Cold Start Hydrocarbon Emissions Research Articles

Experimental and simulation investigation on the different iron content beta zeolite for controlling the cold-start hydrocarbon emission from a gasoline vehicle

In this work, cold start experiments results showed that the HC catcher coated with 1% iron content is more effective than that with 3% iron content, the peak HC emission is reduced from 0.0029 g to 0.0018 g. To further reveal the reasons for the effects of BEA zeolite molecular sieves with different iron contents on HC adsorption, the measured HC emission components (butene, propene, ethene, acetaldehyde, and acetylene) were used as HC simulation molecules for molecular simulation experiments, the simulation results showed that, for single-component adsorption, the adsorption effect of beta zeolite molecular sieves with high iron content was better; for multi-component adsorption, acetaldehyde and butene maintained higher adsorption amounts, and the adsorption of acetaldehyde increased with the increase of the iron content in the beta zeolite molecular sieves; in the presence of water and CO2, the effect of water is greater than that of carbon dioxide, and its effect increases with the increase of iron content in beta zeolite molecular sieves. The maximum adsorption capacity of water can reach 4.5mmol/zeolite, which seriously affected the adsorption of HC molecules. Properly reducing the iron content of BEA zeolite molecular sieve is useful for enhancing its adsorption effect on the main HC simulated molecules.

Fast Catalyst Light-Off with Dynamic Skip Fire

<div class="section abstract"><div class="htmlview paragraph">Catalytic aftertreatment is commonly used to reduce regulated gas emissions from Internal Combustion (IC) engines. Achieving fast catalyst light-off has always been a challenge for automobile IC engine applications. This paper experimentally studied the thermal management and regulated gas emissions from a Spark Ignition (SI) engine with Dynamic Skip Fire (DSF®) technology during cold start period.</div><div class="htmlview paragraph">The study has found that DSF can increase exhaust gas temperature at the catalyst inlet by up to 100°C, and the exhaust enthalpy by up to 20%. Cold start tailpipe carbon monoxide (CO) and hydrocarbon (HC) emissions can be reduced by 10% to 20% largely due to the increased exhaust gas temperature and enthalpy. Dynamic air pumping can further increase exhaust gas temperature by 30 °C, and can nearly double enthalpy delivered to the catalyst, which reduces cold start HC emissions by more than 50%.</div></div>

Alkaline-modified ZSM-5 zeolite to control hydrocarbon cold-start emission

The implementation of Super-Ultra Low Emission Vehicle (SULEV) standards requires fine exhaust gas purification. Adsorbents capable of strong adsorption of hydrocarbons and holding them up to 250-300 °С were prepared by modification of ZSM-5 (SiO2/Al2O3 = 35) zeolite with Li, K, or Na cations. It was shown that the introduction of Li, Na, and K resulted in enhanced toluene adsorption at high temperatures. The best result was obtained for 2.3%Na-ZSM-5. It was established that an increase in the Li content in ZSM-5 to 5 wt% results in a shift of the high-temperature peak to the high-temperature range. Toluene desorption curves for 5%Li-ZSM-5 and 2.3%Na-ZSM-5 zeolites are identical. Analysis of toluene adsorption isotherms at 20 °С for 2.3%Na-ZSM-5 and 5%Li-ZSM-5 revealed that toluene diffusion inside the channels of modified zeolites is extremely slow, and the overall sorption capacity of zeolites with univalent cations decreases as compared with initial ZSM-5. The 5%LiZSM-5 and 2.3%Na-ZSM-5 zeolites characterized by toluene desorption temperatures about 200–400 °С are very promising to control the cold-start hydrocarbon emissions.

Cold-Start Emission Reduction Potential and Limitations of Commercial Passive Hydrocarbon Adsorbers

Effective control of hydrocarbon (HC) emissions during cold start is critical to meeting more stringent future emission standards, and the passive HC adsorber is one possible technology to improve cold-start HC emission control. HC adsorbers for gasoline applications typically contain zeolite materials for HC storage and a noble metal-based three-way catalyst (TWC) for oxidation of stored HCs, both of which are susceptible to hydrothermal aging. In the present study, state-of-the-art HC adsorbers from two catalyst suppliers were evaluated for their cold-start HC emission reduction potential. After aging at 800 °C, the HC adsorbers tested provided reasonably high conversion efficiencies for aromatics and a moderate level of conversion for olefins but were not able to control paraffins to a significant degree. This resulted in a total HC conversion efficiency of 40–50% over the best HC adsorber for the simulated feed stream used in this study. The overall adsorber performance can be improved somewhat by increasing the noble metal loading or the O2 concentration in the feed stream and was found to be sensitive to adsorber volume (space velocity). After aging at 900 °C, however, none of the HC adsorbers tested was able to convert more than 10% of the HC in the feed.

A Nonlinear Model Predictive Controller With Multiagent Online Optimizer for Automotive Cold-Start Hydrocarbon Emission Reduction

In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challenging control problem that has attracted increasing attention from the automotive research community. The main contribution of this paper lies in the implementation of a model-based predictive control scheme that uses a discrete nonlinear control-oriented model for calculating the control commands. In the first part of the experiments, the developed control-oriented model is validated with some data obtained through experiments. Then, by applying the controller to different cold-starting conditions, the values of exhaust gas temperature $T_{\mathrm{exh}}$ and engine-out HC emissions are controlled to reduce the cumulative tailpipe emissions $\text{HC}_{\mathrm{cum}}$ . To ascertain the veracity of the proposed control scheme, the same problem is solved using Pontryagin's minimum principle. The conducted simulations indicate the applicability of DPSO as an effective solver at the heart of the NMPC. Moreover, the results of model-in-the-loop simulation and hardware-in-the-loop testing demonstrate the acceptable performance of the proposed control scheme for real-time applications, which is based on the NMPC method for the considered problem.

A Receding Horizon Sliding Controller for Automotive Engine Coldstart: Design and Hardware‐in‐the‐Loop Testing With an Echo State Network High‐Fidelity Model

AbstractThe aim of the current study is to probe the potential of receding horizon sliding control (RHSC) technique for reducing the coldstart hydrocarbon (HC) emissions of automotive spark‐ignited (SI) engines. The RHSC approach incorporates the potentials of sliding control (SC) and nonlinear model predictive control (NMPC) to employ the future information of the considered engine to keep the system's trajectories close to a stable manifold. To calculate the control commands, the authors adopt an efficient optimization technique, known as the multivariate quadratic fit sectioning algorithm (MQFSA), and also, define three different objective functions, based on l1, l2, and l∞ norms. To demonstrate the efficacy of RHSC controller, its performance is compared with two other well‐known controllers extracted from the literature, namely NMPC and Pontryagin's minimum principle (PMP)‐based controllers. Through numerical simulations for three distinctive operating conditions, it is demonstrated that the RHSC controller is very effective for reducing the total tailpipe HC emissions over the coldstart period of the considered engine system. Moreover, by conducting a hardware‐in‐the‐loop (HIL) test using an echo state network high‐fidelity model, it is indicated that the computational speed of calculating control commands is fast enough to enable RHSC to be used for real‐time implementations in practice.

CFD model of multi-converter HC adsorber-TWC system

A novel three dimensional model of in-line hydrocarbon adsorber system (with both adsorber and three-way catalyst) is developed by coupling the computational fluid dynamics (CFD) flow and temperature effects with a 1D chemical kinetics solver. The model has been validated against experimental values obtained from vehicle test over light-off. The overall performance of the multi-converter hydrocarbon adsorber system is compared with a single converter with only three-way catalytic (TWC) monolith. It is observed that thermal mass of the adsorber monolith plays a vital role in determining the performance of total converter system. The impact of diverting exhaust flow away from the hydrocarbon absorber is also established using this model. Results indicate that the cold start hydrocarbon emission is not a monotonous function of the valve opening time. The optimum time for which the adsorber should be exposed to the exhaust depends not only on its adsorption capacity but also on the thermal capacity of adsorber monolith.

An optimal learning-based controller derived from Hamiltonian function combined with a cellular searching strategy for automotive coldstart emissions

The main objective of this study is to propose a novel algorithmic framework for the implementation of a nonlinear controller for reducing the amount of tailpipe hydrocarbon emissions in automotive engines over the coldstart period. To this aim, the control problem for a given engine is formulated in the form of the standard Bolza problem, and then, the concepts of Euler–Lagrange equation and Hamiltonian function are taken into account to calculate the optimal states, co-states, and control input signals. An extreme learning machine is also linked to an experimentally validated nonlinear state-space representation of the engine during the coldstart to approximate the values of exhaust gas temperature and engine-out hydrocarbon emissions, which are two key variables for the considered control problem. To solve the resulting system of equations, a cellular variant of the particle swarm optimization technique is implemented and the existing nonlinear system of equations is solved heuristically. In addition, some constraints are exerted on the control signals to guarantee the smooth operation of the engine by applying the calculated controlling commands. Finally, the authenticity of the resulting optimal controller is validated against a classical Pontryagin’s minimum principle-based control system. Generally, the findings demonstrate the effectiveness of the proposed control methodology to reduce coldstart hydrocarbon emissions in automotive engines.

A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction

In the current study, a novel intelligent control algorithm is proposed for the efficient reduction of coldstart hydrocarbon (HC) emissions of an automotive engine. The proposed intelligent controller inherits the computational advantages of two advanced techniques, namely hybrid switching predictive controller and extreme learning machine (ELM). The proposed model-based predictive controller has a switching behavior which enables it to be used for a deliberate control of the considered engine's behavior during the coldstart period. As the hybrid controller requires a hybrid model for processing, a bi-level ELM is used which is composed of two independent ELMs for the calculation of exhaust gas temperature (Texh) and engine-out HCs. A novel online trajectory builder is also introduced to make sure that the control commands are adjusted according to the actual behavior of the engine. Through simulations, the computational potentials of ELM for designing hybrid switching controllers are demonstrated. Furthermore, it is indicated that the proposed hybrid switching controller can afford qualified results for the considered case study. To verify the authenticity of the obtained results, two well-known controllers, namely nonlinear model predictive controller (NMPC) and an optimal controller based on the Pontryagin's minimum principle (PMP), are taken into account. The numerical experiments also host an additional step which evaluates the impact of prediction horizon (HP) on the performance of the controller. Based on all of the mentioned simulations, the efficacy of hybrid switching predictive controller with bi-level ELM (HSPC-BELM) is verified.

Online optimization of automotive engine coldstart hydrocarbon emissions control at idle conditions

In this work, we propose a model-based scheme for the online optimization of coldstart hydrocarbon emission control strategy of an automotive spark-ignited engine during its idle operating conditions. First, the existing model-based control schemes to reduce the coldstart emissions are reviewed and classified. Then, the proposed scheme and the related control-oriented engine model validated by experimental data are introduced. At the heart of the suggested scheme, there is a new optimal control problem formulation for the coldstart that can be solved rapidly using the Pontryagin’s minimum principle. This formulation is based on a redefined objective function with weighted terms for the coldstart key variables, that is, the engine-out hydrocarbon emission and the exhaust gas temperature. The weighting numbers for these dominant factors in the redefined objective function are tuned such that the optimum solution becomes close to the minimum of the cumulative tailpipe hydrocarbon emissions. The use of the redefined objective function reduces significantly the computational efforts, resulting in an order of magnitude faster convergence rate than solving the original coldstart hydrocarbon emission minimization problem with a complex form. This important property is demonstrated with some simulation results based on the above-mentioned engine model. This feature makes the new formulation a good fit to the proposed scheme, where measured or estimated state variables of the engine model can be fed back to the control unit to re-calculate online the input profiles, such as the spark timing and the air/fuel ratio. Through online modification of the pre-determined input trajectories obtained from off-line calculations, the control performance degradation in practice due to the plant/model mismatch, which is equivalent to a significant increase of the tailpipe hydrocarbon emissions, can be reduced considerably.

An ensemble neuro-fuzzy radial basis network with self-adaptive swarm based supervisor and negative correlation for modeling automotive engine coldstart hydrocarbon emissions: A soft solution to a crucial automotive problem

In this paper, the authors propose a novel intelligent framework to identify the exhaust gas temperature (Texh) and the engine-out hydrocarbon emission (HCraw) during the coldstart operation of an automotive engine. These are two key variables affecting the cumulative tailpipe emissions (HCcum) over the coldstart phase, which is the number one emission-related problem for today's spark-ignited (SI) engine vehicles. The coldstart operation is regarded as a highly nonlinear, transient and uncertain phenomenon. The proposed identifier integrates different soft computational strategies, i.e. neuro-fuzzy computing, fuzzy controller, swarm intelligent computing, and ensemble network design, beneficial for capturing both uncertainty and nonlinearity of the problem at hand. Furthermore, concepts of negative correlation topology design and hierarchical pair competition based parallel training are extracted from literature to form a diverse and robust ensemble identifier. Training of each neuro-fuzzy sub-component in ensemble network is carried out using a hybrid learning scheme. One feature of the antecedent part of neuro-fuzzy system, i.e. number of linguistic terms for each variable, as well as characteristics of rules in rule base are adjusted using hierarchical fair competition-based parallel adaptive particle swarm optimization (HFC-APSO) and the rest of features, i.e. the shape of (membership functions) MFs and the consequent variables of each rule, are tuned using back-propagation (BP) and steepest descent techniques. As it was mentioned, the authors try to design an ensemble identifier with acceptable rate of generalization, robustness and accuracy. These features help them to tame the intuitive uncertainties associated with the rate of Texh and HCraw emission over the coldstart period. To do so, the potential characteristics of sub-components (solution domain of network design) are divided into a set of partitions and then HFC-APSO is utilized to explore/exploit each of those partitions. The exploration/exploitation rate of PSO (the core of HFC-APSO) is dynamically controlled by a fuzzy logic based controller. Hence, it is expected that HFC-APSO yields a set of accurate sub-identifiers with different operating characteristics. To further foster the diversity of the ensemble, negative correlation criterion is considered which obstructs the integration of identical sub-identifiers. The identification results demonstrate that the method is highly capable of providing an authentic model for estimation of Texh and HCraw emission during the coldstart period.

Early Model-Based Design and Verification of Automotive Control System Software Implementations

Verification and validation (V&V) are essential stages in the design cycle of automotive controllers to remove the gap between the designed and implemented controller. In this paper, an early model-based methodology is proposed to reduce the V&V time and improve the robustness of the designed controllers. The application of the proposed methodology is demonstrated on a cold start emission control problem in a midsize passenger car. A nonlinear reduced order model-based controller based on singular perturbation approximation (SPA) is designed to reduce cold start hydrocarbon (HC) emissions from a spark ignition (SI) combustion engine. A model-based simulation platform is created to verify the controller robustness against sampling, quantization, and fixed-point arithmetic imprecision. In addition, the results from early model-based verification are used to identify and remove sources of errors causing propagation of numerical imprecision in the controller structure. Thus the structure of the controller is modified to avoid or to reduce the level of numerical noise in the controller design. The performance of the final modified controller is validated in real-time by testing the control algorithm on a real engine control unit. The validation results indicate the modified controller is 17–63% more robust to different implementation imprecision while it requires lower implementation cost. The proposed methodology from this paper is expected to reduce typical V&V efforts in the development of automotive controllers.

Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission

In this paper, the authors intend to attest the applicability of computational intelligence for tackling a demanding real-life engineering problem, that is analysing the amounts of exhaust gas temperature () and the engine-out hydrocarbon emission () during the coldstart operation of an automotive engine with respect to the variation of engine speed (), spark timing (Δ) and air/fuel ratio. It has been proven that the coldstart phenomenon is highly transient, nonlinear and uncertain and therefore has absorbed an increasing attention of the researchers of automotive society. In this paper, we prove that a proper integration of intelligent methods can result in a very efficient tool suited for identifying the characteristics of coldstart phenomenon. To do so, a recent spotlighted natural-inspired optimiser called mutable smart bee algorithm is utilised to evolve a Gaussian generalised regression neural network such that the resulted framework can conduct a fast, robust and accurate identification. The outcomes of the conducted experiments endorse on the applicability of intelligent techniques for engine coldstart identification, and also, pave the way for future investigations on intelligent-based analysis of demanding automotive problems.

Control of cold start hydrocarbon emissions of motor bike engine by gasoline-ethanol blends and intake air heating

Two wheeled motor bikes are playing an important role in urban passenger transportation owing to ease of handling and affordable cost. Maximum amount of unburnt hydrocarbons (HC) and carbon monoxide (CO) are emitted during cold start of spark ignition engines. The current work presents the possible reduction of cold start HC emissions of 150 CC motorbike spark ignition (SI) engine with ethanolgasoline blends and/or with intake air heating by glow plug. Anhydrous ethanol was blended with unleaded gasoline in the range of 0% (E0) to 20% (E20) by volume to be used as fuel. The experimented parameters were intake air temperature, exhaust gas temperature, fuel consumption and exhaust gas emissions. Without intake air heating, E10 was found to be the optimum to reduce the cold start HC emissions by 23%. With intake air heating in the range of 40°C to 70°C, maximum HC emissions reduction was 23.8% for neat gasoline at 50°C and 33.6% for E10 blend at 60°C.

Real-Time Hybrid Switching Control of Automotive Cold Start Hydrocarbon Emission

Reduction of cold start hydrocarbon (HC) emissions requires a proper compromise between low engine-out HC emission and fast light-off of the three way catalytic converter (TWC). In this paper, a hybrid switching system is designed and optimized for reducing HC emissions of a mid-sized passenger car during the cold start phase of FTP-75 (Federal Test Procedure). This hybrid system has the benefit of increasing TWC temperature during the early stages of the driving cycle by switching between different operational modes. The switching times are optimized to reduce the cumulative tailpipe HC of an experimentally validated automotive emission model. The designed hybrid system is tested in real-time on a real engine control unit (ECU) in a model-in-the-loop structure. The results indicate the new hybrid controller reduces the HC emissions over 6.5% compared to nonswitching cold start controller designs.

Prediction of cold start hydrocarbon emissions of air cooled two wheeler spark ignition engines by simple fuzzy logic simulation

The cold start hydrocarbon emission from the increasing population of two wheelers in countries like India is one of the research issues to be addressed. This work describes the prediction of cold start hydrocarbon emissions from air cooled spark ignition engines through fuzzy logic technique. Hydrocarbon emissions were experimentally measured from test engines of different cubic capacity, at different lubricating oil temperature and at different idling speeds with and without secondary air supply in exhaust. The experimental data were used as input for modeling average hydrocarbon emissions for 180 seconds counted from cold start and warm start of gasoline bike engines. In fuzzy logic simulation, member functions were assigned for input variables (cubic capacity and idling rpm) and output variables (average hydrocarbon emission for first 180 seconds at cold start and warm start). The knowledge based rules were adopted from the analyzed experimental data and separate simulations were carried out for predicting hydrocarbon emissions from engines equipped with and without secondary air supply. The simulation yielded the average hydrocarbon emissions of air cooled gasoline engine for a set of given input data with accuracy over 90%.

Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification

In this investigation, the authors test the efficacy and reliability of optimally pruned extreme learning machine with ensemble of regularization techniques to identify the exhaust gas temperature (Texh) and the engine-out hydrocarbon emission (HCraw) during the coldstart operation of automotive engines. These variables have significant impact on the cumulative tailpipe emissions (HCcum) during a coldstart phenomenon, which is the number one emission-related problem for today's spark-ignited (SI) engine vehicles. To do so, the concepts of ensemble computing with negative correlation learning (NCL) and pruning of neurons are used in tandem to cope with difficulties associated with extracting knowledge from collected database. In the proposed framework, the regularization strategies are adopted to help us increasing the numerical stability of identifier while mitigating the redundant complexity of hidden neurons. Moreover, to increase the generalization of identifier and also reduce the effects of uncertainty, an ensemble of independent OP-ELM with NCL selection criterion called OP-ELM-ER-NCL is taken into account. To endorse the valid performance of OP-ELM-ER-NCL for modeling the characteristics of engine over the coldstart phenomenon, its performance is compared to a set of well-known identification systems, i.e. standard extreme learning machine (ELM), back-propagation neural network (BPNN), OP-ELM with different types of regularization, ensemble of regularized OP-ELM without negative correlation (OP-ELM-ER), and an ensemble ELM with a constrained linear system of leave-one-out outputs (E-LL), in terms of both accuracy and computational complexity. The simulation results indicate that the proposed identifier is really capable of capturing the knowledge of collected database. It is observed that its resulted accuracy and robustness are comparable with those obtained by identification methods available in the literature. Besides, using NCL strategy aids the ensemble to select the most effective regularization techniques and remove the redundant (ineffective) ones, which consequently decreases the complexity of final ensemble.

Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control

In this work, a systematic method is introduced to determine the required accuracy of an automotive engine model used for real-time optimal control of coldstart hydrocarbon (HC) emissions. The engine model structure and development are briefly explained and the model predictions versus experimental results are presented. The control design problem is represented with a dynamic optimization formulation on the basis of the engine model and solved using the Pontryagin’s minimum principle (PMP). To relate the level of plant/model mismatch and the control performance degradation in practice, a sensitivity analysis using a computationally efficient method is employed. In this way, the sensitivities or the effects of small parameter variations on the optimal solution, which is the minimum of cumulative tailpipe HC emissions over the coldstart period, are calculated. There is a good agreement between the sensitivity analysis results and the experimental data. The sensitivities indicate the directions of the subsequent parameter estimation and model improvement tasks to enhance the control-relevant accuracy, and thus, the control performance. Furthermore, they provide some insights to simplify the engine model, which is critical for real-time implementation of the coldstart optimal control system.

Molecular simulation design of a multisite solid for the abatement of cold start emissions

A highly effective hydrocarbon (HC) trap for the abatement of cold start HC emissions with specific adsorption sites for the different molecules present in the exhaust gases has been designed by means of molecular simulation tools, and later synthesized.

Effects of intake air temperature on SI engine emissions during a cold start

This paper presents the concept of preheating the intake air to reduce cold-start emissions from gasoline engines. The effects of intake air temperature on emissions from a gasoline engine were studied by using an air heater based on spark ignition. A light-duty vehicle test of cold-start emissions was carried out at an ambient temperature of−7°C according to New European Driving Cycle for Euro 3 and Euro 4 exhaust emission legislations. The results showed that preheating the intake air could effectively reduce both hydrocarbon (HC) and carbon monoxide (CO) emissions and improve fuel economy during a cold start. During idling conditions, the key phase of the HC and CO emissions was the first 40 s. With the aid of the air heater, cold-start HC and CO emissions from the vehicle were lower than the limit values in the Euro 3 and Euro 4 regulations.