An Optimal Power Management Strategy for Hydraulic Hybrid Output Coupled Power-Split Transmission

Power-split drive represents a class of Continuously Variable Transmission (CVT) that combines the convenience of CVT with the high overall transmission efficiency. In its hybrid configuration, a high pressure accumulator is used to capture the braking energy that is regenerated to aid the engine power during the next propulsion event. Output coupled power split drives are particularly suited for small and medium duty vehicle applications. In this work, optimal power management strategy has been designed based on Dynamic Programming approach. Although the control strategy obtained by Dynamic Programming is non-causal, it represents the benchmark solution against which other implementable power management schemes can be compared. Another control strategy based on instantaneous optimization is also discussed where a given cost function is minimized at every instant. It results in a sub-optimal solution that is practical and implementable. Finally, Dynamic Programming results are utilized to discuss the possible improvements that can be made to the instantaneous optimization based control strategy.

Component and Full Scale Tests for Suspension Model Validation

The National Crash Analysis Center (NCAC) at the George Washington University (GWU) has been developing and maintaining a public domain library of LS-DYNA finite element (FE) vehicle models for use in transportation safety research. The recent addition to the FE model library is the 2007 Chevrolet Silverado FE model. This FE model will be extensively used in roadside hardware safety research. The representation of the suspension components and its response in oblique impacts into roadside hardware are critical factors influencing the predictive capability of the FE model. To improve the FE model fidelity and applicability to the roadside hardware impact scenarios it is important to validate and verify the model to multitude of component and full scale tests. This paper provides detailed description of the various component and full scale tests that were performed, specifically, to validate the suspension model of the 2007 Chevrolet Silverado FE model.

Role of Trip Information Preview in Fuel Economy of Plug-In Hybrid Vehicles

This paper investigates the role of partial or complete knowledge of future driving conditions in fuel economy of Plug-in Hybrid Vehicles (PHEVs). We show that with the knowledge of distance to the next charging station only, substantial reduction in fuel use, up to 18%, is possible by planning a blended utilization of electric motor and the engine throughout the entire trip. To achieve this we formulate a modified Equivalent Consumption Minimization Strategy (ECMS) which takes into account the traveling distance. We show further fuel economy gain, in the order of 1–5%, is possible if the future terrain and velocity are known; we quantify this additional increase in fuel economy for a number of velocity cycles and a hilly terrain profile via deterministic dynamic programming.

Soft Switching Approach to Reducing Transition Losses in an On/Off Hydraulic Valve

A method for significantly reducing the losses associated with an on/off controlled hydraulic system is proposed. There has been a growing interest in the use of on/off valves to control hydraulic systems as a means of improving system efficiency. While on/off valves are efficient when they are fully open or fully closed, a significant amount of energy can be lost in throttling as the valve transitions between the two states. A soft switching approach is proposed as a method of eliminating the majority of these transition losses. The operating principle of soft switching is that fluid can temporarily flow through a check valve or into a small chamber while valve orifices are partially closed. The fluid can then flow out of the chamber once the valve has fully transitioned. Thus, fluid flows through the valve only when it is in its most efficient fully open state. A model of the system is derived and simulated, with results indicating that the soft switching approach can reduce transition and compressibility losses by 79%, and total system losses by 66%. Design equations are also derived. The soft switching approach has the potential to improve the efficiency of on/off controlled systems and is particularly important as switching frequencies are increased. The soft switching approach will also facilitate the use of slower on/off valves for effective on/off control; in simulation, a valve with soft switching matched the efficiency an on/off valve that was 5 times faster.

Feasibility Study on the Use of a Voice Coil Motor Direct Drive Flow Rate Control Valve

This paper considers the feasibility of a new type of voice coil motor direct drive flow control servo valve. The proposed servo valve controls the flow rate using only a direct measurement of the spool position. A neural network is used to estimate the flow rate based on the spool position, velocity and coil current. The estimated flow rate is fed back to a closed loop controller. The feasibility of the concept is established using simulation techniques only at this point. All results are validated by computer co-simulation using AMESim and Simulink. A simulated model of a VCM-DDV (Voice Coil Motor-Direct Drive Valve) and hydraulic test circuit are built in an AMESim environment. A virtual digital controller is developed in a Simulink environment in which the feedback signals are received from the AMESim model; the controller outputs are sent to the VCM-DDV model in AMESim (by interfacing between these two simulation packages). A LQR (Linear Quadratic Regulator) state feedback and nonlinear compensator controller for spool position tracking is considered as this is the first step for flow control. A flow rate control loop is subsequently included via a neural network flow rate estimator. Simulation results show that this method could control the flow rate to an acceptable degree of precision, but only at low frequencies. This kind of valve can find usage in open loop hydraulic velocity control in many industrial applications.

Free-Locomotion of a Fish-Like Robotic Swimmer Propelled by a Vibrating Ionic Polymer Metal Composite

In this paper, we study the free-locomotion of a miniature bio-mimetic underwater vehicle inspired by carangiform swimming fish. The vehicle is propelled by a vibrating Ionic Polymer Metal Composite (IPMC) attached to a compliant passive fin. The IPMC vibration is remotely controlled through the vehicle’s onboard electronics that consists of a small-sized battery pack, an H-Bridge circuit, and a wireless module. The planar motion of the vehicle body is described using rigid-body dynamics. Hydrodynamic effects, such as added mass and damping, are included in the model to enable a thorough description of the vehicle’s surge, sway, and yaw motions. The time-varying actions exerted by the vibrating IPMC on the vehicle body, including thrust, lift, and moment, are estimated by combining force and vibration measurements with reduced order modeling based on modal analysis. The model predictions are validated through experimental results on the planar motion of the fish-like robotic swimmer.

Individual Muscle Control Using an Exoskeleton Robot for Muscle Function Testing

A functionality test at the level of individual muscles by investigating the activity of a muscle of interest on various tasks may enable muscle-level force grading. This paper proposes a new method for muscle function tests using an exoskeleton robot for obtaining a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by his/her hand. A computational algorithm systematically computes control commands to a wearable robot with actuators (an exoskeleton robot, or a power-assisting device) so that a desired muscle activation pattern for target muscle forces is induced. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscular function tests for target muscles by arbitrarily inducing muscle activation patterns. Simulation results justify the use of an exoskeleton robot for muscle function testing in terms of the variety of muscle activity data.

Air-Fuel Ratio Control of Spark Ignition Engines With TWC Using LPV Techniques

Precise control of the air-fuel ratio in a spark ignition (SI) engine is important to minimize emissions. The emission reduction strongly depends on the performance of the air-fuel ratio controller for the SI engine in conjunction with the Three Way Catalytic (TWC) converter. The TWC converter acts as a buffer to any variations occurring in the air-fuel ratio. It stores oxygen during a lean operation and releases the stored oxygen during a rich transient phase. The stored oxygen must be maintained close to the current storage capacity to yield maximum benefits from the TWC converter. Traditionally this is achieved using a simple PI control or a gain-scheduled PI control to address the variability in the operating conditions of the engine. This, however, does not guarantee closed-loop system stability and/or performance. In this work a model-based linear parameter varying (LPV) approach is used to design an H∞ controller. The design goal is to minimize the effect of disturbances on the air-fuel ratio and hence the relative storage level of oxygen in the TWC, over a defined operating range for the SI engine. The design method formulated in terms of Linear Matrix Inequalities (LMIs) leads to a convex optimization problem which can be efficiently solved using existing interior-point optimization algorithms. Simulations performed validate the proposed control design methodology.

A Method for Distributed Optimization for Task Allocation

Allocation of a large number of resources to tasks in a complex environment is often a very challenging problem. This is primarily due to the fact that a large number of resources to be allocated results into an optimization problem that involves a large number of decision variables. Most of the optimization algorithms suffer from this issue of non-scalability. Further, the uncertainties and dynamic nature of environment make the optimization problem quite challenging. One of the techniques to overcome the issue of scalability that have been considered recently is to carry out the optimization in a distributed or decentralized manner. Such techniques make use of local information to carry out global optimization. However, such techniques tend to get stuck in local minima. Further, the connectivity graph that governs the sharing of information plays a role in the performance of algorithms in terms of time taken to obtain the solution, and quality of the solution with respect to the global solution. In this paper, we propose a distributed greedy algorithm inspired by market based concepts to optimize a cost function. This paper studies the effectiveness of the proposed distributed algorithm in obtaining global solutions and the phase transition phenomenon with regard to the connectivity metrics of the graph that underlies the network of information exchange. A case study involving resource allocation in wildland firefighting is provided to demonstrate our algorithm.

Determining Maximum Sample Period Under Performance Constraints for Sampled-Data Control Systems

For sampled-data control systems, where a continuous-time plant is under digital control, one of the most important design parameter is the sample rate/period. Higher sample rate typically is associated with the need of high performance components and processors that results in higher system cost. In this paper, we propose an approach to determine the slowest sample rate for a sampled-data control system that will achieve the desired performance and robustness specifications. An optimization problem can be formulated using lifting technique to parameterize sample period for a sampled-data control system. The utility of the proposed approach is numerically verified through the control systems design of the media advance system of an inkjet printer.