Engine Control Strategies Research Articles

Impacts of MTBE in gasoline on the tailpipe ammonia emissions from China-6 certified passenger vehicles

Postcatalyst ammonia emissions from gasoline vehicles recently received attentions because of their contribution to the formation of urban secondary aerosols. To better understand whether fuel formulation would curb ammonia, the influence of methyl tertiary-butyl ether (MTBE) additive in gasoline on the tailpipe ammonia emissions from six China-6 compliant vehicles was investigated over the World Harmonized Light-duty Test Cycle (WLTC) at −7 ℃ and 23℃. Ammonia emissions were measured with MTBE-free and 10% MTBE-containing China-6 compliant gasoline fuels on the test vehicles. At – 7 ℃, the ammonia emissions with and without MTBE addition ranged from 0.15-17.07mg/km and 0.81−25.13mg/km, respectively. At 23 ℃, ammonia emissions were 0−7.4mg/km for MTBE-containing fuel and 0−8.76mg/km for MTBE-free fuel. More ammonia emissions were often observed as a result of maneuvering of enriched air-fuel mixtures to improve the combustion inability after cold-start and for de-NOx purpose, called as “λ sweep” (λ is excess air ratio). Due to the oxygen-containing nature of MTBE, its addition favors ammonia reduction. It is noted that “λ sweep”, an engine control strategy injecting extra fuel to deplete stored oxygen in the catalyst and suppress transient NOx formation during reacceleration after prolonged deceleration, may contribute to ammonia emissions due to the use of enriched air-fuel mixtures.

Research on random dynamic cylinder deactivation control strategy of engine based on nonlinear model prediction

In the process of cycle cylinder deactivation, the different proportion of cylinder deactivation between adjacent working cycles will cause the problem of uneven operation of each cylinder. In this paper, a random dynamic cylinder deactivation (RD-CDA) control method based on nonlinear model prediction is proposed. The RBF neural network model of the RD-CDA system is built. The K-means algorithm is applied to the offline training of the center position c of the hidden layer node. The P-nearest algorithm is used to train the hidden layer node width σ offline. The recursive least squares (RLS) algorithm is applied to train the output layer weight vector w online. The online adaptive change of the model is realized, and the model verification is carried out. Then, the nonlinear model predictive control system structures with the minimum torque fluctuation and brake specific fuel consumption rate (BSFC) are built respectively. Under different working conditions, the average torque fluctuation per cycle is improved by about 9% to 18.75%, and the average BSFC is improved by about 2 % -3.9 %, which verifies the effectiveness of this strategy in reducing the dynamic fluctuation of random dynamic cylinder deactivation torque and improving fuel economy.

The Coordinated Control Strategy of Engine Starting Process in Power Split Hybrid Electric Vehicle Based on Load Observation

During the process of moving, the power splitting hybrid vehicle is required to start the engine to transfer from pure electric mode to hybrid drive mode. According to the structural characteristics of the system, the engine starting process was divided into four stages, the engine starting process was established, and a multi-stage engine starting coordination control strategy was designed. In the engine reverse-drag process, the coordination control strategy was transformed into the optimal rotation rate tracker problem. In order to solve the load torque required in the tracking problem, a reduced-order observer was designed. Finally, the validity of the coordination control strategy was verified on the simulation platform of the electro-mechanical composite transmission system. The feasibility of the coordination control strategy was verified by hardware-in-the-loop simulation. The results show that the engine start coordination control strategy could achieve steady and fast engine start control. The maximal impact of the whole vehicle is reduced from 30 to 2.5 m/s3.

Research on modeling and control strategy of zero-carbon hybrid power system based on the ammonia-hydrogen engine

In the context of carbon neutrality, high efficiency, low carbon, and zero environmental impact have become an essential development direction of internal combustion engines (ICEs). Hydrogen energy has the advantage of having a high calorific value. It also has zero carbon emissions and is considered one of the significant alternative fuels for ICEs. Moreover, ammonia has a high octane number and has an anti-knock effect, which facilitates the mitigation of knock in hydrogen ICEs and potentially mitigates the negative impact of knock on engine volume power. Scholars have carried out some explorations on ammonia-hydrogen ICEs (AHICE), which have been applied in marine power systems. It is anticipated that AHICE will become one of the most significant development directions in the field of vehicle power systems in the future. However, there are still some problems with using AHICEs in passenger cars. The proposed work presented a zero-carbon hybrid power system based on an AHICE. Specifically, the external characteristics curve and power output boundary were obtained by bench experiments of the ICE under wide open throttle (WOT) conditions and diverse engine speeds and λ. Indeed, a hybrid system model consisting of an AHICE and a power battery was built where the AHICE was used to respond to the demanded power and the power battery was used to provide additional power and store the electrical energy converted from braking. Incidentally, an engine control strategy was developed to expand the knock limit and power boundary by dynamically adjusting the ammonia-hydrogen volume ratio. Finally, a hybrid power system simulation model was established based on MATLAB/Simulink. The CLTC-P and WLTC simulation results demonstrated that the zero-carbon hybrid system which comprised AHICE and power battery can work in the high efficiency area. The AHICE demonstrates lower fuel consumption while satisfying power requirements. This work provides a promising route to zero-carbon hybrid technology for the passenger car industry facing the challenge of carbon neutrality.

Application of Bayesian Decision Analysis to Estimate Occupational Noise Levels from Limited Exposure Data

In recent years, Bayesian statistical modeling approaches have emerged as valuable tools in Industrial Hygiene (IH), particularly in situations with limited exposure data. This paper delves into the application of Bayesian Statistical Models, specifically Bayesian Decision Analysis (BDA), to analyze occupational noise exposure data within two Similar Exposure Groups (SEGs) of groundskeeping workers, addressing the pervasive issue of high noise levels exceeding 85 dBA. The primary aim is to estimate exposure levels based on the location of the exposure profiles for the SEGs within the AIHA® Exposure Control Categories (ECCs) 0-4, later modified and expanded to AIHA-ECC 0-5. This study employs BDA to estimate noise exposures among groundskeepers, emphasizing the scarcity of literature on BDA in noise exposure rating. Groundskeepers, facing potential risk of hearing impairment due to prolonged exposure, constitute a crucial and critical demographic, with nearly one million individuals in the United States alone. Noise data from the two SEGs was analyzed by using the Exposure Assessment Strategies Committee—EASC- AIHA® IHData Analyst for BDA modeling, complemented with the AIHA IHSTAT™ for certainty rating. The resulting posterior graphs were utilized to categorize exposure profiles into respective AIHA ECCs relative to noise OEL. These findings provide valuable insights, indicating a posterior probability of approximately 43.5% and 56.5% with 95% confidence that noise exposure levels fall within AIHA ECC 3 and AIHA ECC 4, respectively. However, while the 95% percentile decision statistics suggest exposure values lower than the OEL, the Upper Tolerance Limit (UTL) significantly surpass the OEL indicating low certainty regarding these exposure values for employees. Notably, the UTLs suggest that the SEGs are experiencing noise levels above the legally required 90 dBA for more than 5% of the time, which is an unacceptable scenario. Recommendations include implementing protective strategies such as regular training, surveillance, and monitoring, alongside engineering control strategies and mandatory use of personal protective equipment. These results underscore the critical need for refining judgments about noise exposures based on variability within and between employees, suggesting the use of additional statistical tools such as ANOVA (single-multivariate), MATLAB, and Python.

Evaluation of NOx and PN Emission in Relation to Actuator Control.

This study aimed to investigate the interrelationships between key harmful emission components, nitrogen oxides (NOx), and particulate numbers (PNs) in diesel engine exhaust and the control actuators of diesel engines. This research involved conducting a series of experiments under fixed parameters within an engine brake laboratory environment to elucidate these correlations. The objectives of this study were to conduct a comprehensive review of the relevant emissions technology literature and a comparative assessment of particle measurement methods based on dilution ratios and develop innovative aerosol preparation principles tailored to condensation particle measurement. Additionally, this research involved designing and implementing an aerosol preparation unit based on the newly developed principles, along with the creation of test cell control programs using the AVL PUMA Open TST editor interface and Visual Basic. Furthermore, this study was concerned with conducting evaluations of fixed-parameter engine dynamometer tests to explore the functional relationships between the emission of 10/23 nm particles, NOx emissions, common rail pressure variations, and exhaust gas recirculation levels. This study aimed to enhance the understanding of diesel engine emissions dynamics and contribute valuable insights for developing more efficient and environmentally friendly engine control strategies.

Innovative torque-based control strategy for hydrogen internal combustion engine

Over the past years, several efforts have been made to reduce greenhouse gas emissions coming from the transport sector. Due to the highly efficient CO2-free combustion and low manufacturing costs, Hydrogen Internal Combustion engines (H2ICEs) are considered one of the most promising solutions for the future of medium and heavy duty vehicles. However, the combustion of an air-hydrogen mixture presents challenges related to the production of nitrogen oxides (NOx) and high knock tendency, mainly due to the chemical characteristics of the fuel. Although these problems can be mitigated by the use of a lean mixture, which is also useful to increase the combustion efficiency, the presence of excess air reduces exhaust temperatures and, consequently, the enthalpy content in the exhaust would be limited, leading to a reduced boosting capability. Therefore, a proper control of mixture preparation and combustion phasing is mandatory to limit NOx emissions, avoid abnormal combustions, and maximize efficiency without performance limitations.This paper focuses on the design of a dedicated control strategy for H2ICEs. Starting from a previously validated 1-D engine model operated with hydrogen, a 0-D Artificial Neural Network (ANN) - based engine model has been designed and calibrated. By using the obtained fast running ANN-based model, an innovative torque-based engine controller has been developed and both engine and controller models have been tested covering different torque profiles. The results show good accuracy within a range of ±5% on producing the requested torque by controlling the centre of combustion.

Electrical Engineering and Intelligent Control Strategies in Renewable Energy Integration: Enhancing Energy Sustainability

This paper discusses in detail the important role and application value of electrical engineering and intelligent control strategies in the integration of renewable energy, and proposes the viewpoints of improving the utilization rate of renewable energy and ensuring the stability of energy systems through electrical engineering and intelligent control strategies. At the same time, it also looks forward to the future development trend.

A model-based control strategy for turbo-shaft engine

This paper takes the turbo-shaft engine (the power source of the helicopter) as the research object, and mainly conducts the simulation of its related control law and control structure. The working principle of the turbo-shaft engine is briefly introduced initially, and then the component-level model of the turbo-shaft engine is established. This paper focuses on the design of the control law upon the turbo-shaft engine, and verifies the designed control law through the results of simulation by MATLAB/Simulink. Finally, the relevant data collected from 240KW hybrid system is taken into the consideration to verify the control algorithm. The results show that the control accuracy of the corresponding control algorithm in this paper is high (accuracy≤5%).

Enhancing the performance of syngas-diesel dual-fuel engines by optimizing injection regimes: From comparative analysis to control strategy proposal

This study introduced an innovative technology of syngas direct injection through the twinning injector system. This technology can simultaneously adjust the equivalence ratio and the mass of fresh gas, for a syngas-diesel dual-fuel engine converted from a conventional diesel engine. The current work was carried out in two stages, in which the evaluation of engine performance and mixture formation under two various syngas injection strategies such as the port-premixed mixture method and the direct injection method were examined in the first stage. In the second stage, the load control strategy of the syngas-diesel dual-fuel engine, which aims to attain low greenhouse gas emissions and maintain output power, was thoroughly presented. The results revealed that the syngas direct-injection strategy significantly influences the power derating of the dual-fuel engine at a fixed pilot diesel injection. Indeed, when the stop injection angle was held constant at 270°CA, the power derating of the dual-fuel engine increased from 3.78 % to 22.74 % as the start injection angle was increased from 60 °CA to 100°CA, implying that this injection strategy could be suitable for dual-fuel engines with a minor variation in their loading regime. Nonetheless, when the start injection angle was fixed at 60 °CA, the derating power of the dual-fuel engine increased from 7.41 % to 27.84 % as the stop injection angle was advanced from 280 °CA to 220 °CA, showing that this injection strategy was better suited for dual-fuel engines that experienced larger variations in their loading regime. Direct injection of syngas using the twinning injector system at an injection pressure of 6 bar, in conjunction with a suitable start/stop injector strategy, constitutes an appropriate solution for maintaining power derating in syngas-diesel dual-fuel engines below an acceptable threshold of 10 %.

LQTI boost pressure and EGR rate control of a diesel air charge system with eBoost assistance

Turbocharged diesel engines often suffer significant response delay due to so-called turbo-lag, especially engines with large displacement. For this reason, many technologies have been developed to reduce turbo-lag. This paper develops a coordinated control strategy for a diesel engine equipped with an eBoost (electrical compressor) system to significantly reduce turbo-lag. A multiple-input and multiple-output (MIMO) Linear Quadratic Tracking with Integral (LQTI) control strategy, along with its scheduling logic, is developed for the Ford 6.7 L 8-cylinder diesel engine equipped with a variable geometry turbocharger (VGT), exhaust gas recirculation (EGR), and added eBoost along with a bypass valve. Note that the existing production engine does not have an eBoost and bypass valve. Multiple model-based LQTI controllers were designed at different engine operational conditions based on the associated linearized models, and the control outputs are scheduled based upon the engine load condition and bypass valve position. The developed control strategy is validated in both simulation and experimental studies, and the test results show a reduction in engine response time by up to 81.36% in terms of reaching target intake manifold (boost) pressure following a load step-up, compared with the production configuration (without eBoost and bypass valve) with no significant impact on NOx emissions.

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

Brushless DC (BLDC) motors, which have small volumes, are widely used in many areas from the aviation industry to industrial applications due to their high efficiency and torque. In parallel with the development of technology, the field of use continues to expand with the development of BLDC engine (BLDCM) control strategies and the decrease in control costs. In this thesis study, it is aimed to minimize the observed changes in rotor speed compared to the reference speed. To achieve this, PID parameters were tried to be changed simultaneously with fuzzy control techniques, taking the error value as a reference. The control system of the BLDC engine was designed in the MATLAB/Simulink environment. In the simulation, the operating stability of classical PID and PID with updated fuzzy-based parameters on two engines with the same features was compared at different speeds. As a result of the research, it was concluded that the correction of the speed observed in the rotor of the PID-controlled motor, whose fuzzy logic-based coefficients were updated, based on the reference speed was more stable and the percentage of exceedance for the reference value was lower, compared to the classical PID controlled motor.

A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss

The acceleration performance of turbofan engine is mainly limited by the surge boundary of high-pressure compressor (HPC). A certain amount of surge margin (SM) is reserved between the boundary and the acceleration schedule (AS) of acceleration control to avoid the risk caused by intake distortion, etc. However, the performance improvement by low SM design is the requirement for advanced engines. Hence it must execute a fault-tolerant strategy for the impact of sensor error, fuel metering error, etc. This paper proposes a method by adopting four ASs for weighted average to replace the single AS. The weights are altered by the multi-layer perceptron with reference to the real-time fuel results from four ASs, intake condition and rotor speed. For safety and accuracy, an exponential Gumbel loss function is introduced into the model training. The simulation results demonstrate that the method can significantly tolerate the impact of a single fault and the normal deviations of other factors without causing SM of HPC to be less than 3.6%. These also indicate that the acceleration time on the ground does not exceed acceptable 5 s, with a median that is 0.34 s less than the minimum method of two ASs within the flight envelope.

Deep reinforcement learning implementation on IC engine idle speed control

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies.

Long-Cycle Stable Operation of Fluorine-Ion Batteries at Room Temperature Enabled by Advanced Interface Engineering and Ion Diffusion Kinetics Control Strategy

Fluorine-ion batteries (FIBs) offer a high theoretical energy density of 5000 Wh L⁻¹ and superior safety, but they face significant challenges such as electrode material dissolution and volume changes during...

Size dependent effectiveness of engineering and administrative control strategies for both short- and long-range airborne transmission control

Using a size-dependent transmission model linking short- and long-range airborne transmission, we re-estimate the effectiveness of control strategies in mitigating airborne transmission, while concurrently underscoring the need to consider droplet size in airborne transmission dynamics.

Widening the operation limits of a SI engine running on neat ammonia

Present study aims to investigate the lean-burn characteristics of ammonia in a pre-mixed SI (Spark Ignition) engine and the influence of spark energy and discharge characteristics on engine performance and emissions. A wide range of engine operation conditions have been explored with particular focus on emission measurement. Engine parameters have been systematically swept to explore engine control strategies that would be operational in real engine application with a wide range of engine loads and tight emission regulations.Most previous studies required more than 5 % addition of hydrogen to the ammonia in order to achieve stable ignition for wide operation conditions. This has been a major obstacle in the application of ammonia for SI engines due to safety and system complexity issues. In present work, a comprehensive effort was made to understand and optimize the spark ignition system in order to mitigate the need for an ignition improver and all experiments have been performed with 100 % neat ammonia.With present engine modifications, emissions of unburned ammonia was measured to be between 5000 and 10000 ppm and with a combustion efficiency above 95 %. The unburned ammonia is believed to originate from crevices, particularly at the ring-pack. The NOx emission was between 4000 and 8000 ppm even at high excess air ratios. The emission of N2O is critical to minimize, as it is a strong greenhouse gas. It was measured to be between 20 and 80 ppm and appear to be related to post oxidation reactions of ammonia released from crevices during expansion.Advancing the ignition timing has proved to be an efficient handle for balancing the emissions of NH3 and NOx. These emissions will be reduced to H2O and N2 in an SCR catalyst if they are correctly balanced. Fortunately, advancing ignition timing also minimizes the formation of N2O.

Investigation of CO2 and PN emission characteristics according to the propane content for a LPG engine

Many countries of the world aim to achieve carbon neutrality by 2050 and has a set goal for carbon reduction in 2030. Among eco-friendly alternative fuels, engines using liquefied petroleum gas (LPG) have been developed to address regulations. LPG fuel, consisting of a mixture of butane and propane, is currently under investigation to assess how altering the propane content influences fuel atomization, injection quantity, and exhaust gas. This study analyzes the effect of propane content on the engine control strategy and exhaust gas emissions (CO2, particle number (PN)) from LPG fuel based on engine experiments, chassis experiments, and Real-world Driving Emission (RDE) tests. The results indicated that with increased propane content, the injection duration and quantity increased, resulting in increased CO2 emissions but decreased PN emissions. The differences in emission increased according to propane content. Across chassis and RDE tests, higher propane content consistently associated with increased emissions (CO2), while PN consistently stayed at lower levels. The study results offer insights into potential research directions proposed at determining the optimal propane content for minimizing CO2 and PN emissions.

Simulation of Dual Combustion Cycle in Internal Combustion Engines using MATLAB

The aim of this study is to develop a comprehensive mathematical model to accurately represent the dual combustion cycle in internal combustion engines, with a focus on improving combustion efficiency and reducing emissions. The novelty of this research lies in the integration of advanced combustion models within the mathematical framework to capture the unique characteristics of dual combustion cycles. The model will consider factors such as combustion characteristics, fuel-air mixing, heat transfer, and emissions. By accurately simulating the dual combustion cycle, valuable insights can be gained into the complex interactions occurring within the engine, leading to the development of optimized engine design and control strategies. The proposed model will be implemented using MATLAB, which offers flexibility and computational efficiency. The results obtained from the simulation can be validated against experimental data to ensure the accuracy and reliability of the model. This study is expected to contribute to the advancement of dual combustion cycle technology and provide valuable guidance for the design and optimization of future internal combustion engines.

A versatile volume-based modeling technique of distributed local quadratic convergence for aeroengines

For advanced aero-engine design and research, modeling and simulation in a digital environment is indispensable, especially for engines of complicated configurations, such as variable cycle engines (VCE) and adaptive cycle engines (ACE). Also, in the research of future smart engines, reliable real-time digital twins are paramount. However, the 2 dominant methods that used in solving the simulation models, Newton-Raphson (N-R) method and volume-based method, are not fully qualified for the study requirements, because neither of them reaches the satisfactory balance of convergence rate and calculating efficiency. In this study, by deeply analyzing the mathematical principle of these 2 methods, a novel modeling and solving method for aero-engine simulation, which integrates the advantages of both N-R and volume-based methods, is established. It has distributed architecture and local quadratic convergence rate. And a novel modeling method for variable area bypass injectors (VABI) is put forward. These facilitate simulation of various configurations of aero-engines. The modeling cases, including a high bypass-ratio (BPR) turbofan and an ACE, illustrate that the novel technique decreases the iterations by about two-thirds comparing with volume-based method, while the success rate of convergence remains over 99%. This proves its superiority in both convergence and calculating efficiency over the conventional ones. This technique can be used in advanced gas turbine engine design and control strategy optimization, and study of digital twins.