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An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers
Abstract
An effective Air-to-Fuel Ratio (AFR) control is paramount to ensure a good combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of binary sparse measurements of the exhaust gas Oxygen. The latter are provided by a HEGO (Heated Exhaust Gas Oxygen) sensor installed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays common in commercial cars. The HEGO sensor outputs two voltage values only, corresponding respectively to low or high concentration of the residual Oxygen in the exhaust gas (on/off behavior). In view of this, it can be classified as a binary sensor generating irregular and sparse measurements in that the useful information is only present at the instants of the on/off and off/on transitions. An estimation scheme based on the use of a recursive least-squares algorithm has been designed by resorting to the theory of linear hybrid observers with quantized inputs. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is employed in a PI control-loop designed to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations based on both synthetic and real data.
... It was discovered that the system was able to control the AFR with inaccurate signals or data but it has not been applied to real conduction. Another different study tried to add a variable turbo charge to increase the volumetric efficiency of the engine and changes were observed in the AFR because the turbo charge added forced air into the engine based on the power generated by the exhaust gas pressure [6]. However, the research has not yet integrated an intelligent control system to modify the AFR. ...
... Installation of AFR sensor (1), throttle valve speed sensor(2), braking speed sensor(3), steering speed sensor (4), driving operation(5), and engine speed sensor(6). ...
Energy-efficient engines were introduced due to limited amount of global energy and the need for engine power to carry vehicle loads. It was discovered that the power factor of these engines was essential in developing automotive technology with subsequent significant effect on driving comfort. Moreover, it was possible to control the power and energy savings of vehicle engines by adjusting the Air to Fuel Ratio (AFR). Therefore, this study focused on achieving AFR values in the stoichiometric range of 14.7 in order to produce good emissions. The technology applied was observed to have some drawbacks, specifically in fulfilling engine power when the vehicle operates with a large load. This led to the development of a new method by designing an AFR control system with due consideration for driving behavior using an Artificial Neural Network (ANN). The aim was to overcome the problem of meeting engine power and ensuring better efficiency. The driving behavior was classified into through categories including the sporty, standard, and eco schemes. The eco scheme was the smooth behavior of a driver during the movement of the vehicle in a busy urban area, the sporty scheme was the responsive driving behavior when the vehicle operates on the highway at speeds above 80 km/h, and the standard scheme was the behavior between the eco and sporty schemes. Furthermore, the driving behavior in a sporty scheme required the addition of fuel to increase engine power while eco-scheme focused on reducing fuel to increase fuel economy. The findings showed that control system designed was able to improve driving comfort in terms of fuel economy during the eco scheme with an average AFR value of 15.68. The system further reduced the value to 13.66 during the sporty scheme. Furthermore, the AFR under stoichiometry was discovered to have produced the maximum engine power. The system was expected to be incorporated into electric, gas-fired and fuel cell vehicles in the future. ABSTRAK: Faktor kuasa enjin dan enjin cekap tenaga adalah penting dalam membangunkan teknologi automotif. Mesin penjimat tenaga diperlukan kerana jumlah tenaga global yang terhad. Manakala kuasa enjin digunakan bagi membawa muatan kenderaan. Kedua-dua faktor ini sangat mempengaruhi keselesaan pemanduan. Penjimatan kuasa dan tenaga dalam enjin kenderaan boleh dipenuhi dengan mengawal Nisbah Angin kepada Minyak (AFR). Tumpuan kajian semasa adalah berorientasikan ke arah mencapai nilai AFR dalam julat stoikiometri (14.7) atas sebab ingin mencapai pelepasan terbaik. Namun begitu, teknologi ini mempunyai kelemahan terutama dalam memenuhi kuasa enjin apabila kenderaan beroperasi dengan muatan besar. Oleh itu, kajian ini adalah berkaitan kaedah baharu bagi mengatasi masalah memenuhi kuasa enjin dan mencapai enjin cekap tenaga dengan mereka bentuk sistem kawalan AFR yang mempertimbangkan tingkah laku pemanduan menggunakan Rangkaian Neural Buatan (ANN). Tingkah laku pemanduan direka bentuk kepada tiga skim: sporty, standard dan eko. Skim eko adalah kelancaran tingkah laku pemandu apabila kenderaan bergerak di kawasan bandar yang sibuk. Skim sporty ialah tingkah laku pemanduan responsif apabila kenderaan beroperasi di lebuh raya pada kelajuan melebihi 80 km/j, dan skema standard ialah tingkah laku antara skim eko dan sporty. Tingkah laku pemanduan dalam skema sporty memerlukan penambahan bahan api bagi meningkatkan kuasa enjin. Sementara itu, tingkah laku pemanduan dalam skim eko memerlukan pengurangan bahan api bagi meningkatkan penjimatan bahan api. Hasil kajian menyatakan sistem kawalan yang direka mampu meningkatkan keselesaan pemanduan dari segi penjimatan bahan api apabila tingkah laku pemandu memasuki skim eko. AFR dicapai pada nilai purata 15.68. Apabila tingkah laku pemandu memasuki skim pemanduan sporty, sistem kawalan boleh mengurangkan AFR dengan nilai purata 13.66. AFR di bawah stoikiometri menghasilkan kuasa enjin maksimum. Pada masa hadapan, sistem ini berpotensi untuk dibangunkan pada kenderaan elektrik, menggunakan gas dan sel bahan api.
... The simulation results demonstrated the effectiveness and robustness of the proposed control scheme, outperforming the PI controller with Smith predictor under different operating conditions. It is feasible to extend the similar works that is mentioned above [15][16][17][18][19][20][21][22][23][24][25]. ...
Controlling the air-fuel ratio system (AFR) in lean combustion spark-ignition engines is crucial for mitigating emissions and addressing climate change. In this regard, this study proposes an enhanced version of the Aquila optimizer (ImpAO) with a modified elite opposition-based learning technique to optimize the feedforward (FF) mechanism and proportional-integral (PI) controller parameters for AFR control. Simulation results demonstrate ImpAO’s outstanding performance compared to state-of-the-art algorithms. It achieves a minimum cost function value of 0.6759, exhibiting robustness and stability with an average ± standard deviation range of 0.6823±0.0047. The Wilcoxon signed-rank test confirms highly significant differences (p<0.001) between ImpAO and other algorithms. ImpAO also outperforms competitors in terms of elapsed time, with an average of 43.6072 s per run. Transient response analysis reveals that ImpAO achieves a lower rise time of 1.1845 s, settling time of 3.0188 s, overshoot of 0.1679%, and peak time of 4.0371 s compared to alternative algorithms. The algorithm consistently achieves lower error-based cost function values, indicating more accurate control. ImpAO demonstrates superior capabilities in tracking the desired input signal compared to other algorithms. Comparative assessment with recent metaheuristic algorithms further confirms ImpAO’s superior performance in terms of transient response metrics and error-based cost functions. In summary, the simulation results provide strong evidence of the exceptional performance and effectiveness of the proposed ImpAO algorithm. It establishes ImpAO as a reliable and superior solution for optimizing the FF mechanism-supported PI controller for the AFR system, surpassing state-of-the-art algorithms and recent metaheuristic optimizers.
... The study presents a novel and highly reliable solution for the air-to-fuel ratio control in internal combustion engines to prevent engine shutdown and production loss for greater profits. Gianfranco Gagliardi et al. [30] proposed a hybrid observer scheme which employed in a PI control loop designed to maintain the air-to-fuel ratio close to a required value. The effectiveness of the proposed scheme is demonstrated by several numerical simulations based on both synthetic and real data. ...
This paper presents the design and simulation of air-fuel percentage sensors in drone engine control using Matlab. The applications of sensor engineering system have been pioneer in technology development and advancement of automated machine as complex systems. The integration of drone fuel sensor system is the major series components such as injector, pumps and switches. The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring. The sensor system is improved and virtualized in Simulink block set by varying the parameters with high range to observe the fuel utilization curves and extract the validated results. The obtained results show that the possibility of engine operation in critical conditions such as takeoff, landing, sharp maneuver and performance is applicable to turn off the system in case of break down in the sensor to ensure the safety of drone engine. HIGHLIGHTS The drone engine fuel rate sensor is designed and examined to determine the air-to-fuel ratio The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring The obtained results show that the possibility of using engine with different failure mode and fault considerations The represented control structure is simple, efficient and provides the required air-to-fuel ratio
Driver behavior is a variable that significantly influences fuel use, which is a very concerning issue due to the high cost of fossil fuels caused by the limited amount of energy in the market. Therefore, several breakthroughs have been conducted to realize vehicles with high fuel efficiency. This is in addition to the continuous study of electric, hybrid, gas, and fuel cell vehicles, as well as the development of intelligent control systems. Research on driver behavior has been carried out with several variables, however, none have been conducted on this factor related to fuel consumption. This research aims to review the development of driver behavior as the supporting variable in vehicles. Data were collected from dozens of scientific articles stored in search engines, such as Science Direct, Scopus, Springer link, and ProQuest. The articles found were then filtered based on the closeness with the themes discussed, hence only 13 were reviewed and grouped into five research theme areas. These include car, safety systems, vehicle and emission control, as well graphic display themes. The results provided an overview of the potential development of driver behavior in the future.
This paper is concerned with the optimal identification problem of dynamical systems in which only quantized output observations are available under the assumption of fixed thresholds and bounded persistent excitations. Based on a time-varying projection, a weighted Quasi-Newton type projection (WQNP) algorithm is proposed. With some mild conditions on the weight coefficients, the algorithm is proved to be mean square and almost surely convergent, and the convergence rate can be the reciprocal of the number of observations, which is the same order as the optimal estimate under accurate measurements. Furthermore, inspired by the structure of the Cramer-Rao lower bound, an information-based identification (IBID) algorithm is constructed with adaptive design about weight coefficients of the WQNP algorithm, where the weight coefficients are related to the parameter estimates which leads to the essential difficulty of algorithm analysis. Beyond the convergence properties, this paper demonstrates that the IBID algorithm tends asymptotically to the Cramer-Rao lower bound, and hence is asymptotically efficient. Numerical examples are simulated to show the effectiveness of the information-based identification algorithm.
Estimates and prospects of the catalytic technologies applied on-board for neutralization of automotive exhaust gases are reported. In the next decade, the expected aggregate production of motor vehicles will exceeds 1 billion units; 75 % of them will be equipped with internal combustion engines (ICE), which should have a system for neutralization of exhaust gases. The development of catalytic technologies for neutralization of automotive exhaust gases is mutually stimulated by stiffening the environmental protection standards and improving the ICE. For example, the European standards have moved from Euro-1 to Euro-6d. The introduction of Euro-7 standards in Europe and their analogs in some countries is planned for year 2025. The paper considers also various concepts of the systems for neutralization of exhaust gases intended for gasoline and diesel engines that comply with Euro-7 standards.
In this manuscript, (8YSZ)1-x(CeO2)x (0 ≤ x ≤ 0.6), belonging to the cubic fluorite structure, was prepared by a co-precipitation method. We highlighted the effect of a dense diffusion barrier conductivity on the sensing performance by choosing the lowest conductivity of (8YSZ)0.4(CeO2)0.6 as a dense diffusion barrier compared with the highest conductivity of (8YSZ)0.9(CeO2)0.1 published. A sensor was fabricated using bonding Pt paste. Limiting current (IL) platforms were obtained at different oxygen concentrations x(O2) in a temperature range of 720–900 °C. The oxygen sensor had a smaller oxygen measurement range of 0–10% at 850 °C compared to (8YSZ)0.9(CeO2)0.1 as a diffusion layer. These results showed that the higher oxide ion conductivity of the diffusion layer can achieve a wider oxygen measurement range, which can guide the search for new mixed conductor with higher oxide ion conductivity. The sensing performance of the oxygen sensor was not influenced by p(H2O) and x(CO2), which showed the sensor had a strong selectivity towards oxygen. Furthermore, the limiting current of the sensor remained stable during 144 h of detection.
Xylene is a toxic carcinogen, which may irritate eyes and respiratory tract, or damage the central nervous system. However, the traditional semiconductor gas sensor can only detect the ppm level of xylene gas, which cannot meet the building air quality monitoring requirements. Therefore, the development of low concentration gas detection of xylene gas sensor is necessary. In this article, a one-step hydrothermal method for the preparation of reduced graphene oxide (rGO)/Co
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nanocomposite was reported. Then, rGO/Co
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nanocomposites were characterized by various characterization methods. It was found that the structure of cobalt tetroxide (Co
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) nanoparticle-coated rGO nanosheets was successfully synthesized. Specific internal morphology could be seen by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Then the gas sensor made of rGO/Co
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nanocomposite material was tested for its gas sensitivity performance. A series of properties such as repeatability and stability of the material were tested. According to the test results, the sensitivity of 1rGO/Co
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(3.35 wt%) to xylene gas was better than that of 2rGO/Co
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(5.54 wt%) and pure Co
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, and the optimal working temperature of rGO/Co
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to 50 ppm xylene was 175 °C, which was 50 °C lower than that of pure Co
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. In addition, the rGO/Co
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gas sensor could also detect xylene gas with a minimum concentration of 1 ppb. Finally, the gas sensing mechanism of the gas sensor for xylene gas was analyzed.
The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynamical modelling of the AFR path. The generalized predictive control method is proposed to solve the problems of inherent nonlinearities, time delays, parameter variations, and uncertainties in the AFR closed loop. The simulation analysis is investigated for the effectiveness of noise suppression, online prediction, and self-correction on the SI engine system. Moreover, the experimental verification shows an acceptable performance of the designed controller and the potential usage of the generalized predictive control in AFR regulation application.
In order to maintain the in-cylinder air-fuel ratio (AFR) at a prescribed set point, the stoichiometric value, this paper presents an adaptive regulator of AFR control based on Lyapunov-design for the spark ignition engines. In the research, the adaptive regulator is based on the mean value model of the engine which accounts for the impingement of the injected fuel on the manifold walls and the evaporation process. Adaptive update laws are derived for estimating the uncertain parameters related to inaccuracies in air flow into the cylinders and engine torque production and the uncertain fueling parameters of the fuel injection process. The closed-loop fuel controller is realized by utilizing the information of measurable engine speed, manifold pressure and temperature, air mass flow rate through throttle and the exhaust gas oxygen (EGO) sensor at exhaust location, without knowledge of the fuel flow mass. The theoretical proof and analysis show that the closed-loop system possesses the stability and satisfactory AFR value during both steady state and transient operation. Moreover, simulation on an engine test bench demonstrates the capability of the adaptive controller to recover the performance and robustness properties of the control system in the case of uncertainty, disturbance and the intake-to-power delay and the measurement delay of the oxygen sensor.
A nonlinear observer, based on an extended Kalman filter, is
presented for the estimation of the nonlinear fuel film dynamics into
the intake manifold of a spark ignition engine. In order to meet the
stringent limits imposed on automotive exhaust emissions, electronic
engine control systems have to constrain air-fuel ratio in a narrow band
around the stoichiometric value, since even a small excursion results in
a large degradation of catalyst conversion efficiency. A two states
dynamic model is used to describe liquid and vapour fuel mass flows into
the intake manifold, accounting for both the partial impingement of the
injected liquid fuel on the manifold walls and the evaporation process.
The application of an extended Kalman filter allows to deduce a minimum
error estimate of both model states (liquid and vapour fuel mass) and
parameters (evaporation time constant and injected liquid fraction
impinging the wall) utilizing the knowledge of system dynamics and the
UEGO sensor measurement at exhaust location. The observer has been
tested on a set of 35 air-fuel ratio dynamic transients showing a good
prediction level with respect to engine fuel flow and air-fuel ratio.
Moreover the trend of estimated model parameters vs. operating
conditions is consistent with the physical phenomena taking place in the
region interested by the fuel injection process
In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.
State observability and observer designs are investi- gated for linear-time-invariant systems in continuous time when the outputs are measured only at a set of irregular sampling time sequences. The problem is primarily motivated by systems with limited sensor information in which sensor switching generates ir- regular sampling sequences. State observability may be lost and the traditional observers may fail in general, even if the system has a full-rank observability matrix. It demonstrates that if the orig- inal system is observable, the irregularly sampled system will be observable if the sampling density is higher than some critical fre- quency, independent of the actual time sequences. This result ex- tendsShannon'ssamplingtheoremforsignalreconstructionunder periodic sampling to system observability under arbitrary sam- pling sequences. State observers and recursive algorithms are de- veloped whose convergence properties are derived under poten- tially dependent measurement noises. Persistent excitation condi- tions are validated by designing sampling time sequences. By gen- erating suitable switching time sequences, the designed state ob- servers are shown to be convergent in mean square, with prob- ability one, and with exponential convergence rates. Schemes for generating desired sampling sequences are summarized.
In earlier work it has been shown that a nonlinear observer based
on the use of the manifold pressure state equation and a nonlinear fuel
film compensator can maintain accurate A/F ratio control during both
steady state and transient operation. This observer may be called a
manifold absolute pressure (MAP) observer because it is fundamentally
dependent on the use of the manifold pressure state equation and a MAP
sensor. It is in reality a constant gain extended Kalman filter. While
this observer shows promise for some applications, it obviously cannot
immediately be used with air mass flow sensors other than a MAP sensor.
In this paper it is shown that it is possible to construct a family of
alternative nonlinear observers which “naturally” allow the
use of any given air mass flow related sensor or a combination of them
for A/F ratio control. This new family of observers provides the SI
engine control system designer with a variety of robust control systems
which can easily be made redundant in order to satisfy newer engine
emissions and diagnosis requirements and legislation
Accurate control of the air-fuel ratio in a spark-ignition engine
is critical to satisfying future federal and California emissions
regulations. The goal of this research is to explore the use of adaptive
control as a means of precisely controlling the air-fuel ratio. A
control-oriented, physics-based engine model, in which the sampling rate
is based on crank-angle instead of time, has been utilized to construct
a feedforward/feedback control scheme to regulate air-fuel ratio. The
derived control law, however, requires the values of time constants,
delay times, and other model parameters that must be experimentally
determined. Since these parameters can possibly change over time, a
method for estimating them online is preferred. A nonlinear least
squares identification technique is used to accurately determine the
model parameters using normal engine operating data. These parameter
values are then employed in an estimator based controller to demonstrate
cycle-to-cycle air-fuel ratio regulation on a single-cylinder laboratory
engine (CFR) during various throttle transients
This paper devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with non-affine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel faulttolerant control strategy is designed so that the closed-loop system is semi-globally practically finite-time stable, and the tracking error converges to a small residual set in a finite-time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.
This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.
Abstract: The Air-To-Fuel ratio (AFR) is a crucial parameter to ensure good quality combustion and high catalyst efficiency. This work addresses the problem of determining continuous- time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of only binary (on/off) sparse measurements of the exhaust gas Oxygen determined by using a HEGO (Heated Exhaust Gas Oxygen) sensor placed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor. In this context, the HEGO sensor, providing at its output terminal only two voltage values according if the exhaust gas Oxygen concentration overcomes or not a given threshold, can be considered a binary sensor with irregular and sparse measurements in that the useful information in only present at the instants of the on/off and off/on transitions. The theory of linear hybrid observers with quantized outputs is used to single out an estimation scheme based on a recursive least-squares algorithm. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is then used in a PI control-loop finalized to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations.
This paper illustrates the derivation of a linear parameter varying (LPV) model approximation of a turbocharged Spark-Ignition (SI) automotive engine and its usage in designing a model-based fault detection and isolation (FDI) scheme. The LPV approximation is derived from a detailed nonlinear mathematical model of the engine on the basis of the well known Jacobian approach. The resulting LPV representation is then exploited for synthesizing a bank of LPV-FDI H_inf/H_minus Luenberger observers. Each observer is in charge of detecting a particular class of fault and is designed for having low sensitivity to all other exogenous inputs so as to allow an effective fault isolation. The adopted FDI scheme is gain-scheduled and exploits a set of engine variables, assumed to be measurable on-line, as a scheduling parameters. The goodness of the LPV approximation of the engine model and the effectiveness of the LPV-FDI architecture are demonstrated by several numerical simulations.
An adaptive estimator, based on an extended Kalman filter, is presented for the non linear fuel film dynamics in the intake port of a spark ignition engine. In order to meet the stringent limits imposed on automotive exhaust emissions, electronic engine control systems have to constrain Air-Fuel ratio in a narrow band around the stoichiometric value, since even a small excursion results in a large reduction of catalyst conversion efficiency.
This paper presents an alternative control to maintain the air-fuel-ratio (AFR) of port-injected spark ignition (SI) engines at certain value, i.e. stoichiometric value, to improve the fuel economy. We first reformulate the AFR regulation problem as a tracking control for the injected fuel mass flow rate, which can simplify the control synthesis when the fuel film dynamics are taken into account. The unknown engine parameters and dynamics can be lumped as an unknown signal, and then compensated by incorporating the unknown input observer into the control design. Only the measurable air mass flow rate through throttle, manifold pressure and temperature, and the universal exhaust gas oxygen (UEGO) sensor are utilized. Simulations based on a mean-value engine model (MVEM) illustrate that the proposed control can achieve satisfactory transient and steady-state performance with strong robustness when the engine is operated in varying speed conditions.
It is very important for transient air-fuel ratio control to obtain the fuel film dynamic parameters precisely. The fuel film identification equation based on the inlet fuel film dynamic model is built under the consideration of random errors. In order to eliminate the effect of random errors on the fuel film parameter estimation, instrumental variable (IV) method is put forward. At the same time, considering that each set of measurement data have different confidence coefficient in terms of parameters estimation, the weighted term is introduced to the instrumental variable equation. An engine model is set up using EN-DYNA. Parameter identification applying the proposed algorithm, least squares and Markov recursive least squares are conducted respectively at 1500–3000r/min and the throttle opening from 10%–80%. The identification results are compared and analyzed with the calibration value. The results show that maximum deviation of X and τ applying the weighted recursive instrumental variable method are 1.58% and 4.5% respectively. However, the maximum deviation of X and τ are 2.37% and 6.18% in Markov recursive least squares method meanwhile the value are 4.08% and 9.16% in least squres method.
Engine management systems (EMS) has become an essential component of a spark ignition (SI) engine in order to achieve high performance; low fuel consumption and low exhaust emissions. An engine management system (EMS) is a mixed-signal embedded system interacting with the engine through number of sensors and actuators. In addition, it includes an engine control algorithm in the control unit. The control strategies in EMS are intended for air-to-fuel ratio control, ignition control, electronic throttle control, idle speed control, etc. Hence, the control system architecture of an EMS consists of many sub-control modules in its structural design to provide an effective output from the engine. Superior output from the engine is attained by the effective design and implementation of the control system in EMS. The design of an engine control system is a very challenging task because of the complexity of the functions involved. This paper consolidates an overview of the vital developments within the SI engine control system strategies and reviews about some of the basic control modules in the engine management system.
Higher fuel economy and lower exhaust emissions for spark-ignition engines depend significantly on precise air–fuel ratio (AFR) control. However, the presence of large time-varying delay due to the additional modules integrated with the catalyst in the lean-burn engines is the primary limiting factor in the control of AFR. In this paper, the engine dynamics are rendered into a nonminimum phase system using Padé approximation. A novel systematic approach is presented to design a parameter-varying dynamic sliding manifold to compensate for the instability of the internal dynamics while achieving desired output tracking performance. A second-order sliding mode strategy is developed to control the AFR to remove the effects of time-varying delay, canister purge disturbance, and measurement noise. The chattering-free response of the proposed controller is compared with conventional dynamic sliding mode control. The results of applying the proposed method to the experimental data demonstrate improved closed-loop system responses for various operating conditions.
The problem of air–fuel ratio stabilization in spark ignition engines is addressed in this paper. The proposed strategy consists of proper switching among two control laws to improve quality of the closed-loop system. The first control law is based on an a priori off-line identified engine model and ensures robust and reliable stabilization of the system at large, while the second control law is adaptive, it provides on-line adaptive adjustment to the current fluctuations and improves accuracy of the closed-loop system. The supervisor realizes a switching rule between these control laws providing better performance of regulation. Results of implementation on two vehicles are reported and discussed.
Modern gasoline internal combustion engines use a variety of technologies to enhance the efficiency of fresh air induction. These technologies, which include variable valve timing and variable intake geometry systems, also make it more difficult to predict the mass of fresh air that is trapped during the induction stroke of the engine because they not only affect the residual gas fraction of the trapped air charge, but also the wave dynamics of the system. As the number of controllable actuators increases, this estimation problem becomes even more difficult. As these technologies continue to develop, the importance of robustness in air-to-fuel ratio control continues to grow. This paper presents an air-to-fuel ratio control algorithm based on a switching frequency regulator that has favorable robust stability properties in the presence of both input and model errors. Instead of modeling the air path system with a simplified model, this control architecture considers the air estimate as a control input. As a result, air estimation errors behave like input errors, not modeling errors. By using the rich-to-lean and lean-to-rich air-to-fuel ratio switching frequencies of the pre-catalyst exhaust gas oxygen sensor as the primary feedback signal, the control laws are completely independent of the parameters of the plant model. The performance of this controller is demonstrated both with a robust stability analysis and through a vehicle-based experimental validation.
In this paper, we propose a fault detection and isolation filter design method for internal combustion spark ignition engines. Starting from a detailed nonlinear mean-value mathematical description of the engine, a novel linear parameter varying (LPV) model approximation is derived on the basis of a judicious convex interpolation of a family of linearized models. A filter structure consisting of a bank of LPV observers is considered, each of them in charge of detecting a particular class of faults and exhibiting low sensitivity to all other faults and exogenous inputs. The resulting diagnostic filter is parameter-dependent in that a set of measurable engine variables is used online to suitably modify the filter gain so as to better take care of system nonlinearities. The quality of the LPV model approximation of the engine and the diagnostic capabilities of the fault detection and isolation architecture are demonstrated by a series of extensive numerical simulations. Copyright © 2013 John Wiley & Sons, Ltd.
In this paper, a new synthesis method is presented to control air–fuel ratio (AFR) in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The major challenge in the control of AFR is the time-varying delay in the control loop which restricts the application of conventional control techniques. In this paper, the time-varying delay in the system dynamics is first approximated by Padé approximation to render the system dynamics into non-minimum phase characteristics with time-varying parameters. Application of parameter-varying dynamic compensators is invoked to retrieve unstable internal dynamics. The associated error dynamics is then utilized to construct a filtered PID controller combined with a parameter-varying dynamic compensator to track the desired AFR command using the feedback from the universal exhaust gas oxygen sensor. The proposed method achieves desired dynamic properties independent of the matched disturbances. It also accommodates the unmatched perturbations due to the dynamic compensator features. The results of applying the proposed method to experimental numerical data demonstrate the closed-loop system stability and performance against time-varying delay, canister purge disturbances and measurement noise for both port fuel injection engines and lean-burn engines.
Various mathematical models for the air to fuel ratio and control for spark ignition (SI) engines have been proposed to satisfy technical specifications. This paper reveals an improvement of the mean value model (MVEM) and a simple yet effective nonlinear control to enhance the air to fuel regulator. The regulator is designed by using a discrete fuzzy PI algorithm, which provides easy tuning, robustness, and rapid development with a simple architecture. Effects on the dead time, exhausted delay, time-varying air flow and chaotic disturbance are also included. The computer simulation results show satisfactory performance based on standard evaluation criteria. The proposed model has a great potential for practical implementations.
In the paper, a Hendricks Mean Value Engine Model is established by using SIMULINK. At the same time, a fuzzy neural network is designed. The AFR is simulated under transient conditions. The simulation result shows that: With no controller, when throttle degree is changed intensively, the AFR errors are large, With the FNN controller, the AFR errors can be controlled to a narrow range, and the system has shorter adjust-time, smaller overshoot. So, the fuzzy neural network controller has good control performance under gasoline engine transient condition.
The second-generation air-fuel ratio control method has been developed to reduce exhaust gas emissions in accordance with the improvements in catalysts. The control system consists of a feedforward control using a fuel behavior model, a feedback control using an universal exhaust gas oxygen (UEGO) sensor and a feedback control utilizing the heated exhaust gas oxygen (HEGO) sensor. This significantly improves air-fuel ratio tracking performance by feedforward control derived from the models that express the dynamic phenomena and the disturbance attenuation by UEGO feedback controller which compensates for the long dead-time characteristics by the state predictive control. The tracking performance and the disturbance attenuation can be achieved independently by a two-degree-offreedom structure presented in this paper. The exhaust air-fuel ratio downstream of the catalyst precisely converges to stoichiometry, which maximizes the conversion efficiency of the catalyst. Experimental results on actual vehicles show the effectiveness of presented method.
Presented in this paper is a feedforward fueling controller identification methodology for the transient fueling control of spark ignition (SI) engines. The proposed transient feedforward controller is identified and executed in the crank angle domain, and operates in tandem with a steady state fueling controller. The hypothesis is that the feedforward fueling control of SI engines can be separated into steady state and transient phenomena, and that the majority of the nonlinear behavior associated with engine fueling can be captured with nonlinear steady state compensation. The proposed transient controller identification process is built from standard nonparametric identification techniques using spectral density functions where crank angle serves as the independent variable. Two separate system identification problems are solved to identify the air path dynamics and the fuel path dynamics. The transient feedforward controller is then calculated as the ratio of the air path-over-the fuel path dynamics so that the fuel path dynamics match the air path dynamics. Consequently fueling is coordinated with the fresh air charge during transient conditions. It will be shown that a linear transient feedforward-fueling controller operating in tandem with a nonlinear steady state fueling controller can achieve air-fuel ratio (AFR) regulation comparable to a production controller without the extensive controller calibration process. The engine used in this investigation is a 1999 Ford 4.6L V-8 fuel injected engine.
This paper presents the design and experimental test of a fixed-structure LPV controller for the charge control of a spark-ignition engine. A nonlinear model of the plant is transformed into an affine LPV model in the form of an LFT representation. Using a hybrid evolutionary-algebraic synthesis approach that combines LMI techniques based on K-S iteration with evolutionary search, a scheduled PID controller is designed. To reduce conservatism, the technique of quadratic separators is used in the analysis step. To improve tracking behavior, the gain scheduled feedback controller is supported by an LTI feedforward controller. The controller has been implemented on a standard electronic control unit, and experimental results on a test car illustrate that it meets the performance requirements in a wide range of operation.
This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.
Neural networks is very useful in modeling processes for which mathematical modeling is difficult or impossible. In the present
work recurrent neural network (RNN) is used for air-fuel ratio (AFR) estimation in Spark Ignition (SI) Engine. AFR estimation
is difficult due to the nonlinearity and dynamic behavior in SI engines. Additionally, delays in engine dynamics limit the
performance of engine controller. Estimating AFR a few steps in advance can help engine controller to take care of these.
RNN is trained using data from engine simulations in MATLAB/SIMULINK environment. Uncorrelated signals were generated for
training and validation. It has been shown that recurrent neural network can predict engine simulations with reasonably good
accuracy.
Stringent requirements to maintain low emission levels over the vehicle lifetime constrain the combustion Air-Fuel Ratio (AFR) within a narrow band around the stoichiometric value and hence pose a challenging AFR control problem. In order to match the desired AFR level Universal Exhaust Gas Oxygen (UEGO) sensors have been recently employed within control loops involving nonlinear engine dynamics. Sensor measurement uncertainties and inevitable effects of aging of these sensors motivate the use of online tuning algorithms for the controller gains. In this paper we develop an adaptive control structure which consists of an adaptive PI controller and an adaptive Smith predictor for time-delay systems with unknown plant parameters. We implement our control design using a plant model representing simplified engine dynamics with a constant UEGO sensor delay based on the assumption that the overall time constant of the plant is unknown. The performance of the adaptive controller is demonstrated through simulation scenarios where additional benefits of our design approach can be observed.
We consider the problem of state estimation of a discrete time process over a packet-dropping network. Previous work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[ Pk ], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr [ Pk ?? M ], i.e., the probability that Pk is bounded by a given M . We consider two scenarios in the paper. In the first scenario, when the sensor sends its measurement data to the remote estimator via a packet-dropping network, we derive lower and upper bounds on Pr [ Pk ?? M ]. In the second scenario, when the sensor preprocesses the measurement data and sends its local state estimate to the estimator, we show that the previously derived lower and upper bounds are equal to each other, hence we are able to provide a closed form expression for Pr [ Pk ?? M ]. We also recover the results in the literature when using Pr [ Pk ?? M ] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper.
This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramér–Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank.
This paper presents the control of spark ignition (SI) internal combustion (IC) engine fuel-to-air ratio (FAR) using an adaptive control method of time-delay systems. The objective is to maintain the in-cylinder FAR at a prescribed set point, determined primarily by the state of the three-way catalyst (TWC), so that the pollutants in the exhaust are removed with the highest efficiency. The FAR controller must also reject disturbances due to canister vapor purge and inaccuracies in air charge estimation and wall-wetting (WW) compensation. Two adaptive controller designs are considered. The first design is based on feedforward adaptation while the second design is based on both feedback and feedforward adaptation incorporating the recently developed adaptive posicast controller (APC). Both simulation and experimental results are presented demonstrating the performance improvement by employing the APC. Modifications and improvements to the APC structure, which were developed during the course of experimentation to solve specific implementation problems, are also presented.
Automotive companies commonly adopt hardware-in-the-loop simulators to develop new control strategies in order to reduce the effort and the cost of the testing phase. This work aims at presenting a simple intake manifold air dynamics model, suited for HIL simulations, taking into account the nonlinear effects caused by VVA system during all its operating modes, i.e. full lift, early closure, late opening and multi lift. The performances of the proposed model are finally presented through comparisons with experimental data collected by a 1.4 liter Fiat SI engine, showing good reliability and excellent robustness.
An LTI estimation framework is proposed for networked control systmes (NCS), in which local Kalman filter estimates are sent to the remote estimator. Both controlled and uncontrolled data communications are considered. For uncontrolled communication, minimum rate requirements are given for stochastic moment stability, which depend only on the least stable poles. For controlled communication. Sufficient stability conditions are formulated. The framework also makes it possible to improve the trade-off between estimation performance and communication cost.
State estimation of hybrid systems is a significant problem for the design of feedback control and model-based diagnosis algorithms. In this paper, a methodology for state estimation of hybrid systems with discrete sensors based on particle filtering is presented. The quality of the algorithm is evaluated by comparing its performance with Cramer-Rao bounds computed for the discrete-time hybrid filtering problem. The approach is illustrated using simulation results of a tank system example.
Considers the observer design problem for a continuous-time system
when the output is event-based. For the case of linear time invariant
system with an output in finite alphabet the authors develop a hybrid
observer which is represented as a combination of continuous-time
dynamics and nonuniformly sampled discrete-time system. The design idea
uses generalization of the sliding mode concept for discrete-time
systems in the form of dead-beat controllers and observers. It is shown
that even for the linear case the observation problem is not independent
from the control which can be used to regulate the observation process
The aim of this article is to explore a possible approach to the
problem of designing control and diagnostic strategies for future
generations of automotive engines. The work described focuses on the use
of physical models to estimate unmeasured or unmeasurable variables and
parameters, to be used for control and diagnostic purposes. We introduce
the mean value engine model used, present a conceptual strategy for
combined control and diagnosis focusing mainly on the problem of air
fuel ratio control, review basic concepts related to estimation in
nonlinear systems, and propose various forms of the estimators that
serve different objectives. We illustrate estimation problems in the
context of a simplified engine model, assuming both linear and nonlinear
measurement of oxygen concentration in the exhaust. Some simulation and
experimental results related to estimation-based control and diagnosis
are shown
This paper discusses the application of linear observer theory to
engine control with a specific focus on observers based on exhaust
measurements. The motivation for this architecture is that a direct
measurement of the relevant quantity (the exhaust chemistry entering the
catalyst) will typically lead to the most accurate control. A secondary
motivation is the need to reduce costs. If sufficient accuracy can be
obtained with the exhaust sensor providing the primary information, the
cost associated with intake manifold sensors can be reduced. The basic
theory is discussed and experimental results are reported that
illustrate the benefits and superior performance of linear observer
based control
State estimation of hybrid systems is a significant problem for the design of feedback control and model-based diagnosis algorithms. In this paper, a methodology for state estimation of hybrid systems with discrete sensors based on particle filtering is presented. The quality of the algorithm is evaluated by comparing its performance with Cram er-Rao bounds computed for the discrete-time hybrid filtering problem. The approach is illustrated using simulation results of a tank system example.
Engine management systems
- Lathi