Estimation of Ethanol Content in Flex-Fuel Vehicles Using an Exhaust Gas Oxygen Sensor: Model, Tuning and Sensitivity

2008 ◽  
Author(s):  
Kyung-Ho Ahn ◽  
Anna G. Stefanopoulou ◽  
Mrdjan Jankovic
Keyword(s):  
Oxygen Sensor ◽  
Control Process ◽  
Engine Management System ◽  
Exhaust Gas ◽  
Fuel Blend ◽  
Analysis And Design ◽  
Sensor Model ◽  
Ethanol Content ◽  
Model Tuning ◽  
Adaptation Law

Throughout the history of the automobile there have been periods of intense interest in using ethanol as an alternative fuel to petroleum-based gasoline and diesel derivatives. Currently available flexible fuel vehicles (FFVs) can operate on a blend of gasoline and ethanol in any concentration of up to 85% ethanol. In all these FFVs, the engine management system relies on the estimation of the ethanol content in the fuel blend, which typically depends on the estimated changes in stoichiometry through an Exhaust Gas Oxygen (EGO) sensor. Since the output of the EGO sensor is used for the air-to-fuel ratio (AFR) regulation and the ethanol content estimation, several tuning and sensitivity problems arise. In this paper, we develop a simple phenomenological model of the AFR control process and a simple ethanol estimation law which can be representative of the currently practiced system in FFVs. Tuning difficulties and interactions of the two learning loops are then elucidated using classical control techniques. The sensitivity of the ethanol content estimation with respect to sensor and modeling errors is also demonstrated via simulations. The results point to an urgent need for model-based analysis and design of the AFR controller, the ethanol adaptation law and the fault detection issues in FFVs. Tuning and sensitivity issues are demonstrated via simulations and limitations are also discussed.

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

2009 ◽  
Author(s):  
Kyung-ho Ahn ◽  
Anna G. Stefanopoulou ◽  
Mrdjan Jankovic
Keyword(s):  
Oxygen Sensor ◽  
Air Flow ◽  
Intake Manifold ◽  
Absolute Pressure ◽  
Exhaust Gas ◽  
Fuel Ratio ◽  
Ethanol Content ◽  
Sensor Drift ◽  
Flex Fuel ◽  
The Difference

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.

Modeling of upset sensor operation for autonomous unmanned systems applications

2019 ◽  
Vol 7 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Mofetoluwa Fagbemi ◽  
Mario G. Perhinschi ◽  
Ghassan Al-Sinbol
Keyword(s):  
Flight Control ◽  
Simulation Environment ◽  
Control Laws ◽  
Content Type ◽  
Autonomous Flight ◽  
Operational Conditions ◽  
Analysis And Design ◽  
Sensor Model ◽  
Comprehensive Framework ◽  
Sensor Modeling

Purpose The purpose of this paper is to develop and implement a general sensor model under normal and abnormal operational conditions including nine functional categories (FCs) to provide additional tools for the design, testing and evaluation of unmanned aerial systems within the West Virginia University unmanned air systems (UAS) simulation environment. Design/methodology/approach The characteristics under normal and abnormal operation of various types of sensors typically used for UAS control are classified within nine FCs. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each FC. Simple mathematical and logical algorithms are used in this process. Each FC is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. Sensor outputs are integrated with the autonomous flight simulation allowing for evaluation and analysis of control laws. Findings The developed sensor model can provide the desirable levels of realism necessary for assessing UAS behavior and dynamic response under sensor failure conditions, as well as evaluating the performance of autonomous flight control laws. Research limitations/implications Due to its generality and flexibility, the proposed sensor model allows detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It may open new directions for investigating the synergistic interactions between sensors and control systems and lead to improvements in both areas. Practical implications The implementation of the proposed sensor model provides a valuable and flexible simulation tool that can support system design for safety purposes. Specifically, it can address directly the analysis and design of fault tolerant flight control laws for autonomous UASs. The proposed model can be easily customized to be used for different complex dynamic systems. Originality/value In this paper, information on sensor functionality is fused and organized to develop a general and comprehensive framework for sensor modeling at normal and abnormal operational conditions. The implementation of the proposed approach enhances significantly the capability of the UAS simulation environment to address important issues related to the design of control laws with high performance and desirable robustness for safety purposes.

Heated Zirconia Exhaust Gas Oxygen Sensor Having a Sheet-Shaped Sensing Element

1985 ◽  
Author(s):  
Noboru Higuchi ◽  
Shunzo Mase ◽  
Atsushi Iino ◽  
Nobuhide Kato
Keyword(s):  
Oxygen Sensor ◽  
Exhaust Gas ◽  
Sensing Element

Study of Heated Exhaust Gas Oxygen sensor downstream of the catalyst as an emissions calibration tool

2008 ◽  
Author(s):  
Eduardo Mizuho Miyashita ◽  
Manuel Dimitri Ruivo ◽  
Robson Higa
Keyword(s):  
Oxygen Sensor ◽  
Exhaust Gas ◽  
Calibration Tool

Estimation of the Switch Point of an Exhaust Gas Oxygen Sensor in General Exhaust Environments

1995 ◽  
Author(s):  
A. D. Brailsford ◽  
E. M. Logothetis ◽  
M. Yussouff ◽  
J. T. Woestman
Keyword(s):  
Oxygen Sensor ◽  
Exhaust Gas ◽  
Switch Point

ANALYSIS OF A STRAIN GAUGE FOR THE OIL AND GAS INDUSTRY

2020 ◽  
pp. 10-15
Author(s):  
G.A. Ermolaev ◽  
N.V. Gorbunov
Keyword(s):  
Oil And Gas ◽  
New Technologies ◽  
Raw Materials ◽  
Oil And Gas Industry ◽  
Element Analysis ◽  
Gas Industry ◽  
Analysis And Design ◽  
Sensor Model ◽  
Specialized Equipment ◽  
Tension Sensor

Hydrocarbon raw materials are the cornerstone of modern civilization. Evaluating the resources of existing fields is the most important condition for making a decision on the feasibility of production using new technologies. We discuss the results of analysis and design of a rope tension sensor model for delivering specialized equipment to wells to determine the prospects of a well. The calculations were performed using the universal finite element analysis software package ANSYS.

Towards ECU-Executable Control-Oriented Models of a Three-Way Catalytic Converter

2015 ◽  
Author(s):  
Mario Santillo ◽  
Steve Magner ◽  
Mike Uhrich ◽  
Mrdjan Jankovic
Keyword(s):  
Catalytic Converter ◽  
Control Unit ◽  
Oxygen Storage ◽  
Exhaust Gas ◽  
Sensor Model ◽  
Vehicle Engine ◽  
Robust Model ◽  
Combined Models ◽  
Automated Optimization ◽  
Three Way Catalyst

The nonlinear dynamics of an automotive three-way catalyst (TWC) present a challenge to developing simple control-oriented models that are both useful for control and/or diagnostics and real-time executable within a vehicle engine-control unit (ECU). As such, we begin by developing a first-principles control-oriented TWC model and then proceed to apply simplifications. The TWC models are spatially discretized along the catalyst length to better understand and exploit the oxygen-storage dynamics. The TWC models also include the oxidation reaction of ceria by H2O, which is considered important since it represents the production of H2 within the catalyst. We present automated optimization routines to calibrate the TWC model along with a heated exhaust-gas oxygen (HEGO) sensor model using measured vehicle and emissions data. Finally, we demonstrate the combined models’ ability to accurately reproduce the measured HEGO voltage using engine feedgas constituent inputs, which is necessary for designing a robust model-based feedback controller.

Analysis of abnormal output of zirconia oxygen sensor in exhaust gas at low excess air ratio

1993 ◽  
Vol 14 (1-3) ◽  
pp. 501-503 ◽  
Author(s):  
Tatsuo Suemasu ◽  
Yohichi Kurumiya ◽  
Kohsei Ishibashi
Keyword(s):  
Oxygen Sensor ◽  
Exhaust Gas ◽  
Excess Air

Steady-State Biodiesel Blend Estimation via a Wideband Oxygen Sensor

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
David B. Snyder ◽  
Gayatri H. Adi ◽  
Michael P. Bunce ◽  
Christopher A. Satkoski ◽  
Gregory M. Shaver
Keyword(s):  
Steady State ◽  
Diesel Engine ◽  
Oxygen Sensor ◽  
Fuel Blend ◽  
Engine Operation ◽  
Estimation Strategy ◽  
Fuel Blends ◽  
Diesel Vehicles ◽  
Reduce Carbon Dioxide ◽  
Carbon Dioxide Co2

A substantial opportunity exists to reduce carbon dioxide (CO2) emissions, as well as dependence on foreign oil, by developing strategies to cleanly and efficiently use biodiesel, a renewable domestically available alternative diesel fuel. However, biodiesel utilization presents several challenges, including decreased fuel energy density and increased emissions of smog-generating nitrogen oxides (NOx). These negative aspects can likely be mitigated via closed-loop combustion control provided the properties of the fuel blend can be estimated accurately, on-vehicle, in real-time. To this end, this paper presents a method to practically estimate the biodiesel content of fuel being used in a diesel engine during steady-state operation. The simple generalizable physically motivated estimation strategy presented utilizes information from a wideband oxygen sensor in the engine’s exhaust stream, coupled with knowledge of the air-fuel ratio, to estimate the biodiesel content of the fuel. Experimental validation was performed on a 2007 Cummins 6.7 l ISB series engine. Four fuel blends (0%, 20%, 50%, and 100% biodiesel) were tested at a wide variety of torque-speed conditions. The estimation strategy correctly estimated the biodiesel content of the four fuel blends to within 4.2% of the true biodiesel content. Blends of 0%, 20%, 50%, and 100% were estimated to be 2.5%, 17.1%, 54.2%, and 96.8%, respectively. The results indicate that the estimation strategy presented is capable of accurately estimating the biodiesel content in a diesel engine during steady-state engine operation. This method offers a practical alternative to in-the-fuel type sensors because wideband oxygen sensors are already in widespread production and are in place on some modern diesel vehicles today.

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