Accelerated Reliability Testing Methods for Electronic Control Unit of Auxiliary Power Units in Civil Aircraft

This paper proposes a power architecture for a Compound-Wing Unmanned VTOL to supply power during the hovering state. To enhance the hovering efficiency of the UAV while considering the cruising efficiency, the layout structure of a traditional Compound-Wing Unmanned VTOL is optimized. A high-power-density hybrid-power range-extender using an ICE (internal combustion engine) suitable for the Compound-Wing Unmanned VTOL is designed. The engine electronic control unit (ECU) suitable for the range-extender is presented by using a locally linearized state-space equation and LQR (Linear-Quadratic Regulator). Simulation experiments, ground running tests, and flight tests have been conducted to verify the performance of the Compound-Wing Unmanned VTOL and its power architecture.

The growth of electrical/electronic (E/E) systems in vehicles intensifies the need to address their environmental impacts in the automotive industry. Existing tools for E/E architecture (EEA) development focus mainly on technical implementation, while corresponding environmental frameworks remain insufficiently integrated at the product design level. This paper introduces AutoPCM, an automated Large Language Model-based approach that augments human expertise by transforming manufacturing documents into product architecture decompositions. AutoPCM generates the Physical Component Mapping, a visualisation method for representing automotive electronic product architectures, and evaluates applicable circular strategies following the Eco-Sensitivity Framework, a product-centered view of possible circular strategies for distributed and centralised EEAs. Large Language Models interpret manufacturing documents to extract component relationships and joining technologies, generate clear matrix-based representations of product structures, and convert them into JSON Linked Data (JSON-LD) for circularity assessment. AutoPCM, implemented in Palantir Foundry, is validated on two automotive case studies, a headlight electronic control unit and a camera sensor, achieving high performance with F1 scores of 0.93–1.0 for matrix heading and 0.82–0.95 for joint coding. The approach enables real-time sustainability feedback, supporting designers and decision-makers in optimising EEAs for the circular economy.

As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform configuration, and vECU (Virtual Electronic Control Unit) execution into a single, repeatable development workflow. Control logic validated in Simulink is translated into AUTOSAR-compliant software, built into a QEMU (Quick EMUlator)-based vECU, and exercised in DRIM-SimHub using both virtual stimuli and a real sensor–actuator signal delivered through a dedicated I/O interface board. Using a seat–slide virtual limit controller as a representative case, the proposed workflow enables consistent reuse of the test scenarios across model-in-the-loop (MiL), software-in-the-loop (SiL), and virtual ECU stages, while preserving production-level timing behavior and the semantics of the AUTOSAR runtime. The experimental results show that the vECU accurately reproduces the PWM outputs, Hall sensor pulse timing, and limit–stop decisions of physical ECU, and that integration issues previously discovered only in HiL tests can be exposed much earlier. Overall, the workflow shortens verification cycles, improves the observability of timing-dependent behavior, and provides a practical basis for early validation in software-defined vehicle development.

This study discusses the development of a motor vehicle roadworthiness test information system at CV. Axlindo Telematika Purwokerto, which was previously carried out manually through the registration process, testing, recapitulation, and issuance of KIR certificates. This manual system made the service ineffective and time-consuming. The solution developed was an ECU (Electronic Control Unit)-based Electronic Scanner with NodeMCU RS232 support that can automatically read test data and store it on a server for access via the web or Android applications. The development method used a prototype with REST API integration. The test results showed an increase in effectiveness of 98.9%, efficiency of 86.6%, usefulness of 82.2%, and a difference in data transmission time from 19.2 seconds to 1.39 seconds. The main contribution of this study is the design of hardware and software integration that can improve the accuracy and speed of KIR testing based on an intelligent information system.

This paper presents a virtual reference-based preview semi-active suspension system using a Magneto-Rheological (MR) damper to improve ride comfort when traversing bumps. The algorithm is designed to track the virtual reference profile of the vehicle’s corner by introducing a Model Predictive Control (MPC) method while considering the passivity of the MR damper. The proposed MPC is formulated to rely solely on estimable variables from an Inertial Measurement Unit (IMU) and vertical accelerometer. To support implementation on an Electronic Control Unit (ECU), the suspension state estimator employs a simple band-limited filtering structure. The proposed method is evaluated in simulation and achieves performance comparable to a controller that has accurate prior knowledge of the road profile. In addition, simulation results demonstrate that the proposed approach exhibits low sensitivity to sensor noise and bump perception uncertainty, making it well suited for real-world vehicle applications.

Commercial vehicles are a vital component of modern logistics and transportation, forming part of the critical infrastructure and representing safety-critical cyber-physical systems. Contemporary automotive operations are dominated by embedded computing systems that engage through standardized protocols, which constitute the infrastructure of vehicular communication networks. Within the commercial vehicle sector, these systems utilize high-level protocols that operate over the Controller Area Network (CAN) protocol for internal exchanges in medium and heavy-duty vehicles. The Unified Diagnostic Services (UDS) protocol, as described in International Standards Organization (ISO) 14229 (UDS) and ISO 15765 (Diagnostic Communication over CAN), plays a pivotal role by providing vital diagnostic capabilities. This research introduces four specific scenarios that expose deficiencies in the diagnostic protocol standards and how these can be manipulated to initiate attacks on in-vehicle computers within commercial vehicles, circumventing existing security frameworks. In the first three scenarios, we demonstrate three flaws within the ISO 14229 protocol standards. Following this, the fourth and final scenario elucidates a flaw unique to the ISO 15765 protocol standards. For the purpose of demonstration, test setups incorporating actual Electronic Control Units (ECUs) linked to a CAN bus were employed. Further experiments were performed using a fully equipped cab assembly from a 2018 Freightliner Cascadia truck, set up as a testing environment. The experimental outcomes demonstrate how attacks targeting these specific protocols can undermine the integrity of individual ECUs, leading to denial of service. Additionally, within the Freightliner Cascadia configuration, a network architecture typical of contemporary vehicles was observed, featuring a gateway unit that isolates internal ECUs from diagnostic interfaces. Although this gateway is engineered to prevent conventional message injection and spoofing attacks, it permits all diagnostic communications. This selective permeability inadvertently introduces a susceptibility to diagnostic protocol flaws, highlighting an essential area for security improvements within commercial vehicle networks. These insights are vital for engineers and developers tasked with integrating the diagnostic protocols into their network subsystems, underscoring the urgency for improved security provisions.

Electric vehicles (EVs) are increasingly central to sustainable mobility, but braking control remains a safety-critical challenge. EVs must balance regenerative braking, which recovers kinetic energy, with dynamic braking, which ensures rapid deceleration in emergency situations. At the same time, driver drowsiness contributes to nearly one-fifth of serious accidents worldwide, underscoring the importance of systems that respond to external conditions and human states. This study presents a Python-programmed electronic control unit (ECU) on a Raspberry Pi Pico, integrating real-time driver drowsiness detection with adaptive braking control. Inputs from ultrasonic sensors, wheel encoders, and a camera-based drowsiness detection module are transmitted via the Message Queuing Telemetry Transport (MQTT) protocol. At the same time, a fuzzy inference engine processes driver condition, vehicle speed, and obstacle distance to generate proportional pulse width modulation (PWM) signals for motor braking. Experimental validation using a laboratory-scale prototype demonstrated distinct braking profiles under three conditions: slightly drowsy states produced proportional speed reductions as early warnings, drowsy states resulted in smooth full stops with consistent deceleration between 0.042 and 0.050 m/s², and emergency braking delivered rapid stops with shorter distances of 0.116 to 0.204 meters. While a 1-second latency was observed in some slightly drowsy runs, the system consistently adapted braking behavior and restored regular operation when drowsiness signals ceased. These findings validate that a Micro-Python-based ECU can reliably integrate behavioral monitoring with adaptive braking, offering a low-cost, scalable solution for future EV safety systems. Received: 30 August 2025 | Revised: 15 December 2025 | Accepted: 3 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Kalaivanan Kumaran: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Muhammad Nadzmi Razlan: Software, Validation, Investigation, Data curation, Writing – review & editing. Ahmad Safwan Abd Shukor: Software, Validation, Investigation, Data curation, Writing – review & editing. Mohamad Tarmizi Abu Seman: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration.

Pothole detection and mitigation systems are integral to enhancing road safety and optimizing vehicle performance. By leveraging Convolutional Neural Networks (CNN) and Internet of Things (IoT) technologies, these systems enable real-time hazard identification, such as potholes, and facilitate dynamic vehicle adjustments to mitigate potential damage. This study analyzed over 1,000 pothole images, with the CNN model achieving an accuracy rate of 96% to 99%. Edge detection techniques, including Sobel filters, were utilized to assess key pothole attributes such as diameter, depth, and edge sharpness. For potholes with diameters exceeding 50 cm and edge sharpness above 85%, the vehicle's suspension damping was automatically increased by 40%, minimizing the impact on the vehicle's chassis. Additionally, the system dynamically reduced vehicle speed by 10–20 km/h for severe potholes, based on real-time analysis by the Electronic Control Unit (ECU). The ECU also communicated with the Anti-lock Braking System (ABS) to apply braking force when sharp-edged potholes were detected. In scenarios where rear vehicles maintained a safe distance of 50 meters, the braking system was activated, reducing the risk of tire damage and collisions. Through IoT integration, real-time data was stored in the cloud, enabling predictive maintenance and improving repair planning efficiency by 30%. This approach not only enhances passenger safety but also reduces vehicle wear and tear, while improving road infrastructure management efficiency. The combination of CNN and IoT-based solutions marks a significant advancement in automotive safety systems.

In this article, we experimentally evaluate three flash and test strategies of automotive ECUs during serial production. These strategies are: (i) a test-oriented firmware in Virgin Mode, (ii) a dual-bank parallel flash strategy, and (iii) a test that is implemented in the production firmware. The latter could not be evaluated experimentally due to cybersecurity concerns. In this section, we employ 506 flashing cycles (actual production cycles and synthetic cycles from an independent reproduction), and report process time, process jitter, and p-value. In particular, we find that the dual-bank scheme provides the best performance with a mean flashing time of 23.3 s which is a 45% improvement over Virgin Mode. The variance was reduced by a factor of 25, showing that the reduced cycle times were appropriate for a takt time sensitive environment. All differences were statistically important (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup>). The data can be used as a basis to aid the decision making on flashing strategies in the automotive industry, and to define the next generation of ECU manufacturing processes considering process efficiency, cyber security limitations, and EoL procedures robustness.

This study introduces an electronically and mechanically controlled force balancing system designed to ensure safe and stable operation of agricultural machinery on sloped terrain. Equipment attached to the rear of tractors and construction vehicles often disrupts the load distribution, particularly on inclines, increasing the risk of front-end lift and rollover. The proposed mechanism continuously monitors the dynamic load distribution via weight sensors and accelerometers integrated into the front axle, enabling real-time analysis and response. When the measured load falls below predefined thresholds, the system automatically adjusts the hydraulic tank volume to restore the vehicles center of gravity. This process is managed by an electronic control unit (ECU) and can be operated either automatically or manually, significantly enhancing vehicle stability and operational safety. The modular and vehicle-independent design allows seamless integration with various tractor and machinery models, while the multifunctional tank structure-serving as a tool compartment, auxiliary oil reservoir and fuel tank offers practical advantages under field conditions. This innovative approach provides a scalable, sustainable and high-safety solution to mechanical balance challenges in digitalized agricultural operations.

Abstract - Regenerative braking systems play a vital role in improving the energy efficiency of modern electric and hybrid vehicles by recovering kinetic energy during deceleration. This project focuses on the design and development of a regenerative braking system that converts the kinetic energy of a moving vehicle into electrical energy, which is otherwise dissipated as heat in conventional braking systems. An electric motor is utilized to operate in dual modes, functioning as a drive motor during propulsion and as a generator during braking. When braking is applied, the motor generates electrical energy from wheel rotation and transfers it to an energy storage unit such as a battery or capacitor through power electronic converters. An electronic control unit ensures smooth transition between driving and braking modes while maintaining effective deceleration and system safety. The integration of regenerative braking with conventional friction brakes provides reliable stopping performance under all operating conditions. The developed system enhances overall energy utilization, reduces mechanical brake wear, and extends the operational range of electric vehicles. This study demonstrates the potential of regenerative braking technology to contribute significantly to sustainable transportation and efficient energy management in automotive and electromechanical applications. Key Words: Regenerative Braking System, Energy Recovery, Electric Motor Generator, Energy Storage System, Power Electronics, Electric Vehicles, Sustainable Transportation

In vehicle dynamics, sideslip angle is a key parameter and it is required by Electronic Control Units to activate Electronic Stability Control systems. Thus, accurate estimation of the sideslip angle is essential for preventing accidents or mitigating their severity. However, passenger vehicles do not feature dedicated sensors specifically designed to measure sideslip, and they rely instead on estimations derived from other onboard sensors. It is then reasonable to foresee that a direct measurement of the sideslip angle could significantly improve Electronic Stability Control systems and general safety in road transport. This work proposes a novel low-cost method to directly measure the lateral velocity of a vehicle and its sideslip angle. More specifically, the performance of a standard computer mouse optical sensor is experimentally evaluated in comparison with a high-end optical flow sensor specifically designed for vehicle dynamics research. The proposed solution is tested on a 1:5-scale vehicle and on a passenger car. The results demonstrate that the inexpensive, off-the-shelf mouse sensor performs similarly to the specialized high-end sensor. Given this promising result, more research in this field is suggested.

Modern agriculture is becoming less dependent on traditional mechanical systems with the introduction of intelligent control systems. These systems utilize adaptive algorithms and neural networks to optimize the operation of engines, batteries, and hydraulic systems under variable loads and complex operating conditions. ( Research purpose ) To develop an electronic control system for a series hybrid tractor equipped with in-wheel motors. ( Materials and methods ) The study involved an analysis of the hybrid tractor components, including the diesel generator, traction battery, in-wheel motors, and electronic control units. The system architecture is based on microprocessor modules with feedback mechanisms (primarily using the CAN bus for data exchange). Control algorithm models were developed to ensure precise and adaptive system performance under dynamic operating conditions. ( Results and discussion ) The implementation of adaptive algorithms increases the efficiency of the diesel engine-generator by 42%, reduces fuel consumption by 12-15%, and decreases CO₂ emissions by 15%. The precision hydraulic and braking systems shorten the braking distance by 8–12%, while neural networks enable prediction of braking parameters with up to 95% accuracy. In addition, the upgraded battery systems maintain stable operation across a wide temperature range from 0 to 100°C, contributing to the durability and reliability of the equipment. These results confirm the potential of intelligent control systems to enhance both the efficiency and environmental sustainability of agricultural machinery. (Conclusions) The developed electronic control system optimizes the performance of the components in a series hybrid tractor with in-wheel motors, enabling adaptive and precise realtime regulation of operational parameters. It improves enhances the tractor's maneuverability and operational safety under varying field conditions. Ultimately, it leads to a longer component lifespan and improved overall productivity.

This article examines the specifics of welding the casework of power engineering equipment characterized by a pronounced discrete nature and inherent challenges of small-scale production such as low process reproducibility and a high degree of uncertainty in component condition. Pre-production costs are comparable to or exceed the costs of the actual process. This article considers the process properties of power sources and welding machines in a single "power source – welding machine – arc – weld" system. The process properties are separately assessed in steady-state and dynamic modes of equipment operation. In steady-state mode, the process properties of both power sources and welding machines are quite high and almost always meet welding technology requirements. Therefore, when evaluating equipment properties, its ability to handle process disturbances is most often examined. A transient analysis is performed based on the dynamic characteristics of welding circuit elements. For conventional power sources containing electromechanical controllers, the dynamic properties are determined by inductance and design features, while for semiconductor power sources, they are determined by the characteristics of the electronic control unit. The paper concludes that successfully designing a control system for an electric arc welding circuit depends on many factors, primarily the process's information base for the high-quality generation of the input state vector. The main conclusion reached is that creating a modern control system is impossible without the support of information and digital analytical systems for recording primary data sets and subsequently extracting qualitatively new information about the dynamics of the electric arc welding process from them, ensuring a comprehensive approach to improvement encompassing the entire welding production cycle.

As Advanced Driver Assistance Systems continue to evolve rapidly, more sophisticated methods for continuous assessment of driver cognitive and physical states are essential to address safety risks during the transition toward vehicle autonomy. Adaptive neural embedded systems represent a transformative approach to real-time driver monitoring, combining multiple sensor types with edge-deployed artificial intelligence to create personalized safety models. The architecture integrates Convolutional Neural Networks for extracting spatial features and Long Short-Term Memory networks for identifying temporal patterns, processing facial images, physiological signals, and behavioral indicators to detect fatigue and distraction states. Implementation on embedded FPGA-System-on-Chip platforms achieves sub-second inference latency while maintaining high classification accuracy across diverse operational environments. Baseline calibration procedures establish driver-specific detection thresholds that account for individual physiological differences, while ongoing model refinement through incremental learning adapts to changing behavioral patterns over extended operation periods. Direct integration with Electronic Control Units enables graduated intervention strategies ranging from gentle sensory alerts to active vehicle control inputs, including lane-centering assistance and controlled braking. Transfer learning techniques accelerate model development by leveraging pre-trained representations, achieving exceptional precision and recall metrics through fine-tuning on domain-specific drowsiness datasets. The embedded architecture addresses fundamental limitations of cloud-dependent systems, including latency constraints, connectivity dependence, and privacy concerns, delivering deterministic real-time performance essential for safety-critical automotive applications in increasingly autonomous vehicular environments.

Polymer-infused porous substances are lightweight porous materials, utilized in both internal and external elements, panels, electronic control units, and automotive suspension bushing components, thus paving the way for the rise of smarter, more efficient, and eco-friendly production techniques. The polylactic acid (PLA)/poly(butylene adipate-co-terephthalate) (PBAT) represents a biodegradable polymer blend characterized by a bio-based composition, positioning it as a viable alternative to traditional petroleum-derived polymers. The inclusion of PBAT enhances the tear resistance of PLA while simultaneously mitigating its rigidity; therefore, nano material is incorporated as a filler within the composite blend to augment its properties. In this study, 35 wt% and 39 wt% of PBAT and 1 wt% and 5 wt% of graphene nanoplatelets (GNPs) are blended with PLA, and utilizing a twin-screw extruder, two different composite filaments are generated. Subsequently, six distinct composites are printed from the composite filaments employing the fused deposition modeling three-dimensional (3D) printing method by varying the infill densities (35%, 70%, and 100%). Investigations into the sound absorption characteristics and low-velocity impact performance of 3D printed composites are undertaken. Acoustic analysis of the printed composites is performed utilizing an acoustic testing apparatus, and the sound absorption coefficient (SAC) is computed employing MATLAB software. The composite with 35% infill density and 1 wt% GNP registered the highest values in SAC at low- and mid-frequency ranges. Whereas, the composite with 100% infill density and 1 wt% GNP registered the maximum SAC at the high-frequency range. In the low-velocity impact assessments, the composite with 1 wt% GNP markedly enhances the impact resistance, reflecting better energy absorption, stress transfer, and dissipation.

This paper presents a smart detection system designed to identify unauthorized removal of car wheels, particularly high-value alloy wheels. The system utilizes existing onboard sensors and vehicle electronics to detect theft attempts in real time. It operates through a three-stage process: (1) Angular motion detection using accelerometer and gyroscope data embedded in the vehicle’s Electronic Control Unit (ECU) or stability control system to monitor abnormal tilt—specifically when one side of the vehicle is lifted beyond a threshold of 10-15 degrees; (2) Wheel presence verification via Tire Pressure Monitoring System (TPMS) sensors to identify wheel detachment and check the TPMS value; and (3) Key proximity check, which verifies whether the authorized key fob is near the vehicle. If the key is absent during a suspected theft event, the system confirms unauthorized access. Upon validating these conditions, the system can trigger a tamper-resistant alarm through ECU communication, even if the primary alarm system has been disabled. This multi-layered detection strategy significantly enhances vehicle security by combining motion analysis, wheel monitoring, and key authentication. Unlike conventional mechanical locks or pattern-based lug nuts, which are vulnerable to bypassing techniques, this integrated system offers robust, real-time protection through sensor fusion and ECU communication. The proposed solution minimizes false alarms, improves theft deterrence, and reduces economic losses associated with wheel theft.

Adhesion utilisation strongly affects braking safety and anti-slip performance. However, rail vehicles usually lack onboard torque sensors or instrumented wheelsets, preventing real-time measurement of the wheel-rail adhesion coefficient and limiting dynamic adjustment of braking/anti-slip control and braking safety distances. This study proposes an online adhesion estimation method based on wheel dynamic equations combined with a negative-gradient iterative algorithm. The approach employs the Laplace transform and convolution theory and embeds a first-order filter to improve numerical convergence and noise robustness. Simulations under dry, low-adhesion, ultra-low-adhesion and sudden adhesion change conditions demonstrate that the estimator achieves accurate and adaptive real-time adhesion tracking. The algorithm is then deployed on a brake electronic control unit (BECU) and assessed on a hardware-in-the-loop (HIL) test bench incorporating a real pneumatic braking system. Compared with conventional static offline methods, the proposed estimator enables continuous, real-time adhesion monitoring for each axle and provides a basis for optimising braking and anti-slip strategies and dynamically updating braking safety distances in signalling systems to enhance operational safety.

This paper addresses the challenge of ensuring quality control for electronic control units (ECUs) in the automotive industry. Traditional hardware-in-the-loop (HIL) systems, used for software quality assurance, are costly and not always readily available, leading to potential delays in software verification and release. This paper investigates the factors causing these delays and proposes process task workflow optimization using a hybrid testing approach. This approach allows software developers to conduct early integration and system testing for embedded software, complementing the formal validation stage. To support this, the paper presents a novel piece of test equipment called TestBench, which utilizes inexpensive embedded hardware like the Arduino Mega2560, which can carry out the fundamental tasks needed to test the ECU. TestBench enables local testing, remote accessibility, and the monitoring of key parameters, including voltage current consumption, and the communication bus. It also facilitates fault injection for the evaluation of communication protocol robustness. By enabling earlier and more frequent testing, TestBench aims to enhance the quality of software developer outputs and the overall quality of ECU-embedded software. The system has the potential to significantly improve the testing process, making advanced testing capabilities more accessible and cost-effective for engineering teams and educational institutions.