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.

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.

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.

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.

Physical Modeling and Control of a Multi-Cylinder HCCI Engine

Homogeneous charge compression ignition (HCCI) is one of the most promising piston-engine concepts for the future, providing significantly improved efficiency and emissions characteristics relative to current technologies. This paper presents a framework for controlling a multi-cylinder HCCI engine with exhaust recompression and direct injection of fuel into the cylinder. A physical model is used to describe the HCCI process, with the model states being closely linked to the thermodynamic state of the cylinder constituents. Separability between the effects of the control inputs on the desired outputs provides an opportunity to develop a simple linear control scheme, where the fuel is used to control the work output and the valve timings are used to control the phasing of combustion. Experimental results show good tracking of both the work output and combustion phasing over a wide operating region. In addition, the controller is able to balance out differences between cylinders, and reduce the cycle-to-cycle variability of combustion.

Integrating Parameter Estimation Solutions From the Time and Time-Scale Domains

A method of parameter estimation was recently introduced that separately estimates each parameter of the dynamic model [1]. In this method, regions coined as parameter signatures, are identified in the time-scale domain wherein the prediction error can be attributed to the error of a single model parameter. Based on these single-parameter associations, individual model parameters can then be estimated for iterative estimation. Relative to nonlinear least squares, the proposed Parameter Signature Isolation Method (PARSIM) has two distinct attributes. One attribute of PARSIM is to leave the estimation of a parameter dormant when a parameter signature cannot be extracted for it. Another attribute is independence from the contour of the prediction error. The first attribute could cause erroneous parameter estimates, when the parameters are not adapted continually. The second attribute, on the other hand, can provide a safeguard against local minima entrapments. These attributes motivate integrating PARSIM with a method, like nonlinear least-squares, that is less prone to dormancy of parameter estimates. The paper demonstrates the merit of the proposed integrated approach in application to a difficult estimation problem.

Tolerant Ethanol Estimation in Flex-Fuel Vehicles During MAF Sensor Drifts

Flexible fuel vehicles (FFVs) can operate on a blend of ethanol and gasoline in any volumetric concentration of up to 85% ethanol (93% in Brazil). Existing FFVs rely on ethanol sensor installed in the vehicle fueling system, or on the ethanol-dependent air-to-fuel ratio (AFR) estimated via an exhaust gas oxygen (EGO) or λ sensor. The EGO-based ethanol detection is desirable from cost and maintenance perspectives but has been shown to be prone to large errors during mass air flow sensor drifts [1, 2]. Ethanol content estimation can be realized by a feedback-based fuel correction of the feedforward-based fuel calculation using an exhaust gas oxygen sensor. When the fuel correction is attributed to the difference in stoichiometric air-to-fuel ratio (AFR) between ethanol and gasoline, it can be used for ethanol estimation. When the fuel correction is attributed to a mass air flow (MAF) sensor error, it can be used for sensor drift estimation and correction. Deciding under which condition to blame (and detect) ethanol and when to switch to sensor correction burdens the calibration of FFV engine controllers. Moreover, erroneous decisions can lead to error accumulation in ethanol estimation and in MAF sensor correction. In this paper, we present a cylinder air flow estimation scheme that accounts for MAF sensor drift or bias using an intake manifold absolute pressure (MAP) sensor. The proposed fusion of the MAF, MAP and λ sensor measurements prevents severe mis-estimation of ethanol content in flex fuel vehicles.