modules. Experimental results show an improvement of 15% in the minimum range with the designed time optimal excitations.

We pose and solve a smart charging problem for a single residence equipped with an electric vehicle (EV), energy storage, and solar panels. The smart charging problem is cast as a quadratic program in order to exploit existing solution algorithms and to efficiently detect problem feasibility. The objective function consists of a weighted sum of four performance metrics: cost of electricity from the utility, usage of renewable energy, charging urgency and battery degradation. Of these, the renewable energy metric is a novel focus of the work and considers both local and remote sources of renewable energy. Benefits of the proposed smart charging strategy to the EV owner are multiple: charging costs can be minimized in a price-uncertain environment, renewable energy usage can be maximized, and battery lifetime can be extended. These benefits are obtained with minimal computational effort due to our convex problem formulation. They also position our proposed smart charging algorithm for both embedded implementation and large scale simulation studies, in contrast to many non-convex formulations existing in the literature.

Many single-axis motion systems are driven by electric machines equipped with torque-regulating power converters, and the operation of such systems is often commanded using some type of speed controller. The design of the speed controller involves choosing both the reference command shapes and the signal flow architecture. This paper provides a solution to the problem of speed controller design where the goal is to minimize the electric energy required to transition from one speed to another, for both acceleration and deceleration; this topic is inspired by applications that involve frequently occurring speed transitions. Explicit formulas are derived that characterize the energy minimizing transition time, and the corresponding torque and speed trajectories, and these trajectories are fed as reference commands into a controller architecture that incorporates both feedback and feedforward actions. For comparison, results are also derived for the case in which torque is constrained to be constant throughout the speed transition interval.

The purpose of this paper is to develop a new control method for a hydraulic elevator equipped with a variable-speed bidirectional fixed-displacement pump and a converter-fed electric machine operating in torque regulation mode. Conventional hydraulic elevators use a bypass throttle valve to divert excess flow during upwards motion and restrict flow during downwards motion, resulting in wasted energy and fluid heating, whereas the proposed approach manipulates flow by varying the speed of the pump, thereby influencing motion in an energy efficient manner. A primary focus of this investigation has been to keep both the physical system and its controller as simple as possible, while meeting performance and robustness goals. All essential modeling and control details are provided, as well as a summary of extensive simulation results demonstrating the effectiveness of the proposed control method.

This paper proposes a new fine-scale (millimeter level) radio localization and tracking scheme-Hybrid Inertial Microwave Reflectometry-for radio frequency identification and other wireless systems. The scheme combines the backscattered radio frequency signal properties, such as received signal strength and received signal phase, along with reflected inertial data from a tag-mounted accelerometer sensor. Experimental results yield a mean error of 2 mm in ranging accuracy, with a 90% confidence interval of ±1 mm, while tracking a tag moving at a maximum speed of 1.4 m/s.

The reaction wheel pendulum is an underactuated electromechanical system consisting of a pendulum with an un-driven rotation axis at one end and a motor-driven wheel at the other end. Current flow in the motor induces a torque on the wheel and a corresponding reaction torque on the pendulum. This type of system has been studied by various researchers over the past decade, but in every case under the assumption that a brush-commutated permanent-magnet dc motor actuates the system. The purpose of this paper is to extend prior work by actuating the system instead with a brushless three-phase permanent-magnet synchronous ac motor. A systematic approach to modeling and control is pursued for the problem of balancing at the inverted equilibrium. Both state-feedback and output-feedback controllers are designed on the basis of a reduced-order model, and the designs are compared through simulation and analysis. Linear quadratic optimization is used to obtain both regulator and estimator gains, and a loop transfer recovery procedure is used so that the loop gain of the output-feedback design approximates that of the state-feedback design.

Hybrid electric vehicle powertrains, especially those based on the power-split architecture, are complex dynamic systems that must be modeled in a systematic way in order to fully exploit an organized approach to control design. Much of the existing literature focuses on a high-level treatment of the modeling topic, typically emphasizing steady-state behavior. The present paper is motivated by the need for a general approach to lower-level dynamic modeling that can be applied for analysis, control and simulation of vehicles that feature the power-split architecture. A dynamic model and an associated integral controller are first developed in symbolic form. The resulting general formulation is then specialized in order to perform numerical simulations. Particular attention is devoted to an orderly method for computing controller feedback gains.

A contender for direct-drive application, the switched reluctance motor, is examined. It is first described how the number of teeth on the rotor and stator can be selected to achieve an electromagnetic gearing, with the goal of eliminating the need for a mechanical gearbox. It is also discussed how the tooth arcs of the rotor and stator can be adjusted so as to reduce the torque ripple of the motor during commutation. The treatment of these subjects includes a detailed case study in which the performance of four related motors is compared using finite-element analysis with a commutation algorithm for torque control. It is discovered that, although a tradeoff exists between peak torque and torque ripple, the maximum smooth torque and minimum torque ripple can be achieved by the same direct-drive motor design.>