Electronic Control Unit Research Articles

Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles

Each electric vehicle (EV) requires a low-voltage (e.g., 12V) auxiliary battery to provide electric power to onboard electronic control units, lighting systems, and various sensors during power off. Therefore, when the low-voltage battery is in low state of health (SOH) or low state of charge (SOC), it may cause no-start events. The existing OnStar Proactive Alert service can effectively predict low SOC or low SOH events for low-voltage batteries in Internal Combustion Engine vehicles using cranking signals. However, it does not work for EVs since there is no cranking event. In this work, a diagnostic and prognostic solution for the low-voltage battery of EVs is proposed. Four novel health indicators (HIs) along with the decision-making system are developed based on equivalent circuit models. Furthermore, the selection process of appropriate HIs tailored to various operational states of the vehicle is described. The validation results based on GM test EV data have demonstrated the effectiveness and robustness of the proposed solution.

A fully integrated wireless microfluidic immunosensing system for portable monitoring of Staphylococcus aureus

The advanced devices that function fully without the need for external accessories are regarded as a pinnacle goal in the design and construction of modern ones. Staphylococcus aureus (S. aureus), a prominent human pathogen, is responsible for causing a wide variety of infections and chronic diseases. Herein, we present the first instance of a fully integrated wireless microfluidic immunosensing system (FIWMIS) capable of conducting point-of-care S. aureus monitoring in real samples of S. aureus-spiked commercial purified drinking water and S. aureus-spiked watermelon juice. The development of the proposed FIWMIS became a reality by conquering significant engineering hurdles in seamlessly integrating a microfluidic unit for liquid sample transport without the need of an external pump, an immunosensing unit for S. aureus monitoring, and an electronic control unit for signal conversion and wireless transmission. Such full integration culminated in a FIWMIS that upholds its pump-free, wireless, and low-cost characteristics for portable monitoring of S. aureus.

A Novel Light-Weight Machine Learning Classifier for Intrusion Detection in Controller Area Network in Smart Cars

The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles.

Container-Based Electronic Control Unit Virtualisation: A Paradigm Shift Towards a Centralised Automotive E/E Architecture

The past 40 years have seen automotive Electronic Control Units (ECUs) move from being purely mechanical controlled to being primarily digital controlled. While the safety of passengers and efficiency of vehicles has seen significant improvements, rising ECU numbers have resulted in increased vehicle weight, greater demands placed on power, more complex hardware and software, ad hoc methods for updating software, and subsequent increases in costs for both vehicle manufacturers and consumers. To address these issues, the research presented in this paper proposes that virtualisation technologies be applied within automotive electrical/electronic (E/E) architecture. The proposed approach is evaluated by comprehensively studying the CPU and memory resource requirements to support container-based ECU automotive functions. This comprehensive performance evaluation reveals that lightweight container virtualisation has the potential to welcome a paradigm shift in E/E architecture, promoting consolidation and enhancing the architecture by facilitating power, weight, and cost savings. Container-based virtualisation will also enable efficient and robust online dynamic software updates throughout a vehicle’s lifetime.

High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring.

Gradient correlation based detection of adversarial attacks on vehicular networks

The controller area network (CAN) bus, which controls real-time communication and data transmission among vehicle electronic control units, lacks security mechanisms and is highly vulnerable to attacks. Existing in-vehicle network intrusion detection systems (IDSs) typically rely on deep learning models for detection, which are susceptible to interference from adversarial attacks owing to the vulnerability of the models themselves, thereby compromising the detection performance. In this study, we propose an adversarial attack detection method based on gradient correlation that achieves a high accuracy rate using a linear approach to detect adversarial samples. The experimental results show that the proposed model does not require retraining of the original detection model and demonstrates better detection performance for multiple adversarial attacks.

Development of a flexible electronic control unit for seamless integration of machine vision to CAN-enabled boom sprayers for spot application technology

This research work aimed to develop an Electronic Control Unit (ECU) to establish a flexible bridge between machine vision and boom sprayer to control nozzles individually for pesticide spot application based on the Controller Area Network (CAN). The ECU consisted of two electronic entities. The first used UART protocol to parse machine vision messages, detect pest areas, and convert them into binary arrays for nozzle activation. The second received these arrays and generated nozzle controller CAN frames which were broadcast to control the sprayer nozzles on the implement bus. The ECU was tested in four scenarios involving combinations of three machine vision systems and two nozzle systems. The lab tests confirmed, assuming accurate detections, the ECU successfully sent spray commands to all targets across various camera-nozzle ratios. However, at specific ratios (1:3 and 1:6), some nozzles opened in unintended patterns. In the fourth scenario conducted in the field at a 1:2 ratio, all targets were sprayed regardless of their dimensions and distribution in the field. In this scenario, the sprayer operated at speeds of 3.22 km/h, 6.44 km/h, and 9.66 km/h, demonstrating real-time spraying with 55° angled nozzles, where the ECU sent CAN messages every 10ms and issued 400 ms spray commands upon detection, achieving a minimum spray length of 345 mm per detection.

Safety Concepts for Future Electromechanical Brake Central Control Systems

<div>Electromechanical brakes (EMB) are currently coming into focus in the automotive industry. This trend was confirmed in 2022, when a first automotive supplier [<span>1</span>] announced the series production of EMB systems. One major driver is safety, especially if EMB systems are implemented with smart actuators that install redundant electronic control units (ECU) and distributed software [<span>1</span>]. Earlier, the authors have addressed safety mechanisms in EMB actuators [<span>2</span>]. In this article the authors extend their investigation to address safety mechanisms in future EMB central control systems (CCS). Impact of different brake system topologies (X-, H-, centralized) vis-à-vis potential safety mechanisms within communication buses and ECUs is analyzed.</div>

Advancing Vehicle Security: Deep Learning based Solution for Defending CAN Networks in the Internet of Vehicles

The Internet of Vehicle (IoV) is revolutionizing the automobile sector by allowing vehicles to interact with one another and with roadside infrastructure. The Controller Area Network (CAN) is a vital component of such smart vehicles, allowing communication between various Electronic Control Units (ECUs). However, the CAN protocol's intrinsic lack of security renders it open to a variety of cyber-attacks, posing substantial hazards to both safety and privacy.This research investigates the use of deep learning with multi-layer perceptron to improve the security of CAN networks inside the IoV framework. We discuss current threats to CAN networks, including spoofing, replay, and denial-of-service attacks, and how deep learning may be used to identify and mitigate these threats efficiently. We propose a unique deep learning-based defense mechanism for real-time threat detection. The suggested method is highly effective in identifying and mitigating potential risks, as evidenced by extensive testing on real-world CAN datasets. Based on our findings, the proposed solution has the potential to considerably enhance the security of CAN networks in the Internet of Vehicles, making car communication systems more secure and reliable.

XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

Network anomaly detection in cars: A case for time-sensitive stream filtering and policing

Connected vehicles are threatened by cyber-attacks as in-vehicle networks technologically approach (mobile) LANs with several wireless interconnects to the outside world. Malware that infiltrates a car today faces potential victims of constrained, barely shielded Electronic Control Units (ECUs). Many ECUs perform critical driving functions, which stresses the need for hardening security and resilience of in-vehicle networks in a multifaceted way. Future vehicles will comprise Ethernet backbones that differentiate services via Time-Sensitive Networking (TSN). The well-known vehicular control flows will follow predefined schedules and TSN traffic classifications. In this paper, we exploit this traffic classification to build a network anomaly detection system. We show how filters and policies of TSN can identify misbehaving traffic and thereby serve as distributed guards on the data link layer. On this lowest possible layer, our approach derives a highly efficient network protection directly from TSN. We classify link layer anomalies and micro-benchmark the detection accuracy in each class. Based on a topology derived from a real-world car and its traffic definitions we evaluate the detection system in realistic macro-benchmarks based on recorded attack traces. Our results show that the detection accuracy depends on how exact the specifications of in-vehicle communication are configured. Most notably for a fully specified communication matrix, our anomaly detection remains free of false-positive alarms, which is a significant benefit for implementing automated countermeasures in future vehicles.

BIDS: An efficient Intrusion Detection System for in-vehicle networks using a two-stage Binarised Neural Network on low-cost FPGA

Automotive networks are crucial for ensuring safety as the number of Electronic Control Units (ECUs) grows to support vehicle intelligence. The Controller Area Network (CAN) is commonly used for efficient in-vehicle communication among ECUs. However, its broadcast nature and lack of a dedicated security layer make it vulnerable to attacks. This paper proposes a novel CAN bus Intrusion Detection System (IDS), named BNN-based IDS (BIDS), which efficiently provides both unknown attack detection and known attack classification using a hierarchical two-stage Binarised Neural Network (BNN) and Generative Adversarial Network (GAN). BIDS was validated on three datasets, and its implementation achieves an average inference time of less than 0.170 ms with minimal resource utilisation on a low-cost Field Programmable Gate Array (FPGA). This rapid inference speed enables real-time inference on individual CAN messages using a sliding window technique, eliminating the need to wait for multiple accumulated CAN messages required for data preprocessing. Evaluation metrics demonstrate that our IDS achieves high accuracy in both identifying unseen attacks and categorising known attacks. Furthermore, our FPGA implementation consumes merely 2.09 W, which is a 57% reduction compared to a cutting-edge FPGA-based IDS that is capable of detecting unknown attacks using the same dataset.

Thermal Fault-Tolerant Asymmetric Dual-Winding Motors in Integrated Electric Braking System for Autonomous Vehicles

A conventional dual-winding (DW) motor has two internal windings consisting of a master part and a slave part, each connected to a different electronic control unit (ECU) to realize a redundant system. However, existing DW motors have a problem related to heat generation in both the healthy mode and the faulty mode of the motor operation. In the healthy mode, unexpected overloads can cause both windings to burn out simultaneously due to equal heat distribution. If the current sensor fails to measure correctly, the motor may exceed the designed current density of 4.7 [Arms/mm2] under air-cooling conditions, further increasing burnout risk. External factors such as excessive load cycles or extreme heat conditions can further exacerbate this issue. In the faulty mode, the motor requires double the current to generate maximum torque, leading to rapid temperature increases and a high risk of overheating. To address these challenges, this paper proposes the design of a thermal fault-tolerant asymmetric dual-winding (ADW) motor, which improves heat management in both healthy and faulty modes for autonomous vehicles. A lumped-parameter thermal network (LPTN) with a piecewise stator-housing model (PSMs) was employed to evaluate the coil temperature during faulty operation. An optimal design approach, incorporating kriging modeling, Design of Experiments (DOE), and a genetic algorithm (GA), was also utilized. The results confirm that the proposed ADW motor design effectively reduces the risk of simultaneous burnout in the healthy mode and overheating in the faulty mode, offering a robust solution for autonomous vehicle applications.

Improving the dynamic positioning accuracy of the electro-hydraulic drive

Research is focused on improving the dynamic accuracy of control surface positioning during the switching of hydraulic actuator modes and studying the processes and phenomena that cause control surface overshoot. Existing studies on the dynamic accuracy of hydraulic actuators have been analyzed. Transitional processes occurring in the " electromagnetic recirculation valve – electronic control unit" loop during changes in hydraulic actuator operating modes have been examined. The features of processes and physical phenomena leading to increased activation time of the hydraulic actuator have been identified. Solutions and electrical connection schemes for connecting the hydraulic actuator recirculation valve to the electronic control unit have been proposed to meet the accuracy positioning and activation time requirements. Experimental studies have been conducted to simulate the operational mode of hydraulic actuators, and failure situations modes under various load forces have been identified. Based on the results, principles for designing connection schemes for the electromagnetic recirculation valve of the hydraulic-mechanical actuator to the electronic control unit, which enhance system positioning accuracy at the structural level, have been formulated. Technical solutions aimed at achieving the specified accuracy and activation time for the hydraulic actuator have been developed and practically tested. The effectiveness and suitability of the proposed solutions for operational use, considering external factors, have been experimentally confirmed.

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

Reducing real driving fuel consumption and emissions with a hydraulic hybrid vehicle

We have successfully prototyped and tested the real-driving performance of a hydraulic hybrid heavy commercial vehicle. Hydraulic hybrid driveline technology was fitted to a Euro IV diesel truck equipped with a diesel particulate diffuser/filter and an exhaust gas recirculation system. An AVL portable emissions measurement system was fitted to the test vehicle to quantify gaseous and particle phase emissions species and an on-board diagnostic scan tool was used to obtain data from the trucks electronic control unit. Fuel consumption savings up to 17 % were accomplished in hydraulic hybrid mode when testing on a short urban driving route featuring a high intensity of stop-start activity. Average reductions in solid particle number concentration of 40 % were achieved along with increases in NOx, Hydrocarbons (THCs) and CO. Future work could target improved control system integration of the various driveline components to further reduce fuel consumption and emissions in an improved prototype design.

Enhancing ECU identification security in CAN networks using distortion modeling and neural networks

A novel technique for electronic control unit (ECU) identification is proposed in this study to address security vulnerabilities of the controller area network (CAN) protocol. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the lack of message authentication. In this regard, we model the ECU-specific random distortion caused by the imperfections in the digital-to-analog converter and semiconductor impurities in the transmitting ECU for fingerprinting. Afterward, a 4-layered artificial neural network (ANN) is trained on the feature set to identify the transmitting ECU and the corresponding ECU pin. The ECU-pin identification is also a novel contribution of this study and can be used to prevent voltage-based attacks. We have evaluated our method using ANNs over a dataset generated from 7 ECUs with 6 pins, each having 185 records, and 40 records for each pin. The performance evaluation against state-of-the-art methods revealed that the proposed method achieved 99.4% accuracy for ECU identification and 96.7% accuracy for pin identification, which signifies the reliability of the proposed approach.

A hybrid framework for latency compensation in remote testing of automotive electronic control units

As the number of electronic control units (ECUs) in vehicles continues to grow, exchanging physical components between original equipment manufacturers (OEMs) and ECU suppliers presents logistical challenges, impeding the pace of development and leading to accumulation of costs. In this work, we introduce a conceptual framework that enables remote testing of geographically dispersed ECUs over the Internet. Pertinent to the Internet of Things (IoT) sphere, where interconnection of distributed components is hampered by the inherent challenges of latency, we propose a hybrid synchronous methodology that combines asynchronous test management with time synchronization mechanisms to mitigate the delay impact within the distributed environment. Additionally, we discuss challenges and prospects associated with this approach.

AUTOSAR-Compatible Level-4 Virtual ECU for the Verification of the Target Binary for Cloud-Native Development

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts.

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.