Engine Control Unit Research Articles

Role of Cybersecurity in Engine Control Systems: Safeguarding the Heart of Modern Trucks

This article outlines the importance of cybersecurity for modern engine control systems. Much emphasis is placed on how fast-paced the technologies in the automotive industry are and the several risks involved with their use. Due to high connectivity and heavy dependency on software, serious vulnerabilities are present in trucks' engine control units (ECUs) that pose a severe threat regarding cybersecurity in the automotive industry. The article discusses how engine control systems have been developing over time, enumerates the most common cyber-attacks they face, and points out the consequences that may influence vehicle safety and performance, as well as environmental compliance. Attention is given to regulatory laws, industry standards, and strategies regarding how to handle these challenges. The article also discusses how artificial intelligence influences ECU security and the challenges and opportunities arising in this fast-changing sector. Overall, the article provides a broad overview of the relationship between cybersecurity and ECUs, underlining the fact that strong security is urgently needed to provide safety, reliability, and trustworthiness to modern vehicles.

Advanced Methods for Monitoring and Fault Diagnosis of Control Loops in Common Rail Systems

Common rail systems are a key component of modern diesel engines and highly increase their performance. During their working lifetime, there could be critical damages or failures related to aging, like backlash or friction, or out-of-spec operating conditions, like low-quality fuel with, e.g., the presence of water or particles or a high percentage of biodiesel. In this work, suitable data-driven methods are adopted to develop an automatic procedure to monitor, diagnose, and estimate some types of faults in common rail systems. In particular, the pressure control loop operating within the engine control unit is investigated; the system is described using a Hammerstein model composed of a nonlinear model for the control valve behavior and an extended linear model for the process dynamics, which also accounts for the presence of external disturbances. Three different sources of oscillations can be successfully detected and quantified: valve stiction, aggressive controller tuning, and external disturbance. Selected case studies are used to demonstrate the effectiveness of the developed methodology.

Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach

Nowadays, engine experimental research represents a very expensive field within the automotive industry, but it remains fundamental for engine and vehicle development. The present work aims to investigate a novel approach for engine control system calibration, by adopting machine learning techniques to model physical parameters of the engine starting from experimental data measured at the test bench. The main goal is to create a methodology which accelerates the calibration process without losing accuracy. A model that estimates air mass flow is created by adopting either a tree ensemble model or an artificial neural network trained on a small dataset, which was previously acquired at the test bench using a random calibration of the volumetric efficiency map. The model’s performance is first validated on a larger, random dataset. Then, the volumetric efficiency calculated from the air mass flow model estimation is used to calibrate the transfer function of the Engine Control Unit. Finally, the sensitivity of the model error correlated with the number of data points acquired is used in order to determine the best practice for a Design Of Experiment, which minimizes data acquisition. The methodology proposed can lead to reduced time and costs of the whole calibration process of the engine, without losing accuracy. The analysis was conducted on the entire vehicle, which is crucial for drivability, especially in motorcycles since they are highly sensitive to air-to-fuel ratio adjustments. This work demonstrates that machine learning models can be adopted for the fine-tuning of the calibration process, which is normally performed manually.

An experimental study of knock analysis of HCNG fueled SI engine by different methods and prediction of knock intensity by particle swarm optimization-support vector machine

Hydrogen and its careers have a lot of potential in transportation industry due to lesser emissions and better performance. Performance of hydrogen enriched compressed natural gas (HCNG) fueled engine is limited by knock. The purpose of this study is to evaluate and predict knock at different operating conditions by varying (0 %–40 %) hydrogen amount in HCNG, by varying (25 %–100 %) load on engine and by varying (700 rpm–1700 rpm) speed of engine. Knock is evaluated by knock ratio, knock intensity, exhaust temperature and in-cylinder heat transfer rate. Quasi-dimensional combustion model (QDCM) MATLAB program is used to calculate in-cylinder pressure theoretically. By increasing (0 %–40 %) hydrogen in HCNG 32.7 %, 7.6 %, 33.5 %, 21.5 % & 6.4 % increment observed in, in-cylinder pressure, knock intensity, knock ratio, in-cylinder heat transfer rate and exhaust temperature respectively. By increasing (25 %–100 %) load on engine 75.8 %, 69.8 %, 88.2 %, 77.8 % & 46.7 % increment observed in, in-cylinder pressure, knock intensity, knock ratio, in-cylinder heat transfer rate & exhaust temperature respectively. By increasing speed (1100–1700) rpm of engine aforementioned parameters decreases by 12.19 %, 38.9 %, 36.06 %, 47.2 % & 14.1 % respectively. Knock intensity is predicted by particle swarm optimization-support vector machine (PSO-SVM) algorithm effectively. To minimize the prediction error, 31 combination of different (1–5) inputs applied to predict knock intensity and found combination of 5 input variable is 67.2 % & 71.9 % more accurate than 1 input variable in-terms of mean squared error and mean absolute error respectively. Findings of this study can be used to train electronic control unit of engine and in the development of HCNG fueled engine.

Characterizing hydrogen-diesel dual-fuel performance and emissions in a commercial heavy-duty diesel truck

This study investigates hydrogen (H2) as a supplementary fuel in heavy-duty diesel engines using pre-manifold injection. A H2-diesel dual-fuel (H2DF) system was implemented on a commercial class-8 heavy-duty diesel truck without modifying the original diesel injection system and engine control unit (ECU). Tests were conducted on a chassis dynamometer at engine speeds between 1000 and 1400 rpm with driver-demanded torques from 10 to 75%. The hydrogen energy fraction (HEF) was strategically controlled in the range between 10 and 30%. Overall, CO2 reduction (comparable to the HEF level) was achieved with similar brake-specific energy consumption (BSEC) at all loads and speeds. To maintain the same shaft torque, the driver-demanded torque was reduced in H2DF operation, which resulted in a lower boost pressure. At higher loads, engine-out BSNOx slightly decreased, while BSCO and black carbon (BC) increased significantly due to lower oxygen concentration resulting from the lower boost pressure. At lower loads, engine-out BSCO and BSBC decreased moderately, while NO2/NO ratio increased substantially in H2DF operation. Deliberate air path and diesel injection control are expected to enable higher HEF and GHG reductions.

Modeling of an Adaptive HHO Gas Controller Based on Fuzzy Logic and Polynomial Function Controls to Improve Engine Torque of Gasoline Engine

Typical hydrogen-hydrogen-oxygen gas (HHO) usage to improve gasoline engine performance refers to the fixed HHO flow rate method, providing 0.25‒0.5 liters/minute of HHO for every 1000 cc engine size. However, the arising hypothesis expresses that the fixed HHO flow rate method does not optimally improve engine torque for various loads. The research objective is to propose an adaptive HHO gas controller that manages the HHO generator to produce an appropriate HHO flow rate for engine operation by adapting to load and engine speed variations. Hence, the engine torque improvement optimally occurs for various loads. The adaptive HHO controller combines fuzzy logic and polynomial function controls involving real-time engine data, such as mass airflow (MAF), air-fuel ratio (AFR), and the commanded AFR from the engine control unit (ECU), whose values vary with load and engine speed. A system simulation based on Matlab-Simulink investigates engine performance improvement due to the controller. The results show that the adaptive HHO flow rate due to the proposed adaptive HHO controller improves engine torque during small, medium, and big-loaded engine operations, respectively, by 1.5%‒4.7%, 6.8%‒26.8%, and 21.1%‒72.8% depending on engine speed 2500 rpm‒4000 rpm) and the commanded AFR (12.6‒15.4). Conversely, under the same condition, the fixed HHO flow rate with 0.75 liters/minute HHO for a 1500 cc engine, used for comparison, improves the engine torque by 0%‒0.6%, 0.3%‒14.2%, and 9.3%‒50.1%, respectively. The data show that the adaptive HHO controller provides better improvement. Moreover, the adaptive HHO controller improves engine thermal efficiency and reduces AFR error against the commanded AFR.

Optimization of NOX emissions of a CRDI DIESEL engine using CMA-ES method

Engine calibration is the tuning of embedded parameters in the engine control unit (ECU) software to improve vehicle characteristics and meet legal requirements. Due to the stricter emission limits and rising customer expectations, current ECU software may include variables up to 30,000, which require very much time for engine calibration development. For this reason, automotive manufacturers continuously develop mathematical-based optimization methods to find optimum operating conditions for the engines. This study aimed to develop an online optimization algorithm to conduct automated dynamometer tests in the calibration development process. A modified covariance matrix adaptation (CMA) algorithm, which is an evolutionary strategy (ES) method belonging to meta-heuristic optimization, was integrated with an automation system for online calibration optimization. Some CMA method parameters such as step size and damping factor were initially revised to achieve the method to function efficiently in online engine calibration. Optimization was conducted at three different operating points of a 2-liter common rail direct injection (CRDI) diesel engine, where NOx emission mainly impacts the New European Driving Cycle (NEDC) results. The main injection timing, rail pressure, pilot injection quantity and timing, manifold pressure, and mass air flow were controlled in the optimization process. Optimization targets were determined according to the NOx-PM Pareto curve for each operating point. Covariance matrix adaptation was used to generate Pareto curves. Sixty-five measurements were taken for each operating point in the optimization process. Once optimization targets were determined, optimization occurred at each operating point. A total NOx emission reduction of 3.8% was obtained in the NEDC test, while fuel consumption and PM remained almost the same at steady-state operating points. The modified CMA-ES algorithm is expected to be an efficient method for online calibration optimization.

Arduino-Based Automotive Diagnostic Tester

Abstract The work presented introduces a system that encompasses general notions about the dashboard computer and Arduino platform, used to create an automotive diagnostic tool through the OBDII port. This involves establishing a connection between the Arduino platform, a Bluetooth module, and a Bluetooth adapter that communicates with the Engine Control Unit (ECU), transmitting information displayed on an LCD screen. To highlight the system’s performance, a comparison of results was conducted with an authorized automotive tester from an auto service, using two different vehicle models.

Greenhouse gas reduction in a medium-duty compression ignition engine with optimization for B20

Soy-based biodiesel can reduce well-to-wheels greenhouse gas (GHG) emissions per unit energy (i.e., gCO2e/MJ) by 66%–72% as compared to the petroleum-based diesel fuel with currently adopted agricultural and industrial practices. Biodiesel can reduce particulate matter and carbon monoxide emissions with a manageable degree of increase in NOx emissions. From the perspective of GHG emissions reduction per unit travelling distance (i.e., gCO2e/mile), the application of B20 in compression ignition engines without the adjustment in engine control unit (ECU) settings will not extract the best carbon emissions reduction that B20 could achieve. Optimizing the engine control settings permits re-calibration to achieve the maximum brake fuel conversion efficiency (BFE) based on comprehensive understanding on the impact of both “fuel” and “ECU calibration” on BFE and other criteria pollutant emissions. The maximum GHG emissions reduction with B20 application is experimentally measured with the optimized ECU calibration, thus providing the understanding of the combined impact of biodiesel fuel and calibrations on engine performance and emissions. Six steady operating modes were considered, that can be combined to estimate the US federal test procedure BFE and emissions over the Federal Test Protocol (FTP) 75 cycle. Combined with the weight factors to simulate the EPA FTP 75 cycle from these 6 “mini-map” test points, 0.53% improvement in the energy requirement per unit traveling distance (i.e., MJ/mile) is achieved for B20 with the final ECU calibration, in addition to the degree of GHG emissions reduction on a “gCO2e/MJ” basis from the use of B20 blend of soy biodiesel of ∼12.5% reduction in gCO2e/MJ, for a total GHG emissions reduction of 13%.

Effect of Hydroxy Gas Addition in Gasoline-ethanol Fuel Mixture on Performance of Spark Ignition Engine

Fossil fuel continues as primary energy source for the transportation industry, particularly for the fuel used in spark ignition (SI) engines. The use of biofuels such as bioethanol is a solution to the reliance on fossil fuels to reduce green-house gas emissions in the atmosphere and improve environmental air quality. However, the use of bioethanol as a fuel for the SI engine has limitations, such as a reduction in power produced as compared to pure gasoline due to the low energy input contained by bioethanol compared to gasoline. The objective of this study is to improve the energy conversion efficiency of the SI engine by adding Hydroxy (HHO) from water electrolysis to the gasoline-bioethanol mixture, allowing for increasing application of bioethanol at higher concentrations as an additive in future. The test was performed on a 155 cc SI engine equipped with an Engine Control Unit (ECU) to control the fuel injection. The pressure, temperature, oxygen, and throttle position sensors located on the engine and connected to a NI-MyRIO microcontroller. The injection duration of HHO gas based on the duration of gasoline fuel injection, both of fuel are injected through each injector at the intake manifold. The result shows the characteristics of HHO gas improved the disadvantages of biofuels, which have a lower heating value and higher flame speed of bioethanol and gasoline fuel. The presence of HHO gas into the bioethanol-gasoline blend affected to increase the produced power, torque and thermal efficiency of the SI engine. An increase in thermal efficiency at 9.16% obtained at concentration of 20% bioethanol and HHO.

Automatic smooth map generation of internal combustion engines via local-global model based calibration technique

The addition of new sensors and actuators to the engine, to reduce fuel consumption and emissions besides improving the engine operation, complicates the control commands stored in the engine control unit (ECU). Substitution of mechanical actuators with electronic ones increases the engine’s degrees of freedom and the number of control parameters, which results in the increased engine calibration time and cost. The aim of this paper is to take advantage of optimization techniques to achieve optimal values of control parameters in a fast and automated way. In this regard, it requires replacing the real engine with the virtual model and implementing the model-based calibration by coupling the virtual engine model with optimization algorithms. In this study, deep neural network (DNN) modeling and genetic algorithm (GA, NSGA-II) optimization are used for model-based calibration. The effect of all input control parameters, including ignition angle, continuously variable valve timing, etc., on all output control parameters including, brake-specific fuel consumption, emissions level, knock limit, combustion stability, etc., are investigated simultaneously by a valid global model, which is a remarkable achievement in the model-based calibration. Dynamic lag of some actuators delays the execution of control commands sent from ECU. To avoid abrupt variations in the actuators values, smoothness of the engine maps is considered in the calibration process. To reduce fuel consumption, decrease emission levels and attain smooth maps, the calibration of control parameters is performed by local-multi-objective optimization and global-single-objective optimization. Local-global model-based calibration presented in this study reduces 3.7% of the brake-specific fuel consumption and 7%–10% of emissions level at breakpoints of the engine map compared to manual calibration. In addition, the calibration time and costs while producing better engine performance can be reduced by automating the calibration process. Finally, calibrated maps are stored as a lookup table (LUT) in ECU. Generating an optimal lookup table involves the pre-calculation of several points that cover the calculation domain and allow the interpolation for other points. Selecting the optimal points for exact calculation is of great importance in the size and accuracy of LUT. In this study, an optimization tool is also presented to generate accurate and efficient LUT.

The evolution of embedded systems in automotive industry: A global review

The evolution of embedded systems in automotive industry: A global review

Multi-modal LSTM network for anomaly prediction in piston engine aircraft

An aircraft is a highly intricate system that features numerous subsystems, assemblies, and individual components for which regular maintenance is inevitable. The operational efficiency of an aircraft can be maximised, and its maintenance needs can be reduced using an effective yet automatic AI-based health monitoring systems which are more efficient as compared to designing and constructing expensive and harder to operate engine testbeds. It has been observed that aircraft engine anomalies such as undergoing flameouts can occur due to the rapid change in the temperature of the engine. Engine oil temperature and cylinder head temperature, two measures connected to this issue, might be affected differently depending on flight modes and operational conditions which in turn hamper AI-based algorithms to yield accurate prediction on engine failures. In general, previous studies lack comprehensive analysis on anomaly prediction in piston engine aircraft using modern machine learning solutions. Furthermore, abrupt variation in aircraft sensors' data and noise result in either overfitting or unfavourable performance by such techniques. This work aims at studying conventional machine learning and deep learning models to foretell the possibility of engine flameout using engine oil and cylinder head temperatures of a widely used Textron Lycoming IO-540 six-cylinder piston engine. This is achieved through pre-processing the data extracted from the aircraft's real-time flight data recorder followed by prediction using specially designed multi-modal regularised Long Short-Term Memory network to enhance generalisation and avoid overfitting on highly variable data. The proposed architecture yields improved results with root mean square error of 0.55 and 3.20 on cylinder head and engine oil temperatures respectively averaged over three case studies of five different flights. These scores are significantly better i.e., up to 84% as compared to other popular machine learning predictive approaches including Random Forest, Decision Tree Regression, Artificial Neural Networks and vanilla Long Short-Term Memory networks. Through performance evaluation, it can be established that the proposed system is capable of predicting engine flameout 2 minutes ahead and is suitable for integration with the software system of aircraft's engine control unit.

Open Source Embedded System Approach for Decreasing Carbon Footprint of Oldtimers

In the ever-evolving landscape of automotive engineering, the importance of efficient and adaptable engine control is paramount. Open-source Engine Control Unit (OS-ECU) emerges as an innovative, presenting a blend of intricate electronic design and adaptability. This study delves deep into the very heart of OS-ECU, unraveling its printed circuit board (PCB) layout with Through Hole Technology (THT), electronic components, and the harmonious interplay between them. By meticulously exploring these aspects, we aim to understand not only the electronic foundation of OS-ECU but also its practical implications when integrated into a vehicle. Subsequently, the research touches upon the synergy between the electronics Engine Control Unit (ECU) and the accompanying software, shedding light on how they cohesively drive engine performance. Our findings emphasize the transformative potential of open-source electronic solutions like OS-ECU in reshaping and enhancing traditional automotive systems.

Кратковременная топливная коррекция бензинового двигателя по напряжению датчика кислорода в процессе эксплуатации автомобиля

The object of the study was fuel trim of the air-fuel mixture, in particular, short-term fuel trim by voltage change of the oxygen sensor installed before the catalytic converter during vehicle operation. Experimental studies were on the Bishkek – Osh (Kyrgyz Republic) highway at an altitude of 750–3200 m above sea level. It is characterized by flat, mountainous and high mountainous vehicle operating conditions. The experimental studies results were used to graph the dependence of short-term fuel trim on the change of oxygen sensor voltage during vehicle operation. The values of fuel trim parameters can be used to assess the state of the air-fuel mixture, as well as to make a conclusion about the technical condition of sensors, systems and mechanisms of the engine and elements of the car exhaust system. In addition, in normal operation of the engine, when the fluctuation of fuel correction indicators is ±10 %, fuel-economic and environmental indicators will be in fit. It should also be recognized that the modern automobile engines have a system of various sensors and electronic engine control unit, but they have their certain disadvantages in ensuring the stoichiometric composition of the air-fuel mixture (λ = 1).

Data mining techniques for estimating instantaneous NOX emissions from Euro 6 diesel passenger cars

Nitrogen oxides (NOX) emissions that are caused by road traffic diesel engines affects public health. The existing instantaneous emissions models are often imprecise due to the lack of knowledge of highly non-linear processes behind real-world emissions and they do not include meteorological and driving volatility variables. This paper applied data mining techniques based on the Cross Industry Standard Process for Data Mining (CRISP-DM) method to a dataset of four diesel Euro 6 passenger cars tested in real-world driving conditions to: a) model stabilised hot NOX emissions based on kinematic (speed), internal engine (engine coolant temperature, engine load, engine speed, intake air temperature, manifold absolute pressure and mass air flow), meteorological (humidity) and driving volatility (acceleration and vehicular jerk); b) compare the performance of different machine learning (ML) techniques in predicting NOX emission rates, namely: Artificial Neural Networks (ANN), Random Forest (RF), and Gradient-Boosted Trees (GBT). The model that utilizes a set of detailed variables, particularly engine coolant temperature, engine load, engine speed, intake air temperature, humidity, acceleration and vehicular jerk, and using ANN technique was better able to deal with variability in emission data than models based on a single set of these variables. It was also found that models produced high Root Mean Square Error due to their inability in predicting high peaks in measured emission data. The presented models rely on fast inference times and can therefore be deployed for engine control units to inform drivers about their NOX emissions during driving.

Consumption Volume Comparison of Fuel for the 2009 Toyota Altis 1.6 Gasoline Engine Car

This research study aimed to investigate the fuel consumption rates of different types of fuel for a 2009 Toyota Altis 1.6 car. The objective was to determine the fuel consumption rates of various types of gasoline and compare them. Four different fuel types were used in the experiments: gasoline 91, gasoline 95, gasoline E20, and gasoline E85. Data was collected from the car's Engine Control Unit (ECU) using a G-scan 2 device during test drives covering a distance of 8 kilometers in urban driving conditions. Three main factors were considered during the experiments: consistent single-driver performance throughout the tests, controlled additional equipment in the vehicle, and varying environmental conditions. On average, the research found that gasoline 91 had the highest fuel consumption rate, averaging 0.73 liters, indicating relatively high fuel wastage during driving. In contrast, gasoline 95 had the lowest average fuel consumption rate at 0.54 liters for the same 8-kilometer distance. This is attributed to the fact that gasoline 95 has a higher-octane rating of 95%, making it more suitable for this type of engine. Additionally, gasoline E20 had an average fuel consumption rate of 0.64 liters, while gasoline E85 had an average fuel consumption rate of 0.66 liters over the same test distance. Gasoline 95 consistently demonstrated the lowest fuel consumption among the four fuel types. Furthermore, the research also calculated the energy content of each fuel type. Gasoline 91 was found to have an energy content of 273,755.2 kJ, gasoline 95 had an energy content of 271,857.6 kJ, both of which were relatively close due to their main component being gasoline. On the other hand, E20 had an energy content of 48,428.94 kJ, and E85 had an energy content of 37,426.66 kJ, both lower due to their higher ethanol content. In conclusion, among the four tested fuel types, gasoline 95 exhibited the lowest fuel consumption rate for the 2009 Toyota Altis 1.6. Additionally, the energy content of the fuels showed that gasoline 91 and gasoline 95 had similar values, primarily composed of gasoline, while E20 and E85 had lower energy contents due to their higher ethanol content.

An Integrated Engine Control Unit for a Spark Ignited Internal Combustion Engine

A stepping valve system has been proposed before and proved to be feasible. The stepping valve move perpendicular to the piston, thereby avoiding piston-valve collisions associated with interference engines. In this study, the proposed Stepping Valve Control Unit, SVCU, was integrated with the conventional Engine Control Unit, ECU, of a simple SI engine. The resulting Integrated Engine Control Unit, IECU, as it is called, simplified the design of the ECU. The integration involved combining the basic fuel injector and sparking controls with the SVCU. The camshaft and crankshaft sensors were replaced by a Piston Direction Sensor, PDS. The PDS is an optical sensor, which works in conjunction with a gray coded trigger wheel to relay information to the IECU. The IECU was simulated using Quartus II design software. The hardware implementation of the IECU was done using EPM7128SL FPGA contained within the UP1 Altera Development Board. The behavior of the IECU was expected.

Virtual Multi-Criterial Calibration of Operating Strategies for Hybrid-Electric Powertrains

In hybrid vehicle development, the operating strategy has a decisive role in meeting the development goals, such as compliance with emission standards and high energy efficiency. A considerable number of interactions and cross-influences on other topics, such as emissions, on-board diagnostics, or drivability, must be considered during the calibration process. In this context, the given time constraints pose further challenges. To overcome these, approaches for virtualization of the calibration process are an effective measure. For this purpose, in the current study, a real engine control unit is embedded into a virtual simulation environment on so-called hardware-in-the-loop (HiL) testbenches, which allow virtual calibration and validation of the complete target vehicle. In this context, the paper presents a novel method for virtual calibration of operating strategies for hybrid-electric propulsion systems. This includes an innovative multi-criterial approach that considers the requirements of several development tasks, such as emission and OBD calibration. Measurement data for this optimization is generated on a HiL testbench setup tailored for the described methodology, including both the electrical setup and the simulation environment. To validate the selection of modeling approaches and the parametrization, the simulation environment is operated in open loop. The results of the open loop validation show promising behavior regarding the proposed use case. Finally, the presented methodology is evaluated regarding time and cost savings compared to a conventional approach.

Knock probability determination employing convolutional neural network and IGTD algorithm

This study presents a novel method based on the convolutional neural network to evaluate knock probability. In this way, lots of data sets are extracted from the real driving conditions of a turbocharged gasoline vehicle. The trained network shows 92 % accuracy in knock detection and 87 % accuracy in normal combustion recognition. This trained network is then used to forecast the knocking probability of the vehicle in different operating conditions. The results revealed that the exhaust gas temperature sensor considerably affects the knock recognition precision by 3 %. Most of the network errors in knock probability determination would be associated with the full load-high speed region and the network errors in normal combustion detection would be related to the low-end torque zone. It is also found that the knock probability decreases with the vehicle's speed increase and its probability at the constant vehicle speed would be higher at low engine rotational speed. This new approach can predict engine knock zones for different vehicle parameters and can be implemented by the engine control unit to suppress knock before happening in an active manner rather than the current passive method.