Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

A Multiphysics Co-Simulation Framework of a Gas Engine and Three-Way Catalyst toward a Complete Vehicle Design Model

In view of the increasingly stringent emission regulations, the automotive sector needs considerable support from the development of robust and reliable engine and aftertreatment models. Accurate reproduction of engine-out and tailpipe pollutants plays a crucial role in complying with these legislations. Given the difficulty in characterizing some critical phenomena, frequently caused by strong dynamics and related to experimental uncertainties, communication between several calibrated and reliable models is mandatory. This is certainly valid for powertrains that will be powered with alternative gas fuels such as natural gas, bio-methane and hydrogen in the future. This paper describes a methodology to co-simulate a 1D CNG HD 6-cyl engine model and a 1D quasi-steady three-way catalyst model in a global framework for high-fidelity virtual prototyping of the vehicle system. Through the implementation of a dedicated control logic in MATLAB/Simulink, the modeling architecture allows for the reproduction of the engine performance parameters together with the evaluation of the TWC pollutants’ conversion efficiency. An extensive database of experimental tests was used to assess the model response. The latter was validated in multiple steady-state operating conditions of the engine workplan. Using a semi-predictive combustion model, the validation was carried out over a wide range of different air-to-fuel ratios and during fast rich/lean transitions to evaluate the formation and conversion phenomena of the main chemical species, both engine-out and tailpipe. Subsequently, the complete model was validated in dynamic conditions throughout a WHTC, accurately reproducing the cut-off phases and their sudden accelerations. The numerical–experimental agreement on pollutant reproduction is generally good and globally below 3%. Larger deviations occur in extremely rich conditions and in CH4 emission evaluation due to the lack of information related to the combustion process and chemical mechanisms involving the Pd surface

A Control-Oriented Spatially Resolved Thermal Model of the Three-Way-Catalyst

The three-way-catalyst (TWC) is an essential part of the exhaust aftertreatment system in spark-ignited powertrains, converting nearly all toxic emissions to harmless gasses. The TWC’s conversion efficiency is significantly temperature-dependent, and cold-starts can be the dominating source of emissions for vehicles with frequent start/stops (e.g. hybrid vehicles). In this paper we develop a thermal TWC model and calibrate it with experimental data. Due to the few number of state variables the model is well suited for fast offline simulation as well as subsequent on-line control, for instance using non-linear state-feedback or explicit MPC. Using the model could allow an on-line controller to more optimally adjust the engine ignition timing, the power in an electric catalyst pre-heater, and/or the power split ratio in a hybrid vehicle when the catalyst is not completely hot. The model uses a physics-based approach and resolves both axial and radial temperature gradients, allowing for the thermal transients seen during heat-up to be represented far more accurately than conventional scalar (i.e. lumped-temperature) real-time models. Furthermore, we also use a physics-based chemical kinetics reaction model for computing the exothermic heat of reaction and emission conversion rate which is temperature and residence-time-dependent. We have performed an experimental campaign with a standard spark-ignited engine and a commercial TWC, where we measured steady-state operation and cold-start transient behavior. This experimental data allowed us to tune the model, where we found excellent matching between the measured and modeled tailpipe emissions. Modeling the radial temperature gradient improved the relative accuracy of the conversion efficiency by 15%, and simulations indicate the potential for an absolute improvement by 15 percentage points for some cases. Furthermore, the modeled TWC temperature evolution for a cold-start was typically within \ub110 \ub0 C of the measured temperature (with a maximal deviation of 20 \ub0C). The proposed model thus bridges a gap between heuristic models suited for on-line control and accurate models for slower off-line simulation

Cold-Start Modeling and On-Line Optimal Control of the Three-Way Catalyst

We present a three-way catalyst (TWC) cold-start model, calibrate the model based on experimental data from multiple operating points, and use the model to generate a Pareto-optimal cold-start controller suitable for implementation in standard engine control unit hardware. The TWC model is an extension of a previously presented physics-based model that predicts carbon monoxide, hydrocarbon, and nitrogen oxides tailpipe emissions. The model axially and radially resolves the temperatures in the monolith using very few state variables, thus allowing for use with control-policy based optimal control methods. In this paper, we extend the model to allow for variable axial discretization lengths, include the heat of reaction from hydrogen gas generated from the combustion engine, and reformulate the model parameters to be expressed in conventional units. We experimentally measured the temperature and emission evolution for cold-starts with ten different engine load points, which was subsequently used to tune the model parameters (e.g. chemical reaction rates, specific heats, and thermal resistances). The simulated cumulative tailpipe emission modeling error was found to be typically − 20% to + 80% of the measured emissions. We have constructed and simulated the performance of a Pareto-optimal controller using this model that balances fuel efficiency and the cumulative emissions of each individual species. A benchmark of the optimal controller with a conventional cold-start strategy shows the potential for reducing the cold-start emissions

Towards Optimal Real-Time Automotive Emission Control

The legal bounds on both toxic and carbon dioxide emissions from automotive vehicles are continuously being lowered, forcing manufacturers to rely on increasingly advanced methods to reduce emissions and improve fuel efficiency. Though great strides have been made to date, there is still a large potential for continued improvement. Today, many subsystems in vehicles are optimized for static operation, where subsystems in the vehicle perform well at constant operating points. Extending optimal operation to the dynamic case through the use of optimal control is one method for further improvements.This thesis focuses on two subtopics that are crucial for implementing optimal control; dynamic modeling of vehicle subsystems, and methods for generating and evaluating computationally efficient optimal controllers. Though today\u27s vehicles are outfitted with increasingly powerful computers, their computational performance is low compared to a conventional PC. Any controller must therefore be very computationally efficient in order to feasibly be implemented. Furthermore, a sufficiently accurate dynamic model of the subsystem is needed in order to determine the optimal control value. Though many dynamic models of the vehicle\u27s subsystems exist, most do not fulfill the specific requirements set by optimal controllers.This thesis comprises five papers that, together, probe some methods of implementing dynamic optimal control in real-time. Two papers develop optimal control methods, one introduces and studies a cold-start model of the three-way catalyst, one paper extends the three-way catalyst model and studies optimal cold-start control, and one considers fuel-optimally controlling the speed of the engine in a series-hybrid. By combining the method and model papers we open for the potential to reduce toxic emissions by better managing cold-starts in hybrid vehicles, as well as reducing carbon dioxide emissions by operating the engine in a more efficient manner during transients

Effect of Catalytic Converter on I.C. Engine Emission parameters - a Review

This paper is a literature review on effect of Catalytic convertor on CI engine parameters (emission parameter). The emissions from CI Engines have adverse effects on human health, living organisms, and environment. The major emission form diesel engine are: unburned hydrocarbon, oxides of carbon (COX), oxides of nitrogen (NOX), oxides of sulphur (SOX), and solid carbon particulate matter (PM). Similarly, for biodiesel it was observed that HC, CO, CO2 were lowered, while NOX remains still higher. For in-cylinder reduction of emission Catalytic converter are effective for CI Engines. It was found that using Catalytic convertor reduces the emission from engine effectively

A review on exhaust gas after-treatment of lean-burn natural gas engines – From fundamentals to application

Modern lean-operated internal combustion engines running on natural gas, biogas or methane produced from wind or solar energy are highly fuel-efficient and can greatly contribute to securing energy supply, e.g. by mitigating fluctuations in the power grid. Although only comparably low emission levels form during combustion, a highly optimized emission control system is required that converts pollutants over a wide range of operation conditions. In this context, this review article pinpoints the main challenges during methane and formaldehyde oxidation as well as selective catalytic reduction of nitric oxides. The impact of catalyst formulation and operation conditions on catalytic activity and selectivity as well as the combination of several technologies for emission abatement is critically discussed. Additionally, recent experimental and theory-based progress and developments are assessed, allowing coverage of all time and length scales relevant in emission control, i.e. ranging from mechanistic and fundamental insights including atomic-level phenomena to full-scale applications

Automotive Powertrain Control — A Survey

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

Control-oriented modelling of three-way catalytic converter for fuel-to-air ratio regulation in spark ignited engines

[EN] The purpose of this paper is to introduce a grey-box model of three-way catalytic converter, which is capable of estimating the oxygen storage level to aid the fuel-to-air ratio control in spark ignited engines. As it is well-known, the prime parameter that drives the transient dynamics in current three-way catalytic converter is their capability to store a certain amount of oxygen, then allowing to oxidize some pollutant species such as carbon monoxide or hydrocarbons even at rich conditions during short periods of time. Since oxygen storage level is considered a good indicator of the catalyst state but it cannot be directly measured, a model based real-time capable estimation like the one proposed in this paper could be valuable. The model accounts for oxygen storing as well as oxidation and reduction of the main species involved, taking as inputs fuel-to-air equivalence ratio, air mass flow, temperature and gas composition at three-way catalyst inlet. From these inputs, oxygen storage level and brick temperature are calculated as model states, which finally provide the gas composition downstream of the catalyst as output. In addition, a simplified model of narrowband lambda sensor is included, it provides a voltage from gas composition at the outlet of the catalyst and allows to assess the model behaviour by comparison with the on-board lambda sensor measurements. Finally, the validation of the model performance by means of experimental test as well as different practical cases, where the benefits of oxygen storage level estimation plays a key role, are introduced.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the support of Spanish Ministerio de Economía, Industria y Competitividad through project TRA2016-78717-R.Guardiola, C.; Climent, H.; Pla Moreno, B.; Real, M. (2019). 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