Methodology to Evaluate the Fuel Economy of a Multimode Combustion Engine With Three-Way Catalytic Converter

Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultra-low levels of engine-out NOx emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper a methodology is introduced, investigating the fuel economy of such a multimode combustion concept in combination with a three-way catalytic converter (TWC). The TWC needs to exhibit unoccupied oxygen storage sites in order to show acceptable performance. But the lean exhaust gas during HCCI operation fills the oxygen storage and leads to a drop in NOx conversion efficiency. Eventually the levels of NOx become unacceptable and a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage. The resulting lean-rich cycling leads to a penalty in fuel economy. In order to evaluate the impact of those penalties on fuel economy, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI and back. Fuel and emissions maps acquired in steady state experiments are used. Two depletion strategies are compared in terms of their influence on drive cycle fuel economy and NOx emissions.

Numerical Analysis of the Tapping Atomic Force Microscopy on a Viscoelastic Sample

Mechanical characterization of thin samples is now routine due to the prominence of the Atomic Force Microscope. Advances in amplitude modulation techniques have allowed for accurate measurement of a sample’s elastic properties by interpreting the changes in the vibration of a cantilevered beam in intermittent contact. However, the nonlinearities associated with contact complicate attempts to find an accurate time-history for the beam. Furthermore, the inclusion of viscous effects, common to soft samples, puts an explicit solution even farther from reach. A numerical method is proposed that analyzes the time-history and frequency response of a microcantilever beam with a viscoelastic end-condition. The mathematics can be simplified by incorporating the viscoelastic end-condition into the equation of motion directly by modeling it as a distributed load. A forcing function can then be derived from the Standard Linear Solid model of viscoelasticity and implemented in the non-conservative work term of Hamilton’s principle. The Galerkin method can separate the resulting nonlinear equation of motion into time and space components. Performing a numerical analysis of the time factor equation provide the beam’s response over time. The results demonstrate the distinctive effects of viscoelasticity and periodic contact on the beam’s motion and provide the framework for the determination of viscous properties using dynamic techniques.

Observer Design for State Estimation of UV Curing Processes

This article discusses the challenges of non-intrusive state measurement for the purposes of online monitoring and control of Ultraviolet (UV) curing processes. It then proposes a two-step observer design scheme involving the estimation of distributed temperature from boundary sensing cascaded with nonlinear cure state observers. For the temperature observer, backstepping techniques are applied to derive the observer partial differential equations along with the gain kernels. For subsequent cure state estimation, a nonlinear observer is derived along with analysis of its convergence characteristics. While illustrative simulation results are included for a composite laminate curing application, it is apparent that the approach can also be adopted for other UV processing applications in advanced manufacturing.

Selling Power System Flexibility: Ancillary Service and Real-Time Energy Market Challenges for Storage

Power system operators obtain the flexibility required to reliably balance aggregate generation and load through ancillary service and five-minute energy markets. Market prices are based on the marginal opportunity costs of the generators. This market design works well for generators but inherently fails for storage and demand response, denying these new technologies a fair opportunity to compete and denying the power system access to potentially lower cost reliability resources. Market design or regulatory changes may be required for storage and demand response to be viable ancillary service providers.

A Model-Based Layer-to-Layer Control Algorithm for Ink-Jet 3D Printing

Ink-jet 3D printing is a promising technology for additive manufacturing, with the potential for impacting a wide variety of industries. In traditional ink-jet 3D printing, the part is built up by depositing droplets layer upon layer in an open-loop manner. Droplet and edge dimensions are typically predicted experimentally and are assumed to remain constant through the printing process. However, there is no guarantee of consistent droplet shape and dimensions or the smoothness of the finished parts due to uncertainties in the manufacturing process. To address this issue, we propose a model-based feedback control law for ink-jet 3D printing that uses a height sensor for measuring profile height after each layer for determining the appropriate layer patterns for subsequent layers. Towards this goal, a simple model describing the relationship between profile height change and droplet deposition in the layer building process is first proposed and experimentally identified. Based on this model, a closed-loop layer-to-layer control algorithm is then developed for the ink-jet printing process. Specifically, the proposed algorithm uses a model prediction control algorithm to minimize the difference between the predicted height and the desired height and the predicted surface unevenness after a fixed number of layers. Experimental results show that the algorithm is able to achieve more consistent shapes between layers, reduced edge shrinking of the part, and smoother surface of the top layer.

Functional Electrical Stimulation for Equilibrium-Point Control of Human Ankle Movement: Frequency Domain System Identification of Human Ankle Dynamics

This paper presents a novel method for creating an electrical stimulation pattern to control the equilibrium-point (EP) of human ankle movement. Focusing on the synergetic activation of agonist–antagonist (AA) muscles, the proposed method employs the ES-AA ratio (the ratio of the electrical stimulation levels for AA muscles) and the ES-AA sum (the sum of the electrical stimulation levels for AA muscles), which are based on the AA ratio (the ratio of the electromyography (EMG) voltage levels for AA muscles) and the AA sum (the sum of the EMG voltage levels for AA muscles) used in human movement analysis [1, 2]. The ES-AA ratio is related to the EP of the joint whereas the ES-AA sum is associated with mechanical stiffness of the joint. Using the AA concepts, we estimated the transfer function between the input ES-AA ratio (for the tibialis anterior (TA ) and gastrocnemius (GC)) and the force output of the endpoint in the ankle joint in an isometric environment by investigating the frequency characteristics, and finally found that the ankle-joint system was a second-order system with dead time in terms of the ES-AA ratio and foot force.

Application of Linear Observers to Estimate States of a Roll Simulator

In this research, a variety of Kalman Filters are implemented in an effort to estimate sled speed of a Roll Simulator. An Extended Kalman Filter (EKF) is incorporated to capture the nonlinear dynamics of the sled-platform assembly to estimate sled speed for the entire motion, as a linear Kalman Filter was found to be inadequate. When applied to experimental data, the EKF over-estimates sled speed, which is due to a disturbance force and/or uncertainty in system parameters. In combination with the disturbance observer, the Kalman Filter adequately estimates sled speed for experimental data. For lower speed/payload applications, a Kalman Filter using an accelerometer and measured drum speed is able to accurately track sled speed when a gain scheduling scheme is employed.

Stochastic Estimation of Human Ankle Mechanical Impedance in Lateral/Medial Rotation

This article compares stochastic estimates of human ankle mechanical impedance when ankle muscles were fully relaxed and co-contracting antagonistically. We employed Anklebot, a rehabilitation robot for the ankle to provide torque perturbations. Surface electromyography (EMG) was used to monitor muscle activation levels and these EMG signals were displayed to subjects who attempted to maintain them constant. Time histories of ankle torques and angles in the lateral/medial (LM) directions were recorded. The results also compared with the ankle impedance in inversion-eversion (IE) and dorsiflexion-plantarflexion (DP). Linear time-invariant transfer functions between the measured torques and angles were estimated for the Anklebot alone and when a human subject wore it; the difference between these functions provided an estimate of ankle mechanical impedance. High coherence was observed over a frequency range up to 30 Hz. The main effect of muscle activation was to increase the magnitude of ankle mechanical impedance in all degrees of freedom of ankle.

Energy Management and Control of a Hybrid Electric Vehicle With an Integrated Low Temperature Combustion (LTC) Engine

Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.