Electronic Control Unit Research Articles

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.

IoVCipher: A low-latency lightweight block cipher for internet of vehicles

The data security of CAN bus system is receiving increasing attention with the rapid development of Internet of Vehicles (IoV). However, traditional ciphers are not the best choice due to the limitations of computation, real-time, and resources of Electronic Control Units in vehicles. Thus, this paper proposes a lightweight block cipher IoVCipher to protect the security of IoV. It is designed focus on the latency and area in round-based architectures (both encryption and decryption) to meet this resource-constrained environments. For this purpose, two S-boxes with low latency and tiny area are constructed in this paper, one involution and one non-involution. Considering the decryption latency, a low latency subkey generation method is designed. In addition, this paper proposes a new extended MISTY structure that makes the encryption and decryption of hardware implementations similar. In comparison to other low-latency lightweight block ciphers such as PRINCE, QARMA, MANTIS and LLLWBC, IoVCipher achieves an effective balance between latency and area in the round-based architecture, and IoVCipher has low latency, low area, and low energy in the fully unrolled architecture. Finally, IoVCipher is implemented on a real-time speed acquisition and encryption testbed to simulate encrypted transmission of real-time speed in a CAN bus environment.

Implement Standard UDS Diagnostics over can for Automotive Actuator ECU

Electronic control units (ECUs) are an integral part of modern vehicles, controlling everything from safety procedures and system diagnostics to engine optimization. These ECUs need to work flawlessly to keep the vehicle reliable and efficient. The aim of this project is to use Controller Area Network (CAN) and Unified Diagnostic Services (UDS) communication systems to diagnose ECUs in vehicles (specifically those that control the engine). The main goal of this project is to create a comprehensive diagnostic system specifically designed for vehicle ECUs with the aim of improving engine diagnostics and improving vehicle maintenance procedures. The project combines UDS and CAN, two communication methods widely used in the automotive industry, to achieve these goals and provide effective communication between diagnostic tools and ECUs.

Driving Towards Safety: The Role of ECUs and IMUs in Advanced Driver-Assistance Systems (ADAS)

This research paper explores the pivotal role of Electronic Control Units (ECUs) and Inertial Measurement Units (IMUs) in Advanced Driver-Assistance Systems (ADAS), focusing on their functionalities, applications, regulatory frameworks, emerging technologies, and real-world case studies. Through a comprehensive review of literature, this paper investigates the integration of ECUs and IMUs in ADAS and its implications for vehicle safety, mobility, and sustainability. Key topics addressed include the adoption challenges, societal impact, international regulatory landscape, standardization efforts, emerging technologies such as artificial intelligence and sensor fusion, and real case studies exemplified by Tesla's Autopilot system, Volvo's City Safety system, and Waymo's autonomous taxi service. The paper concludes by highlighting the transformative potential of ECUs and IMUs in shaping the future of automotive technology and advancing towards safer, more efficient, and more sustainable transportation systems.

Compact organ-tissue electrophoresis system (CORES).

Electrophoretic tissue clearing has been a commonly used laboratory method since the early twentieth century. Infrastructure for standard procedures has yet to be formed. In particular, control of the heat produced by electrophoresis, the voltage applied to the electrodes, the resistance, and the speed of liquid circulation create difficulty for researchers. We aimed to develop a compact organ electrophoresis system that enables the researcher to have easy, rapid, and inexpensive working opportunities. The system includes an electronic control unit, a liquid tank, a temperature control unit, and an electrophoresis chamber. The control unit software can keep the system stable by using information on temperature and circulation rate received through the sensors using the feedback principle. Corrosion and particle collection are reduced to a minimum as platinum wires are used for electrophoresis electrodes. A temperature control unit can heat and cool via a liquid tank base. The CORES is an all-in-one, easy-to-use solution for electrophoretic tissue clearing. It assures efficient, rapid, and consistent tissue clearing. The system was stable with 72 h of continuous operation. Patent applications and trial version studies for introducing the system to researchers are still in progress.

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms.

Grey wolf optimization tuned drivetrain vibration controller with backlash compensation strategy using time-dependent-switched Kalman filter

To improve the performance and durability of vehicle components, efforts have been made to reduce driveline oscillations using advanced active control algorithms. However, existing methods often rely on subjective parameter adjustments, which can be burdensome for designers. This study introduces an effective tuning algorithm for a driveline vibration controller that accounts for nonlinear backlash effects. Initially, a driveline dynamics model is developed to focus on transient oscillations resulting from changes in driving force and the presence of nonlinear backlash. The backlash impact is incorporated into the model through a discontinuous dead-zone region. Two operational dynamics, which are the contact mode and the backlash mode, are considered. A dynamic output feedback [Formula: see text] controller is designed as a baseline controller to mitigate low-frequency resonance in the driveline. A solution for managing the nonlinear backlash challenges is introduced, involving the use of a simple control mode switching algorithm in conjunction with the controller. This algorithm relies on a time-dependent-switched Kalman filter. Additionally, the optimal settings for the parameters needed by the mode-switching algorithm are autonomously determined using the grey wolf optimizer (GWO). The proposed active controller can be implemented in real vehicles by using an on-vehicle acceleration sensor and electronic control unit (ECU). In a simulation environment, the vehicle body vibration is online fed back to the resultant controller, and an actuator is supposed to apply control commands to the driveline. The effectiveness of this newly proposed active controller is confirmed through comparative tests, revealing the superior vibration control.

EDR (Event Data Recorder) in Vehicles using IOT

The increasing integration of Electronic Control Units (ECUs) in modern vehicles has paved the way for sophisticated in-vehicle monitoring and diagnostics systems, givingrise to the concept of Event-Driven Recording (EDR). EDR in vehicles involves the continuous capture and storage of critical data related to various events, such ascollisions, sudden accelerations, or system malfunctions. This data, often collected from multiple sensors and modules within the vehicle, serves as a crucial tool for post-incident analysis, aiding in the reconstruction of events leading up to and during accidents. EDR not only provides valuable insights for accident investigation but also plays a pivotal role in enhancing vehicle safety by facilitating the identification of potential design flaws and contributing to the development of more robust safety features.

Thermographic Analysis of Exhaust Gas and Emissions by Varying Catalyst Behaviour and Injection Parameters

This study focuses on the detailed analysis of exhaust emissions from multi-point fuel injection (MPFI) engines by manipulating the injection parameters through a programmable electronic control unit. In addition, tests are carried out using different generations of catalytic converters and checking that their working temperature is correct using a thermographic camera, verifying operation, to evaluate their effect on emission reduction. Detailed comparisons of the results between these configurations will allow the identification of the combination that reduces emissions the most without compromising engine efficiency and performance. This research aims to promote a more sustainable approach in the automotive sector by properly configuring systems, but also by demonstrating the technical robustness of their application in vehicles. It has also helped to verify that varying injection and ignition parameters help to fine-tune fuel injection, resulting in efficient combustion. Combining this variation with catalytic converters has further reduced exhaust pollutants.

Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

Dynamic Event-Triggered-Based Adaptive Finite-Time Neural Control for Active Suspension Systems With Displacement Constraint.

In this article, we give an event-based control algorithm for nonlinear active suspension systems (ASSs) with the vertical displacement constraint. For in-vehicle communication networks, the controller area network (CAN) is a widely used standard for interconnecting electronic control units (ECUs). The main task in this work is, thus, to reduce the communication burden on the CAN. To this end, we develop a dynamic event-triggered communication mechanism in ASSs. Meanwhile, in practice, the vehicle safety is degraded when the body vibration exceeds the allowable maximum. Thus, the vertical displacement of ASSs should be constrained within a reliable range. For this purpose, we present a novel finite-time integral barrier Lyapunov function (FTIBLF), which not only guarantees that the vertical displacement constraint bounds are not violated, but also enables the position of the suspension to be stabilized in the neighborhood of a desired position in finite settling time. Furthermore, neural networks (NNs) are utilized to identify the unknown nonlinear characteristics in ASSs subjected to the modeling error and unknown mass. As a result, we propose an adaptive neural control technique based on the dynamic event-triggered condition, which helps to improve the ride comfort while ensuring the driving safety and handling stability. Finally, simulation results are provided to verify the validity of the presented methodology.

Overview of the use of oxygen sensors in automotive applications

In the process of combustion, the air-fuel ratio is important for control strategies to reduce fuel economy and improve engine performance. Oxygen sensors are key elements in electronic fuel injection systems to indicate the oxygen content and send electrical signals to the electronic control unit. Starting with the first generation of oxygen sensors, which had limited applications, all automotive manufacturers now use wideband sensors to detect oxygen concentration with rapid reaction and linear output characteristics. Also, these new sensors provide accurate information for a wide range of air coefficients (λ = 0.65-2.4), improving combustion efficiency. In this paper, several types of narrowband and wideband sensors were analyzed, measuring the activation temperature for each sensor until a reliable signal was obtained.

Experimental research regarding the autonomy of electric cars in real driving conditions

Currently, almost all car manufacturers have in the production scheme at least one vehicle model with a hybrid or electric energy source. This fact is generated by the high interest in the market related to the subsidies offered to the purchase by the states but also by the high performance in terms of pollution, noise, and economy, which they can achieve. The only performance indicator which for the moment is not at a satisfactory level is represented by autonomy. At this point, the electric cars have an autonomy between 250 and 800 km driven after a single charge of the traction battery. It should be mentioned though that, the autonomy of electric vehicles declared by European manufacturers is obtained in laboratory conditions, these being carried out in accordance with the provisions of the European Union regulations. Obviously, there are significant differences between the electric autonomy obtained in laboratory conditions and the autonomy obtained in real driving conditions. These differences are related to the ideal test conditions achieved at the laboratory level and which cannot be fully found in real driving conditions. Starting from these premises, in a first part of the paper, are presented the theoretical arguments regarding the determination of autonomy in real driving conditions with a driving profile specific to the RDE testing and are described the technical details of the electric vehicle under investigation. In the second part of the paper, are presented the results of the experimental research for the three driving zones specific to the RDE testing: driving in the urban zone, driving in the rural zone, and driving on the motorway. The downward evolution of the state of charge of the traction battery for each driving zone is shown graphically. Following the processing, analysis and interpretation of the data acquired from the car electronic control units, are formulated conclusions regarding the causes that influence the electric autonomy.

A Decentralized Voting and Monitoring Flight Control Actuation System for eVTOL Aircraft

The emergence of eVTOL (electrical Vertical Takeoff and Landing) aircraft necessitates the development of safe and efficient systems to meet stringent certification and operational requirements. The primary state-of-the-art technology for flight control actuation in eVTOL aircraft is electro-mechanical actuators (EMAs), which heavily rely on multiple redundancies of critical components to achieve fault tolerance. However, challenges persist in terms of insufficient reliability, immaturity, and a lack of a measurable evaluation method. This research addresses these issues by elucidating the design requirements for EMAs in eVTOL aircraft and proposing a systematic design and evaluation approach for EMA architecture. A key enhancement involves the incorporation of decentralized voting and monitoring (VoDeMo) mechanisms within the Electronic Control Units (ECUs) to improve the overall safety of the EMA. The paper introduces an innovative triple-dual redundant architecture for aircraft control effectors, comprising three dissimilar lanes of ECUs and two similar redundant parallel channels of power electronics and motors. The design is synergistically supported by a comprehensive evaluation that incorporates quantifiable Model-Based Safety Assessment (MBSA), utilizing both physical simulation and logical safety models. Hardware-In-the-Loop (HIL) tests are conducted on a constructed prototype to validate the proposed architecture.

An Effective Ensemble Learning-Based Real-Time Intrusion Detection Scheme for an In-Vehicle Network

The emergence of connected and autonomous vehicles has led to complex network architectures for electronic control unit (ECU) communication. The controller area network (CAN) enables the transmission of data inside vehicle networks. However, although it has low latency and enjoys data broadcast capability, it is vulnerable to attacks on security. The lack of effectiveness of conventional security mechanisms in addressing these vulnerabilities poses a danger to vehicle safety. This study presents an intrusion detection system (IDS) that accurately detects and classifies CAN bus attacks in real-time using ensemble techniques and the Kappa Architecture. The Kappa Architecture enables real-time attack detection, while ensemble learning combines multiple machine learning classifiers to enhance the accuracy of attack detection. The scheme utilizes ensemble methods with Kappa Architecture’s real-time data analysis to detect common CAN bus attacks. This study entails the development and evaluation of supervised models, which are further enhanced using ensemble techniques. The accuracy, precision, recall, and F1 score are used to measure the scheme’s effectiveness. The stacking ensemble technique outperformed individual supervised models and other ensembles with accuracy, precision, recall, and F1 of 0.985, 0.987, and 0.985, respectively.

Safety Concepts for Future Electromechanical Brake Actuators

<div>A growing interest in electromechanical brakes (EMB) is discernible in the automotive industry finding its climax in an announcement of EMB series production in late 2022 [<span>1</span>]. The introduction of EMB allows for new design opportunities using distributed software on smart actuators. However, additional efforts are needed to ensure continuously high levels of safety even when established design principles in the brake system are changed.</div> <div>This article discusses different safety concepts that could potentially be put in place in EMB actuators. Therefore, safety goals that need to be satisfied by an actuator are derived. Furthermore, three different degrees of complexity are differentiated, evolving to different required electronic control units (ECU) and architectures. Additionally, also the safety of the actuation unit (AU) is considered to realize a holistic safety concept for the actuator. Finally, a conclusion is drawn comparing the different investigated concepts.</div>

Intrusion detection system for controller area network

The rapid expansion of intra-vehicle networks has increased the number of threats to such networks. Most modern vehicles implement various physical and data-link layer technologies. Vehicles are becoming increasingly autonomous and connected. Controller area network (CAN) is a serial bus system that is used to connect sensors and controllers (electronic control units—ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. The goal of this research is to design, implement, and test an efficient and effective intrusion detection system for intra-vehicle CANs. Classic cryptographic approaches are resource-intensive and increase processing delay, thereby not meeting CAN latency requirements. There is a need for a system that is capable of detecting intrusions in almost real-time with minimal resources. Our research proposes a long short-term memory (LSTM) network to detect anomalies and a decision engine to detect intrusions by using multiple contextual parameters. We have tested our anomaly detection algorithm and our decision engine using data from real automobiles. We present the results of our experiments and analyze our findings. After detailed evaluation of our system, we believe that we have designed a vehicle security solution that meets all the outlined requirements and goals.

Stress corrosion cracking of leaded brass used in oil temperature sensor of vehicle

In today’s modern age, many kinds of sensors are used in cars. Sensors used in automobiles monitor the performance of the entire vehicle. They are used to control and process oil, temperature, emission level, coolant level, etc. In vehicle engine, oil is pumped under high-pressure throughout the engine system to lubricate the engine parts that are in constant friction. By doing so, it also transfers away heat from these engine parts. As the oil travels around the motor, large amount of heat keeps being absorbed and transferred. It eventually reaches engine sump where it is cooled using a coolant, and then again recirculated through the engine. In this process, overheating of this oil can greatly affect its viscosity and reduce its efficiency, eventually causing performance issue of the engine. To prevent overheating, temperature oil sensors are used, that continuously send data to the Electronic Control Unit (ECU) then subsequently ECU adjusts and regulates the fuel quantity and ignition timing. Oil Temperature Sensors (OTS) usually consists of three parts: brass body, thermistor unit and a coupler unit. For continuous temperature monitoring, the brass body is continuously in contact with the engine oil that are based on sulphates and ammoniate, under high pressure and elevated temperature. In present study, we observed leakage failure in the brass body of OTS and conducted root cause failure analysis. The basic objective of failure analysis is to identify the overall damage mode like: mechanical overload, environmental degradation / corrosion, fatigue etc. and its type and root-cause leading to the failure and its understanding help to mitigate similar failures in near future. In the present work, the failed / cracked brass body material was analyzed by using Scanning Electron Microscopy attached with Energy Dispersive Spectroscopy for identifying mechanisms of corrosion failures in OTS brass body components is highlighted. Failure analysis of cracked brass body showed the Stress Corrosion Cracking (SCC) failure which is further confirmed by multiple crack branching and Intergranular (IG) fracture.

AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle

<div>This scientific publication presents the application of artificial intelligence (AI) techniques as a virtual sensor for tailpipe emissions of CO, NOx, and HC in a high-performance vehicle. The study aims to address critical challenges faced in real industrial applications, including signal alignment and signal dynamics management. A comprehensive pre-processing pipeline is proposed to tackle these issues, and a light gradient-boosting machine (LightGBM) model is employed to estimate emissions during real driving cycles. The research compares two modeling approaches: one involving a unique “direct model” and another using a “two-stage model” which leverages distinct models for the engine and the aftertreatment. The findings suggest that the direct model strikes the best balance between simplicity and accuracy. Furthermore, the study investigates two sensor setups: a standard configuration and an optimized one, which incorporates an additional lambda probe in the exhaust line after the main catalyst. The results indicate a significant enhancement in performance for NOx and CO estimations with the introduction of the third lambda probe, while HC results remain relatively unchanged. Additionally, the AI model is tested on two different electronic control unit (ECU) software calibrations, yielding excellent results in both cases. This suggests that machine learning models are robust to control software variation and can be used to optimize software calibrations in a virtual environment, reducing the reliance on extensive experimental testing. Moreover, the AI model’s performance demonstrates compatibility with real-time implementation. In conclusion, this work establishes the viability and efficiency of AI techniques in accurately estimating tailpipe emissions from an engine in an industrial context. The study showcases the potential for AI to contribute to emission estimation and optimization processes, offering a promising pathway for an innovative industrial practice.</div>

Electronic control and measurement unit of conductometric nitrogen dioxide sensor in the atmosphere

High levels of air pollution lead to various negative consequences, including deterioration in public health. One of the main air pollutants in industrial cities is nitrogen dioxide. Nitrogen dioxide (NO2) is produced by various industrial processes. It is highly toxic and can cause various diseases such as bronchitis, asthma and other respiratory diseases. Despite all efforts to reduce NO2 emissions, the problem of air pollution remains relevant. Therefore, it is important to continue research into the development of new methods for continuous atmospheric monitoring and timely detection of emissions. One of the main tasks of the modern branch of science in the development of a monitoring system is the development of a sensitive, reliable and inexpensive gas sensor for NO2 in the air. The tasks of this work are design, construct, assemble and test a new original electronic control and measurement unit for a previously developed conductometric sensor for nitrogen dioxide in the atmosphere which is capable of recording and interpreting the signal in the form of a numerical response and displaying it on the screen. As a result of the work carried out, a new original electronic control and measurement unit was developed and constructed for the previously developed conductometric gas sensor for nitrogen dioxide in the atmosphere which is capable of recording and interpreting the signal in the form of a numerical response and displaying it on the screen. The developed device was tested in a system with a gas sensor with a p-type semiconductor gas-sensitive element. It has been established that for a model atmosphere of 50 ppm NO2 + N2, the sensor response recorded by the developed device is equal to (10,4 ± 0,2)∙10-3, and for a model atmosphere of 10 ppm NO2 + N2 – (6,2 ± 0,2)∙10-3. The obtained values can be used to determine the concentration of nitrogen dioxide under real atmospheric monitoring conditions.