Electronic Control Unit Research Articles

IDS-DEC: A novel intrusion detection for CAN bus traffic based on deep embedded clustering

As the automotive industry advances towards greater automation, the proliferation of electronic control units (ECUs) has led to a substantial increase in the connectivity of in-vehicle networks with the external environment. However, the widely used Controller Area Network (CAN), which serves as the standard for in-vehicle networks, lacks robust security features, such as authentication or encrypted information transmission. This poses a significant challenge to the security of these networks. Despite the availability of powerful intrusion detection methods based on machine learning and deep learning, there are notable limitations in terms of stability and accuracy in the absence of a supervised learning process with labeled data. To address this issue, this paper introduces a novel in-vehicle intrusion detection system, termed IDS-DEC. This system combines a spatiotemporal self-coder employing LSTM and CNN (LCAE) with an entropy-based deep embedding clustering. Specifically, our approach involves encoding in-vehicle network traffic into windowed messages using a stream builder, designed to adapt to high-frequency traffic. These messages are then fed into the LCAE to extract a low-dimensional nonlinear spatiotemporal mapping from the initially high-dimensional data. The resulting low-dimensional mapping is subjected to a dual constraint in conjunction with our entropy-based pure deep embedding clustering module. This creates a bidirectional learning objective, addressing the optimization problem and facilitating an end-to-end training pattern for our model to adapt to diverse attack environments. The effectiveness of IDS-DEC is validated using both the benchmark Car Hacking dataset and the Car Hacking-Attack & Defense Challenge dataset. Experimental results demonstrate the model's high detection accuracy across various attacks, stabilizing at approximately 99% accuracy with a 0.5% false alarm rate. The F1 score also stabilizes at around 99%. In comparison with unsupervised methods based on deep stream clustering, LSTM-based self-encoder, and classification-based methods, IDS-DEC exhibits significant improvements across all performance metrics.

TRAFFIC ANOMALY DETECTION IN VEHICLE BUS BY RECURRENT LSTM NEURAL NETWORK

Modern high-end cars have many electronic control units for driving assistance that combine huge amounts of data about the functioning of car components. A significant part of these vehicles use a controller area network for communication between electronic units. Controller area network is a simple and reliable network protocol that due to its simplicity lacks any security mechanisms for data transmission. The problem of controller area network vulnerability is worsening as constantly growing amounts of data between cars, road infrastructure and the Internet. The traffic of attacks on controller area networks can be treated as abnormal that allows using anomaly detection methods for their recognition. In this work we propose the recurrent long short-term memory encoder-decoder neural network for controller area network attacks detection.

Research on Vehicle Pose Detection Method Based on a Roadside Unit.

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.

Engineering Autonomous Mobility: Wired and Wireless CAN Bus Design Principles

<div>The controller area network (CAN) bus, the prevailing standard for in-vehicle networking (IVN), has been used for more than four decades, despite its simple architecture, to establish communications between electronic control units (ECUs). Weight, maintenance overheads, improved flexibility, and wiring complexity escalate as the quantity of ECUs rises, especially for high-demand autonomous vehicles (AVs). The primary objective of this study is to examine and discuss the significant challenges that arise during the migration from a wired CAN to a wireless CAN (WCAN). Suggested remedies include changing the configuration of the conventional ECU, creating a hidden wireless communication domain for each AV, and developing a plan to counteract the jamming signals. The simulation of the proposed WCAN was done using MATLAB and validated using OPNET analysis. The results showed that the packet loss of the eavesdropping electronic control unit ranged from 63% to 100%. Anti-jamming results show that when packet loss reaches 2% for a continuous period of time of 0.01 sec, the passive channel is automatically activated, ensuring secure data transmission.</div>

Development and field evaluation of a VR/AR‐based remotely controlled system for a two‐wheel paddy transplanter

AbstractOperating a two‐wheel paddy transplanter traditionally poses physical strain and cognitive workload challenges for farm workers, especially during headland turns. This study introduces a virtual reality (VR)/augmented reality (AR)based remote‐control system for a two‐wheel paddy transplanter to resolve these issues. The system replaces manual controls with VR interfaces, integrating gear motors and an electronic control unit. Front and rear‐view cameras provide real‐time field perception on light‐emitting diode screens, displaying path trajectories via an autopilot controller and real‐time kinematic global navigation satellite systems module. Human operators manipulate the machine using a hand‐held remote controller while observing live camera feeds and path navigation trajectories. The study found that forward speed necessitated optimization within manageable limits of 1.75–2.00 km h−1 for walk‐behind types and 2.00–2.25 km h−1 for remote‐controlled systems. While higher speeds enhanced field capacity by 11.67%–12.95%, they also resulted in 0.74%–1.17% lower field efficiency. Additionally, Operators' physiological workload analysis revealed significant differences between walk‐behind and remotely controlled operators. Significant differences in energy expenditure rate (EER) were observed between walk‐behind and remote‐controlled paddy transplanters, with EER values ranging from 8.20 ± 0.80 to 27.67 ± 0.45 kJ min⁻¹ and 7.56 ± 0.55 to 9.72 ± 0.37 kJ min⁻¹, respectively (p < 0.05). Overall, the VR‐based remote‐control system shows promise in enhancing operational efficiency and reducing physical strain in paddy transplanting operations.

Impact Analysis of using Natural Language Processing and Large Language Model on Automated Correction of Systems Engineering Requirements

AbstractThe increasing complexity of Electronic Control Units (ECUs) in the Automotive Industry due to the integration of more sophisticated vehicle features has made the Systems Engineering (SE) application a necessity to define and implement efficient solutions. In this context, requirements emerge as a critical part of the communication between cross‐functional teams. Thus, the more complex systems become, the more requirements are needed to define them. However, lack of information, misalignment and ambiguity on requirements impact the entire development process, resulting in issues later, harder to be fixed. Some studies are being applied to evaluate techniques using Natural Language Processing (NLP) and how it can replace extensive peer reviews, identifying weaknesses in requirements earlier in the process, avoiding wasted time and large financial losses. Normally, NLP is combined with templates such as Easy Approach Requirements to Syntax (EARS), or other rule‐based techniques such as INCOSE's requirements writing best practice rules to define metrics and assess the compliance of the requirements syntax automatically. The focus of this work is to enhance the use of requirements syntax assessment algorithm by combining NLP techniques with Large Language Models (LLMs) to provide automatically corrected requirements.

Evaluating Lean Combustion Cycle Stability and Emissions in Spark Ignition Engines with Gasoline, Ethanol, and Methanol Blends

Abstract Fuels for vehicles account for a large portion of the world’s total energy demand, which in turn leads to increased carbon emissions. Ethanol and methanol are a fuel with a simple carbon chain and OH- bonds. It has similar properties to gasoline, and ethanol can be made from the fermentation of plant carbohydrates, called bioethanol. The advantage of using bioethanol is that it contributes to carbon neutrality. This paper will investigate the use of three manually blended gasoline ethanol and methanol (GEM) fuels in a spark ignition engine to address cycle-to-cycle variation (CCV), knock potential, and emissions with lean blend conditions. In the experiments conducted, the air-fuel ratio was conditioned lean by utilizing an electronic control unit to adjust the injector spray duration. This experiment provides results that there is a potential for mild knocking on the use of alcohol fuel with lean fuel mixture conditions at engine speed 4000 RPM, while at engine speed 6000 RPM and 8000 RPM the use of GEM tends to be stable, but in the CCV results the increase in COV (coefficient of variation) value using GEM fuel tends to be more sloping, especially with the addition of more methanol. Emission results from the use of GEM produce top emission CO2 value obtained by the E5M15 mixture at λ=1.2 and an engine speed of 8000 RPM, with a value of 13.75% and then peak CO2 emissions at a value of λ = 1.2 whereas in the use of pure gasoline peak CO2 is at a value of λ = 1.1.

Advanced Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI

This paper proposes a methodology for the design of electronic control unit (ECU) hardware units with increased performance and reliability. Today's vehicles are equipped with dozens of ECUs that significantly influence the system's efficiency, reliability, performance, and safety. With the increased complexity of control algorithms and the environmental constraints that automotive systems operate, the robustness and efficiency of the ECUs are of utmost importance. In this work, an approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), and artificial intelligence strategies to provide increased redundancy checks and robust control. An additional redundancy is added to the hardware architecture of the ECU to include a parallel hardware unit. The two controlling units operate in parallel. The output of each of them is compared, allowing redundancy checks in the computation of the output variable (oV) of the system. The mathematical model of the ECU depicts the governing equations of the ECU in the form of differential equations, which results in a corresponding state-space configuration. These mathematical models are encoded into the field programmable gate array (FPGA) and processed in hardware, leading to an equivalent software-based implementation. To analyze the performance of both models within the ECU, an artificial neural network (ANN)-based strategy is proposed. The ANN depicts the governing equations of the ECU in the form of differential equations encoded in the form of sigmoidal functions. To analyze the reliability of the control action in the ECU, the temperature of the system is increased, which leads to a random variation of the system parameters. The variability of the ECU parameters leads to a deviation in the computation of the oV and the corresponding control action. The robustness of the control is determined in such conditions. A control law is determined to guarantee proper control action under variations in the governing equations of the system. This control law is represented by a simple algebraic equation, which can then be cast in various control strategies, such as look-up tables or fuzzy logic controllers. Several cases are simulated to assess the performance of the proposed control law for the hardware redundancy scheme, for the ANN-based equivalent software implementation approach, and for the additional fuzzy logic controller. The simulation results are analyzed concerning the requested oV and give insight into the performance and reliability of the proposed dedicated ECU design.

Parameter fine tuning on CRDI engine operated with blends of grape biodiesel and diesel

This study investigates the feasibility of using grape biodiesel from the wine industry as a sustainable and cost-effective alternative to traditional fuels. The research focuses on optimizing key parameters like blend ratio, injection timing, injection pressure, engine load, and exhaust gas recirculation in a Common Rail Direct Injection engine to achieve low emissions without compromising performance. Due to grape biodiesel's higher viscosity, different energy content, and varying ignition delays compared to diesel, precise adjustments are essential for complete combustion and reduced emissions. This study uses response surface methodology and a Central Composite Design matrix to find the best values for a number of parameters in modern CRDI engines that use advanced electronic control units. Through fifty input combinations, the study aims to minimize specific fuel consumption, hydrocarbons, nitrogen oxides, and carbon monoxide while maximising brake thermal efficiency, brake mean effective pressure, and mechanical efficiency. The optimal configuration includes a fuel injection timing of 6° bTDC, an engine load of 82 %, 6.7 % EGR, 1000 bar injection pressure, and a 33 % grape biodiesel blend. These optimum input conditions yielded outputs of 3.55 bar BMEP, 31.85 % BTE, 64 % mechanical efficiency, 0.278 kg/kWh SFC, 0.127 % CO, 357 ppm NOx, and 8 ppm of HC. These adjustments ensure low emissions and efficient engine operation, highlighting grape biodiesel's potential as a viable alternative fuel.

Experimental study on Camless compressed air engine port timing using electronic control unit

This research paper presents an experimental study conducted on an air engine with an electronic control unit (ECU) to investigate the influence of inlet pressure on port timing. Traditional air engines use mechanical camshafts to control valve timing, but camless air engines use solenoid valves controlled by an ECU for more precise control. The study compares variations in intake sustains angle (ISA) and exhaust advance angle (EAA) to determine their impact on engine performance. The results show that ISA has a more significant influence on work performance and work done compared to EAA. The study also identifies the optimal ISA for different intake pressures, showing that higher ISA is beneficial at low intake pressure, while lower ISA is preferable with increasing pressure. Overall optimal ISA increases an average 12 % percentage efficiency of the air engine compared to the change in EAA. This research provides valuable insights for designing and developing non-polluting propulsion engine systems and optimizing the design of air engines for improved performance, fuel efficiency, and lower emissions.

Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool

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 investigate knock intensity (KI) by different convective heat transfer models. This study can be used to train electronic control unit (ECU) of engine operating on different loads from low to high speed. In present study, experimentation have been performed on HCNG fueled spark ignition engine at different operating conditions by varying hydrogen amount in HCNG, by varying load of engine, by varying exhaust gas recirculation (EGR) rate in air and by varying speed of engine to calculate in-cylinder pressure, knock intensity (KI) and heat transfer rate (HTR). Six convective heat transfer models (Assanis, Eichelberg, Han, Hohenberg, Nusselt and Woschni) have been incorporated in quasi dimensional combustion model (QDCM) to calculate the parameters of HCNG engine. Comparative and predictive analysis using artificial neural network fitting tool (ANNFT) of aforementioned simulated models have been performed with experimental findings to attain the efficient model for knock intensity of HCNG engine. Woschni model, predicts the minimum error of 0.37 % & 0.86 % b/w experimental and simulated in-cylinder pressure and indicated mean effective pressure respectively. Han model predicts the minimum error of 3.8 % b/w experimental and simulated knock intensity operating at 11.2 % EGR. Maximum error of 20.3 % at 2.4 % EGR is attained by using Woschni model in knock intensity calculation. The best suited algorithm for present data set is Bayesian regularization utilized in ANNFT to predict knock intensity effectively. The findings of this study can be utilized in the development of HCNG engine.

An automated consistency management approach for a privacy-aware electric vehicle architecture

Modern vehicles contain a number of highly connected embedded systems that generate, store, and process information and exchange it with their environment. Since a large part of this information is privacy-critical, privacy laws such as the GDPR of the European Union apply to it. In this work, we evaluate the privacy-criticality of exemplary data and data flows of the electric driving domain on a reference architecture. We categorize the ECUs of the architecture based on the criticality of the data they process and propose measures and technologies as building blocks that provide adequate privacy protection according to the requirements given by the GDPR.To ensure that all requirements are met by the reference architecture, we propose a more principled solution that simplifies the mapping between an architecture and the measures. For this purpose, we propose an architecture description template in JSON and an algorithm for automated consistency checks that outputs the measures and the security extension needed per Electronic Control Unit (ECU) to comply with derived privacy requirements.

Multi-classification in-vehicle intrusion detection system using packet- and sequence-level characteristics from time-embedded transformer with autoencoder

In recent years, the increased application of controller area network (CAN) protocols has made it the de facto standard for communication between electronic control units (ECUs) in the automotive and transportation fields. This widely used protocol was designed as a reliable and straightforward broadcast-based protocol that connects ECUs without considering security concerns such as node authentication or traffic encryption. Despite its efficiency, this tradeoff makes the CAN bus vulnerable to attacks. Implementing intrusion detection systems (IDSs) based on machine learning (ML) can address these security challenges effectively. However, existing ML-based IDSs have limited classification capabilities, lack adaptability and time sensitivity, incomprehensive analysis, and produce high false-negative rates (FNR), while attack schemes are becoming increasingly complex, resulting in insufficient capability of intrusion detection in real-time and insufficient ability to offer reliable protection. Therefore, our study proposes a novel in-vehicle IDS for multiclass classification using both packet- and sequence-level characteristics extracted from an autoencoder and a variant transformer (Time-embedded Transformer) with an improved position encoding mechanism, which analyses CAN traffic in-depth from various perspectives to overcome the existing challenges above. Both standard (Car-Hacking) and advanced (ROAD) datasets are used to evaluate the capabilities of our proposed IDS. The evaluation results demonstrated 100 % detection accuracy and 0 % FNR for both the Car-Hacking and ROAD Masquerade datasets, which also peaked at the highest F1 score for the ROAD Fabrication dataset, emphasizing superior intrusion detection to minimize FNR of the proposed model with high adaptability through its multi-dimensional analysis at packet- and sequence-level.

The Need for Cybersecurity in Automotive Industry

This research paper analyzes the importance of cybersecurity in the automotive industry and explains how cybersecurity threats impact the modern world of connected, autonomous, and electrical vehicles. Furthermore, this paper covers in-depth research into the different types of automotive cyber threats such as physical, sensor, software, and network attacks on a vehicle, and various methods to prevent these threats from happening in real-world scenarios. The research paper also helps the reader to gain a broader understanding of cybersecurity in the automotive world. The methodology of this research involved analyzing and collecting information through various external websites, articles, and research papers. Information was collected on automotive cybersecurity incidents from news articles and international cyber security conference presentations. Based on this evidence from various external sources, the research problem was supported by factual data. This paper also talks about all the various solutions to cybersecurity threats in depth such as authentication and encryption, malware detection, software analysis, VPNs (Virtual Private Network), and cryptography. This paper also provides information about some of the new regulations and standards that are currently adopted in the automotive industry. With the rapid change in automotive technology, it is important to be aware that cars can have up to 150 ECUs (Electronic Control Unit) and about 300 million lines of code in near future which could expose more vulnerabilities. (2020, McKinsey & Company). To meet the challenge of these automotive cyber security threats, it is important that awareness should be created among the product engineers and researchers working on future automobiles. This paper attempts at exploring the challenges with automotive cyber security and creating this awareness.

Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different loads

The transportation industry is increasingly focused on hydrogen based fuel as a promising alternative due to its potential for reduced emissions and enhanced performance. The purpose of this study is to improve efficiency and reduce emissions on low speed on different loads for heavy duty vehicles. This study can be impactful to train the electronic control unit (ECU) for heavy duty vehicles working on aforementioned conditions. This study investigates the effect of hydrogen ratios (0%–40 %) in HCNG, (0%–15 %) exhaust gas recirculation (EGR) ratios, and spark timing (8°-34o CA bTDC) at low and high loads (15 % & 75 %) under stoichiometric conditions at low speed (700 rpm). Performance, emissions and combustion parameters were thoroughly analysed across these conditions. Brake thermal efficiency increases by 20.7 % & 19.4 % by the addition of (0 %–40 %) hydrogen at low and high load at 14o CA bTDC running on 5 % and 0 % EGR respectively. NOx emissions reduces by 11.9 % & 17.9 % by the addition of (0 %–15 %) EGR and increases by 46.1 % & 46.4 % by increasing the amount of hydrogen in HCNG at low and high load at 14o CA bTDC at 0 % EGR respectively. Coefficient of variation reduces 13.8 % by (0 %–40 %) hydrogen addition at 11 % EGR at 16o CA bTDC. Optimized Gaussian process regression (GPR) and neural network (NN) machine learning techniques were applied to the dataset, and found GPR matern 5/2 is best one. The findings can be utilized in the development of HCNG engine.

Evaluation of SiL Testing Potential—Shifting from HiL by Identifying Compatible Requirements with vECUs

Due to the increasing complexity of vehicle software, it is becoming increasingly difficult to comprehensively test all requirements. This inevitably means that alternative test methods, e.g., simulation-based methods, must be used more frequently. However, the challenge involves identifying appropriate requirements that can be technically tested in a simulation environment initially. The present work is aimed at evaluation and optimization of the effectiveness of software-in-the-loop (SiL) simulations in the testing process of vehicle software. The focus is on supporting the testing process by shifting specific test cases from hardware-in-the-loop (HiL) test benches to SiL-based simulations. For this purpose, a systematic approach was developed to analyze and categorize requirements, enabling precise and efficient allocation of test cases. Furthermore, a detailed review and recommendation for improving the ProSTEP iViP standard for virtual electronic control units (vECU) was carried out. The developed matrix associates the defined requirement clusters with different classifications of vECUs, facilitating the identification of suitable test environment types for conducting specific test cases. By assigning test cases to appropriate vECU levels, the testing processes can be targeted and cost-optimized. Finally, the theoretical results were evaluated in an SiL simulation environment. It was observed that a significant part of the requirements could effectively be tested using a vECU. These findings confirmed the potential of SiL simulation environments to not only support, but also enhance, the testing process for vehicle software by providing a cost-effective and flexible complement to traditional HiL test benches.

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture.

With recent advances in vehicle technologies, in-vehicle networks (IVNs) and wiring harnesses are becoming increasingly complex. To solve these challenges, the automotive industry has adopted a new zonal-based IVN architecture (ZIA) that connects electronic control units (ECUs) according to their physical locations. In this paper, we evaluate how the number of zones in the ZIA affects the end-to-end (E2E) delay and the characteristics of the wiring harnesses. We evaluate the impact of the number of zones on E2E delay through the OMNeT++ network simulator. In addition, we theoretically predict and analyze the impact of the number of zones on the wiring harnesses. Specifically, we use an asymptotic approach to analyze the total length and weight evolution of the wiring harnesses in ZIAs with 2, 4, 6, 8, and 10 zones by incrementally increasing the number of ECUs. We find that as the number of zones increases, the E2E delay increases, but the total length and weight of the wiring harnesses decreases. These results confirm that the ZIA effectively uses the wiring harnesses and mitigates network complexity within the vehicle.

CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus

Considering the complexity of network traffic in IoV operations, methods that can identify complex patterns become useful. Machine learning fosters several techniques to enhance the detection, prevention, and mitigation of cyberattacks. However, important features are not addressed in the current state-of-the-art security datasets for IoV. For example, in the case of intra-vehicle communications, it is critical to consider the interaction among multiple Electronic Control Units (ECUs). Also, mimicking a realistic IoV environment is not simple since establishing a test environment requires considerable financial investment. Hence, there is a need for a testbed composed of several real ECUs in an IoV environment comprising network traffic. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to foster the development of new cybersecurity solutions for IoV operations. To accomplish this, five attacks were executed against the fully intact inner structure of a 2019 Ford car, complete with all ECUs. However, the vehicle was immobile and incapable of causing any potential harm or injuries. Hence, all attacks were carried out on the vehicle without endangering the car’s driver or passengers. These attacks are classified as spoofing and Denial-of-Service (Dos) and were carried out through the Controller Area Network (CAN) protocol. This effort establishes a baseline complementary to existing contributions and supports researchers in proposing new IoV solutions to strengthen overall security using different techniques (e.g., Machine Learning — ML). The CICIoV2024 dataset has been published on CIC’s dataset page.

Automotive digital forensics through data and log analysis of vehicle diagnosis Android apps

Modern vehicles including smart cars have been equipped with many electronic devices such as electronic control units (ECUs), on-board diagnostics (OBD) systems, telematics and infotainment systems, gateways, sensors, etc. Because these devices create, transmit, and store a lot of digital data, modern vehicles are becoming key source of digital evidence in vehicular forensics. In addition, some dedicated mobile apps can capture driving and diagnostic data from a vehicle via a Bluetooth-enabled OBD-II scanner. In this paper, we propose a new process for effective automotive forensics. It collects and analyzes three different types of data left on an Android phone which has been connected to the OBD-II port of a vehicle via Bluetooth communication. The three types of data are OBD-II Android apps' data, Bluetooth HCI snoop log, and the main log buffer of the Android logging system. By analyzing them individually and integratedly, we find Bluetooth connection time, vehicle information, MAC address of the OBD-II scanner, vehicle velocity, sharp speeding event, sudden braking event, refueling event, and so on. We also construct a timeline of Bluetooth traffic and driving events through the timeline analysis, which can be used to determine the driver's behaviors in terms of vehicle forensics.

Low-pressure gasoline direct injection system development and emissions analysis for a reciprocating two-stroke engine

This study presents the design and construction of an electronic low-pressure gasoline direct injection system for two-stroke engines, with the objective to study engine performance and the emission of exhaust polluting gases, maintaining the expectations of this type of engine. The investigation includes a test bench with an incorporated engine and the required control devices for this, as well as a gas analyzer Class 0, a computerized data acquisition and several tools for displaying and process all the electronic signals from the engine in real time. In order to manage this system, a custom electronic injection control unit has been designed and built, which allows to control the fuel injection timing and duration. This unit uses the signal from an inductive sensor installed in the crankshaft as a reference and synchronization point. Additionally, modifications have been made to the fuel feeder circuit and the electronic of the test engine, as well as to some parts of the mechanism in order to adapt them to this technology. The implementation of the engine tests is described, and the performance and operational points of the original system and those of the new injection system are evaluated.