Engine Electronic Control Unit Research Articles

Effects of different national standards and driving conditions on pollutants and persistent free radicals in diesel engine exhaust particles

Exhaust pollutants from diesel vehicles constitute an important portion of air pollution. In addition to conventional pollutants such as carbon and nitrogen oxides, persistent free radicals (PFRs) exist on exhaust particles could also pose a health risk by inducing oxidative stress. However, recently there is a dearth of comprehensive studies addressing this concern. In this study, the exhaust particles emitted by tractors adhering to two prominent emission standards, namely GB III and GB I, that currently hold the largest tractor stocks, were collected under various working conditions. For the first time, this study dynamically monitored the characteristics of PFRs in exhaust particles emitted by internal combustion engines using biodiesel as fuel during driving on rural actual roads. Due to the stricter emission standard of GB III, which resulted in lower particle emissions, the concentration of PFRs emitted under the same fuel consumption was ultimately reduced. Noteworthily, while advancements like fuel atomization under engine electronic control unit (ECU) and the utilization of oxidation catalysts with low ignition temperature successfully decreased polycyclic aromatic hydrocarbons (PAHs) emission by altering combustion in the engine, they also resulted in heightened carbon structure defects, leading to a higher concentration of PFRs emitted per unit mass of particles. Additionally, compared to non-plowing driving conditions, localized hypoxia during plowing that could cause excessive fuel injection and uneven formation of fuel-air mixture resulted in the emission of a significant amount of carbon-containing substances with unstable structures. Consequently, this scenario led to an increased concentration of PFRs during plowing conditions. The results of this study demonstrated that the stricter emission standards and optimized technology could better reduce the concentration and types of PFRs in exhaust particles, reducing the environmental risk of exhaust particle, which is also of great significance for the realization of pollution reduction and carbon reduction goals.

Data-Driven Model for Real-Time Estimation of NOx in a Heavy-Duty Diesel Engine

The automotive sector is greatly contributing to pollutant emissions and recent regulations introduced the need for a major control of, and reduction of, internal combustion engine emissions. Artificial intelligence (AI) algorithms have proven to hold the potential to be the thrust in the state-of-the-art for engine-out emission prediction, thus enabling tailored calibration modes and control solutions. More specifically, the scientific literature has recently witnessed strong efforts in AI applications for the development of nitrogen oxides (NOx) virtual sensors. These latter replace physical sensors and exploit AI algorithms to estimate NOx concentrations in real-time. Still, the calibration of the algorithms, together with the appropriate choice of the specific metric, strongly affects the prediction capability. In the present paper, a machine learning-based virtual sensor for NOx monitoring in diesel engines was developed, based on the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The latter is commonly used in the literature to deploy virtual sensors due to its high performance, flexibility and robustness. An experimental campaign was carried out to collect data from the engine test bench, as well as from the engine electronic control unit (ECU), for the development and calibration of the virtual sensor at steady-state conditions. The virtual sensor has, since then, been tested throughout on an on-road driving mission to assess its prediction performance in dynamic conditions. In stationary conditions, its prediction accuracy was around 98%, whereas it was 85% in transient conditions. The present study shows that AI-based virtual sensors have the potential to significantly improve the accuracy and reliability of NOx monitoring in diesel engines, and can, therefore, play a key role in reducing NOx emissions and improving air quality.

Аналіз конструктивних особливостей різних типів коробок перемикання передач автомобілів

One of the important components of the car design is the gearbox, which is part of the transmission. It is the gearbox that is responsible for changing the direction of the car and its speed. This affects traction and dynamic properties in general. The purpose of this article is to analyze the design features of various types of car gearboxes. The work analyzes the design features of various types of car gearboxes. The purpose of the gearbox, the effect on the traction-speed properties, which are a set of properties that determine the possible speed ranges and the maximum intensity of acceleration when the car is moving in the traction mode in various operating conditions, are given, as well as the main requirements that it should provide. The peculiarities of the construction of mechanical gearboxes, their structure, the principle of operation and the possibility of placement in a car are considered. The structure and main elements of the hydromechanical transmission (HMT) design are presented. It consists of three main parts: a torque converter, a manual gearbox and a control system. The design features of the gearbox with V-belt and toroid variators are considered. The principle of operation of variators, which ensures a stepless change of the gear ratio within the established limits, is presented. The design of the gearbox with a double clutch is considered, which ensures uninterrupted transmission of the power flow to the drive wheels. The gearbox is controlled by an electronic control unit, which receives information from sensors located in the box and is connected by a high-speed bus to the engine's electronic control unit. The main advantages and disadvantages of each type of considered gearboxes are highlighted. The mechanical gearbox has proven itself as a unit that is simple in design and reliable in operation. The prospects for the development of the gearbox are the development of new electronic control systems, the use of modern materials with high performance indicators and the use of high-performance methods of mechanical processing.

Multidimensional Computational Fluid Dynamics Combustion Process Modeling of a 6V150 Diesel Engine

Abstract The smart diesel program requires the engine electronic control unit to consider additional parameters, such as altitude and climatic conditions, in the mapping calibration process. A specially designed environmental simulation cabin, which can simulate environmental conditions at any longitude and dimension, would allow dynamometer testing to be performed indoors. Considering its high cost, a three-dimensional (3D) computational fluid dynamics (CFD) is needed to guide and/or complement experimental researches. As a result, the main objective of this study was to establish a 3D RANS model (i.e., reasonable computational cost and running time) that can provide in-cylinder details and predict the efficiency of a 6V150 diesel engine under varied operating conditions. A sector mesh approach was employed, considering only the compression, combustion, and expansion periods from intake valve closing to exhaust valve opening. The results indicated that the model simulated cylinder pressure agreed well with the experimental data, with relative errors of less than 6% during the primary compression, combustion, and expansion. Further, the model predicted heat release phasing was inconsistent with the experimental results, with absolute errors of less than one crank angle degree for peak pressure location, CA50, and ignition delay. In addition, the multidimensional model captured the effects of environmental pressure and temperature on spray formation (i.e., the dominant phenomenological event). Moreover, the model reasonably reproduced the effects of engine control variables on performance and emissions. All these observations demonstrated the validity of the selection and calibration of geometry, chemistry, and submodels including turbulence, spray, heat transfer, combustion, etc. Overall, the model was deemed capable of predicting combustion characteristics under extreme conditions, including high-temperature, high-cold, and high-altitude environments, which can facilitate the development of smart engines.

Towards a Powerful Hardware-in-the-Loop System for Virtual Calibration of an Off-Road Diesel Engine

A common challenge among internal combustion engine (ICE) manufacturers is shortening the development time while facing requirements and specifications that are becoming more complex and border in scope. Virtual simulation and calibration are effective instruments in the face of these demands. This article presents the development of zero-dimensional (0D)—real-time engine and exhaust after-treatment system (EAS) models and their deployment on a Virtual test bench (VTB). The models are created using a series of measurements acquired in a real test bench, carefully performed in view of ensuring the highest reliability of the models themselves. A zero-dimensional approach was chosen to guarantee that models could be run in real-time and interfaced to the real engine Electronic Control Unit (ECU). Being physically based models, they react to changes in the ECU calibration parameters. Once the models are validated, they are then integrated into a Simulink® based architecture with all the Inputs/Outputs connections to the ECU. This Simulink® model is then deployed on a Hardware in the Loop (HiL) machine for ECU testing and calibration. The results for engine and EAS performance and emissions align with both steady-state and transient measurements. Finally, two different applications of the HiL system are presented to explain the opportunities and advantages of this tool integrated within the standard engine development. Examples cited refer to altitude calibration activities and soot loading investigation on vehicle duty cycles. The cases described in this work are part of the actual development of one of the latest engines developed by Kohler Engines: the KDI 1903 TCR Stage V. The application of this methodology reveals a great potential for engine development and may become an essential tool for calibration engineers.

Supervisory Control for NO$_\text{x}$ Emission Compliance of Heavy-Duty Vehicles

This article provides a detailed description of a supervisory control problem for a heavy-duty vehicle equipped with a diesel engine and an exhaust aftertreatment system (EATS). The control problem considers the coupling between the engine and EATS and assumes the availability of predictive driving information for a long horizon. The article also proposes a computationally efficient supervisory control algorithm, which is implemented in an existing engine electronic control unit. The algorithm is based on distributed economic nonlinear MPC. It minimizes total fuel consumption (by the diesel engine and EATS) and fulfils Euro VI real-driving and world harmonized transient cycle based NO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\text{x}$</tex-math></inline-formula> emission legislation for the heavy-duty diesel vehicle. The performance of the algorithm is evaluated using a high-fidelity simulation platform, which includes a validated GT-POWER model of a 13 L turbo compound diesel engine and the first-principles model of an EATS. Simulation results show that the supervisory controller has 1.7% less total fuel consumption, and 88.4% less NH <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> slip, compared to a baseline controller, to achieve the same real-driving NO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\text{x}$</tex-math></inline-formula> emission.

A Segmentation Model of ECU Excitation Signal Based on Characteristic Parameters.

According to the basic structure and working principle of the excitation signal sensors of a diesel engine electronic control unit (ECU), a segmentation model of an ECU excitation signal based on characteristic parameters (ESCP-SM) is proposed. In the ESCP-SM, the ECU excitation signal is divided into several parts, and each part has its characteristic parameters model. By using the same global parameters and strictly controlling each part’s proportional parameters, the ESCP-SM can achieve signal alignment and dynamic frequency modulation. Based on the simulation experiment, spectrum analysis proves that this modeling method ensures that the original signal’s effective information is not lost. Pearson similarity analysis shows that the similarity between the simulation signal and practical signal reaches 74%, exhibiting strong correlation. In addition, we set up a physical testing environment. ESCP-SM is realized based on virtual instrument technology, and provides excitation signals for a Komatsu 8 ECU. By modifying the parameter configuration, the ECU can drive the injector to work correctly.

Effect of Hydrogen Addition on the Energetic and Ecologic Parameters of an SI Engine Fueled by Biogas

The global policy solution seeks to reduce the usage of fossil fuels and greenhouse gas (GHG) emissions, and biogas (BG) represents a solutions to these problems. The use of biogas could help cope with increased amounts of waste and reduce usage of fossil fuels. Biogas could be used in compressed natural gas (CNG) engines, but the engine electronic control unit (ECU) needs to be modified. In this research, a spark ignition (SI) engine was tested for mixtures of biogas and hydrogen (volumetric hydrogen concentration of 0, 14, 24, 33, and 43%). In all experiments, two cases of spark timing (ST) were used: the first for an optimal mixture and the second for CNG. The results show that hydrogen increases combustion quality and reduces incomplete combustion products. Because of BG’s lower burning speed, the advanced ST increased brake thermal efficiency (BTE) by 4.3% when the engine was running on biogas. Adding 14 vol% of hydrogen (H2) increases the burning speed of the mixture and enhances BTE by 2.6% at spark timing optimal for CNG (CNG ST) and 0.6% at the optimal mixture ST (mixture ST). Analyses of the rate of heat release (ROHR), temperature, and pressure increase in the cylinder were carried out using utility BURN in AVL BOOST software.

The Migration of Engine ECU Software From Single-Core to Multi-Core

As multiple functions have been added to single-core-based engine electronic control units (ECUs) in vehicles, automotive researchers and manufacturers have actively studied multi-core architecture for engine ECUs. Multi-core architecture can provide load balancing and parallelism that can meet the requirements of international organization standard (ISO) 26262. However, since real-world engine ECUs have the most complex automotive open system architecture (AUTOSAR)-based control logic and datasets among automotive ECUs, developing multi-core-based engine ECUs is a substantial amount of work. Thus, automotive researchers and manufacturers will need new methodologies for multi-core-based engine ECUs. In this paper, we focus on designing a multi-core migration methodology and applying it to a real-world AUTOSAR-based engine ECU from HYUNDAI. We verify its practicability and enhanced performance. In conclusion, through connection with other automotive domain ECUs, it is demonstrated that a multi-core engine ECU using our migration technology can be applied in real-world automotive vehicles, leading to a significant improvement in performance.

Short communication: The effects of not controlling the hydrogen supplied to an internal combustion engine

Recent research works have focused on analyzing the performance of the hydrogen-fueled internal combustion engines. In this short communication, we present the results of using hydrogen-enriched gasoline in an internal combustion engine without modifying the fuel control algorithm. For this purpose, two different analyses are presented and experimentally validated. In the first analysis, the internal combustion engine is fueled with hydrogen-enriched gasoline using the engine electronic control unit. On the other hand, the second analysis uses this fuel (gasoline + H2), and a control scheme based on model predictive control with constraints considering the hydrogen ratio. The experimental results showed that by using the engine electronic control unit, the gasoline consumption increases by 7.18%. And using the model predictive control, gasoline consumption can be reduced by 2.2%.

Comparison of model predictive controller and optimized min-max algorithm for turbofan engine fuel control

Min-max selector structure is traditionally used as the industrial control architecture of commercial turbofan engines. However, recent studies indicate that this structure with linear compensators suffers from lack of safety guarantee in fast demands. On the other hand, model predictive control (MPC) technique, which incorporates input/output constraints in its optimization process, has the potential to fulfill the control requirements of an aircraft engine. In this paper, a practical approach is performed for design and optimization of the turbofan engine controller through a comparative study where all control modes and requirements have been taken into account simultaneously. For this purpose, a thermodynamic nonlinear model is firstly developed for the turbofan engine. The linear regulators of minmax structure are then optimized via genetic algorithm (GA). The MPC technique is formulated based on the proper discrete-time linearized state-space models at desired operating points with real-time optimization, in which the MPC tuning horizons are obtained through GA optimization procedure. The both controllers are implemented on appropriate hardware taking the real-time aspects into account. Finally, a hardware in the loop (HIL) platform is developed for the turbofan engine electronic control unit (ECU) testing. The software and HIL simulation results confirm that MPC improves the response time of the system in comparison with min-max algorithm and guarantees the engine limit protection. This study demonstrates competitive advantages of MPC in terms of limit protection assurance and fast response, despite more computational burden.

Application of rule‐based expert systems in hardware‐in‐the‐loop simulation case study: Software and performance validation of an engine electronic control unit

AbstractInnovative techniques to validate software are needed to reduce cost and increase software quality.This research aims to check if two rule‐based expert systems (EXs) combined with dynamic‐link libraries (dlls) perform better than other techniques widely employed in the automotive sector when validating the engine electronic control unit (ECU) software by using a hardware‐in‐the‐loop (HIL) simulation.To perform this research, 15 software modules (SMs) of different complexities were chosen to be validated in an HIL simulation by using different techniques such as the manual execution, the tester‐in‐the‐loop, the model‐based testing, a rule‐based EX, and the combination of two EXs to establish the code and functional coverage, the productivity gain, the number of bugs found, potential limitations of each technique, and the success rate of the HIL simulation. The test cases used are described in‐depth in Section 2.The enhancement, which dlls and EXs offer, depends on the number of states in the functional model used in the EXs and the number of subintervals in which the SM inputs can be divided. A range between 6 and 16 more bugs can be detected when using two EXs. The HIL enhancement can reach 6%, 16.8%, and 18% depending on the SM complexity.

Design and implementation of MPC for turbofan engine control system

The objective of an aircraft engine control system is to provide required thrust as well as protection against the physical and operational limits. Model predictive control (MPC) technique is an attractive approach that incorporates input/output constraints in its optimization process to fulfill the control requirements of an aircraft engine. However, due to heavy computational burden of MPC, the real-time implementation of this algorithm is challenging and selection of MPC design parameters is crucial. This paper presents the design and hardware implementation of MPC algorithm as well as its HIL testing for turbofan engine control. In addition, a feedback correction technique is employed to compensate the effect of plant-model mismatch. For this purpose, a thermodynamic nonlinear engine model is firstly developed. A multivariable model predictive controller is then designed based on a discrete-time linearized state-space model where the horizons are obtained through a genetic algorithm optimization procedure. Moreover, this controller is implemented on an appropriate hardware taking the real-time aspects into account. Finally, an HIL platform is developed for testing the turbofan engine electronic control unit (ECU). For this purpose, a multi-level throttle command is applied to the ECU and the performance of the engine is evaluated. The results indicate that the controller satisfies all the engine constraints, and confirm the successful software and hardware implementation of the control algorithm in real-time.

Volumetric efficiency estimation based on neural networks to reduce the experimental effort in engine base calibration

Over the last decades, internal combustion engines have undergone a continuous evolution to achieve better performance, lower pollutant emissions and reduced fuel consumption. This evolution involved changes in the engine architecture needed to perform advanced management strategies. Therefore, Variable Valve Actuation, Exhaust Gas Recirculation, Gasoline Direct Injection, turbocharging and powertrain hybridization have widely equipped modern internal combustion engines. However, the effective management of a such complex system is due to the contemporaneous development of the on-board Engine electronic Control Unit. In fact, the additional degrees of freedom available for the engine regulation highly increased the complexity of engine control and management, resulting in a very expensive and long calibration process. For this reason, this study proposes an effective methodology based on the use of Neural Networks to overcome some critical issues concerning the calibration of engine control parameters. NN are adopted to provide a detailed engine data sheet starting from a reduced number of experimental data. To verify the potential of the proposed methodology, this detailed data set is subsequently used as input to a specific Computer Aided Calibration algorithm developed by the authors and the achievable calibration performance are evaluated. In particular, the calibration performance was assessed with reference to a specific ECU function in this paper. The research clearly demonstrates the effectiveness of the proposed approach since the calibration performance falls within acceptable limits even after a 60% cut of the experimental data usually acquired for calibration purposes, highlighting how the use of neural networks can allow a significant reduction of the experimental effort along with its related times and costs.

Estimation of farm tractor performance as a function of time efficiency during ploughing in fields of different sizes

Tractors have to perform various agricultural tasks in fields of different sizes. While working, tractors consume large amounts of fuel and contribute considerably to pollution of the environment. This study provides a method for the estimation and comparison of tractor performance when ploughing is carried out in fields of different lengths. Tractor performance as a function of time efficiency is estimated during tests with a 160 kW Massey Ferguson MF6499 tractor performing soil ploughing operations. For characterisation of different field sizes, a time efficiency factor was introduced, which was expressed as the ratio between the duration of ploughing operation and the duration of the whole ploughing process including the non-working time. Ploughing of fields with lengths of 200, 300, 500, 750, and 1000 m resulted in time efficiency values of 0.56, 0.67, 0.78, 0.84, and 0.88, respectively. This study analysed the tractor productivity, power utilisation efficiency, fuel consumption, and CO2 and CO emissions for five fields. These indicators were calculated using the tractor engine Electronic Control Unit (ECU) ‘Load Profile’ database. Regression analyses demonstrated that the tractor performance indicators perfectly correlate with the time efficiency factor according to either a linear or exponential equation model. It should be noted that the suggested method allows for better estimation and comparison of tractor performance indicators when ploughing is performed in fields with different width-to-length ratios.

Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter

The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (Δ P) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A diesel oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO2and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the Δ P estimator closely simulated the measured total Δ P and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.

A Backlash Compensator for Drivability Improvement Via Real-Time Model Predictive Control

Nonlinear dynamics in the transmission and drive shafts of automotive powertrains, such as backlash, induce significant torque fluctuations at the wheels during tip-in and tip-out transients, deteriorating drivability. Several strategies are currently present in production vehicles to mitigate those effects. However, most of them are based on open-loop filtering of the driver torque demand, leading to sluggish acceleration performance. To improve the torque management algorithms for drivability and customer acceptability, the powertrain controller must be able to compensate for the wheel torque fluctuations without penalizing the vehicle response. This paper presents a novel backlash compensator for automotive drivetrain, realized via real-time model predictive control (MPC). Starting from a high-fidelity driveline model, the MPC-based compensator is designed to mitigate the drive shaft torque fluctuations by modifying the nominal spark timing during a backlash traverse event. Experimental tests were conducted with the compensator integrated into the engine electronic control unit (ECU) of a production passenger vehicle. Tip-in transients at low-gear conditions were considered to verify the ability of the compensator to reduce the torque overshoot when backlash crossing occurs.

Hypervisor for consolidating real-time automotive control units: Its procedure, implications and hidden pitfalls

Abstract The future of automotive industry has a major focus on computing innovations such as autonomous driving, connectivity and mobility. These complex innovations integrate hardware and software components of different automotive safety integrity levels. Although standards like AUTOSAR provides flexibility in integrating different software components, it compromises on isolation from software interference. Virtualization is a technique, used for several decades in server domains, ensuring integration of heterogeneous subsystems without interference. Until recent years, inheriting virtualization onto real-time systems has been avoided due to its overhead and impact in-terms of timing behavior. With the usage of multi- and many-cores in the automotive industry, virtualization is gaining more importance to handle the upcoming innovations with incredible increased complexity and the needed computation power. This paper describes the technique for bringing virtualization into an automotive state-of-the-art multicore controller, which does not support virtualization. We have used two automotive state-of-the-art heterogeneous applications: engine electronic control unit and vehicle control unit, and a prototype version of hypervisor from ETAS-GmbH. Important aspects in virtualization like system-startup, handling of interrupts and traps, facilitating input–output access are discussed. Finally, the performance of a virtualized system in terms of core loading, interrupt and task timing parameters are evaluated and compared.

Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines

Over the past 20 years, with the increase in the complexity of engines, and the combinatorial explosion of engine variables space, the engine calibration process has become more complex, costly, and time consuming. As a result, an efficient and economic approach is desired. For this purpose, many engine calibration methods are under development in original equipment manufacturers and universities. The state-of-the-art model-based steady-state design of experiments (DOE) technique is mature and is used widely. However, it is very difficult to further reduce the measurement time. Additionally, the increasingly high requirements of engine model accuracy and robust testing process with high data quality by high-quality testing facility also constrain the further development of model-based DOE engine calibration. This paper introduces a new computational intelligence approach to calibrate internal combustion engine without the need for an engine model. The strength Pareto evolutionary algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 gasoline direct injection (GDI) engine test bench, a simulink real-time based embedded system was developed and implemented to engine electronic control unit (ECU) through rapid control prototyping (RCP) and external ECU bypass technology. Experimental validations prove that the developed engine calibration approach is capable of automatically finding the optimal engine variable set which can provide the best fuel consumption and particulate matter (PM) emissions, with good accuracy and high efficiency. The introduced engine calibration approach does not rely on either the engine model or the massive test bench experimental data. It has great potential to improve the engine calibration process for industries.

Effects of hydrogen addition to the intake air on performance and emissions of common rail diesel engine

Abstract This paper reports an investigation of the engine performance and emissions of an engine burning hydrogen-enriched diesel fuel. Hydrogen was chosen as the secondary fuel for its renewability in the long term and overall sustainability as a fuel. A four-cylinder, four-stroke, 1.461-L diesel engine with a common rail injection system was used for our tests. The cylinder pressures, rate of heat releases (ROHRs), brake specific energy consumptions (BSECs), brake thermal efficiencies (BTEs), exhaust gas temperatures (EGTs), and exhaust emissions were investigated under 50 Nm, 75 Nm and 100 Nm engine loads at 1750 rpm. Diesel fuel was injected directly to combustion chamber while hydrogen was continuously inducted into the intake manifold at two different flow rates while the original settings of the engine's electronic control unit were preserved. Results showed that hydrogen enrichment decreased HC and CO2 emissions and ROHRs, and increased EGTs and cylinder pressures under all conditions we tested. NOx emissions decreased with a 20 lpm flow rate and increased with a 40 lpm flow rate. Hydrogen also had a positive effect on BSEC and BTE, especially with low engine loads. Overall, hydrogen enrichment increases efficiency and reduces carbon-based emissions, all without major engine modifications.