Browse Topic: Electrical, Electronics, and Avionics

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CONNECTOR RECEPTACLE, BAYONET COUPLING, IN LINEAS95234/1A (Current)To be published on 06/01/2103
AE-8C1 Connectors Committee
Characterisation of Brake Wear PM2.5 and PN10 Emissions Across Pad/Disc Technologies, Vehicle Types and Braking Behaviours, Under Real World Conditions2026-24-0022To be published on 09/21/2026
This paper summarises targeted brake emissions investigations undertaken as part of the Department for Transport’s Non Exhaust Emissions programme. The work was carried out to improve understanding of particulate mass and particle number emissions from friction braking and to assess the influence of component selection, vehicle technology, operating conditions, and control measures on those emissions. Starting from a pre-developed brake enclosure and sampling system methodology, these were further refined to improve airflow management, reduce leakage, and minimise sampling artefacts. The updated system was integrated with a suite of particulate measurement instruments, including MPEC (hot and cold), APC10, DMS500, and eFilter, enabling simultaneous measurement of volatile and non volatile PN10 plus PM2.5. A matrix of nine brake pad formulations and two disc types was tested on a common C segment vehicle platform across chassis dyno and on road cycles, together with controlled braking
Andersson, Jon
Simulation-Based Development and Evaluation of Heat-Quantity-Based Thermal Operating Strategies for Automotive Applications2026-24-0012To be published on 09/21/2026
Efficient and robust thermal management is a key prerequisite for reproducible vehicle testing and energy-efficient powertrain operation. Conventional thermal operating strategies are commonly based on temperature setpoints and often neglect the dynamic relationship between heat input, system inertia and transient operating conditions, leading to increased energy demand and limited reproducibility under highly dynamic driving cycles. This paper presents a simulation-based development and evaluation of a heat-quantity-based thermal operating strategy for automotive applications. Instead of controlling the system solely via temperature targets, the proposed approach utilizes accumulated and instantaneous heat input as central decision variables for thermal control, explicitly accounting for thermal system dynamics. The investigation is conducted using a detailed vehicle simulation model including a physically-based thermal system representation. An innovative decentralized thermal
Stenger, Erik, Fiore, Luis, Weimer, Niko, Beidl, Christian
Adaptive Neural Network Fuel Mass Correction for Structural Model Error Compensation in Diesel Engines with Variable Biodiesel Properties2026-24-0023To be published on 09/21/2026
Compression-ignition engines operating with biodiesel blends often exhibit variability in fuel properties, such as density, viscosity, and cetane number, which can lead to systematic deviations in injected fuel mass when using conventional physics-based models. These deviations can reduce combustion efficiency and increase brake-specific fuel consumption (BSFC). This study proposes a lightweight neural network–based approach to compensate for structural errors in baseline injection models, using a single-layer perceptron trained on the relative error (delta) between actual and modeled injected mass. By normalizing engine and fuel parameters and introducing a small amount of measurement noise, the network learns to predict a corrective factor that adapts the injected mass to match the desired target under varying fuel conditions. Simulation results demonstrate that the neural correction significantly reduces systematic bias: in test cases with intentionally introduced structural error
Gutierrez, Marcos, Taco, Diana, Sampietro-Saquicela, Jose, Valencia-Ortiz, Nakira, Rodas, Federico
Cost and Emission aware strategic optimization: a fleet-level tool to support scenario-oriented design2026-24-0034To be published on 09/21/2026
The transition toward low-emission transport systems requires not only technologically optimized Battery Electric Vehicles (BEVs) but also integrated methodologies capable of supporting industrial stakeholders throughout the deployment phase. In particular, for logistics operators, fleet sizing and charging infrastructure planning are tightly coupled with vehicle configuration and mission scheduling. Therefore, decision-support tools are required to minimize total operational costs and environmental impact while ensuring service continuity. Building upon a previously developed two-level BEV design framework, this work introduces a higher-level optimization tool aimed at extending powertrain design outcomes toward fleet-level decision-making, providing an integrated methodology capable of determining not only the optimal vehicle configuration but also the optimal number of vehicles and charging stations required to satisfy operational scheduling constraints. The proposed tool performs
Bartolucci, Lorenzo, Cennamo, Edoardo, Cordiner, Stefano, Donnini, Marco, Grattarola, Federico, Lombardi, Simone, Mulone, Vincenzo, Tribioli, Laura
Computational Electro-Thermal and Degradation Modeling Framework for Lithium-Ion Batteries in Electric Vehicles2026-24-0011To be published on 09/21/2026
This paper presents a mathematical modeling framework for lithium-ion batteries used in electric vehicles (EVs). The model is implemented using Python. At the cell level, the model describes charge and discharge behavior, state of charge (SOC), terminal voltage, internal resistance losses, and heat generation. An energy balance equation is used to estimate temperature variation during operation. Temperature-dependent resistance and capacity are included to represent nonlinear battery behavior under different load conditions. At the system level, feedback relationships between SOC, temperature, state of health (SOH), and degradation rate are modeled using system dynamics. Battery aging is represented through mathematical functions that relate capacity loss to temperature and usage cycles. This allows simulation of long-term performance under different driving scenarios. The model enables parametric and sensitivity analyses to evaluate the effects of discharge rate, ambient temperature
Gutierrez, Marcos
Real-Time Road Intelligence through a Hazard Authentication Model2026-24-0027To be published on 09/21/2026
Advanced Driver Assistance Systems (ADAS) are evolving beyond onboard perception. The ability to dynamically map and share temporary road hazards is important for connected and autonomous driving, but authenticating the data is critical. An in-vehicle hazard recognition layer performs real-time video analysis and geotagging on embedded platforms. Real-time video is analyzed for road hazards—such as potholes, construction zones, waterlogging, fallen trees, roadside accidents, and debris—using onboard cameras and lightweight computer vision models. This metadata is sent to a cloud-based aggregation layer to validate hazard reports. It is of paramount importance to validate hazard reports originating from diverse sources, regardless of their accuracy. This paper presents a mathematical approach to determine an overall Hazard Confidence Score (HCS) based on data received from diverse sources. A unique hazard authentication model is introduced to quantify the credibility of each hazard
Bose, Souvik
Comparative Analysis of BEV Performance from Simulated Environment and Physical Hardware Platform2026-24-0005To be published on 09/21/2026
The proliferation of simulation environments has accelerated technological progress across various scientific domains by offering a cost-effective and time-efficient framework for data acquisition and analysis. In the automotive sector, high-fidelity modeling of vehicle components and driving scenarios bypasses the logistical constraints associated with hardware procurement and the intensive requirements of large-scale testing infrastructures. However, pre-calibrated or native software models often imply simplified hypotheses, missing relevant aspects of the entire powertrain-to-wheel energy chain. This study presents a comparative analysis of battery performance within a battery electric vehicle (BEV) by synchronizing virtual simulations with experimental hardware at the test bench. The methodology involves the concurrent modeling of the driving environment, the vehicle chassis, and the propulsion system, followed by the execution of identical driving cycles on a physical platform
Sequino, Luigi, Sementa, Paolo, Altieri, Nunzio, Vaglieco, Bianca Maria, Sorrentino, Chiara
Experimental Comparison Between Real and Simulated Battery Pack Efficiency in Light Quadricycles Using a Custom Inertia Dynamometer2026-24-0008To be published on 09/21/2026
This study compares the energy efficiency of a real battery pack with that of a simulated battery pack using a hardware battery simulator, through experimental testing on a dedicated inertia dynamometer designed for light quadricycles. The real battery pack under investigation consists of lithium-ion cells with a nominal voltage of 48 V, a nominal capacity of 100 Ah, and a total energy content of 4.8 kWh. The pack is equipped with a Battery Management System (BMS) that monitors voltage, current, temperature, and State of Charge (SOC). The battery simulator was configured to replicate the same electrical specifications, including the OCV–SOC curve, internal resistance, and current limits, enabling a direct comparison between theoretical and experimental behavior. Experimental tests were conducted on a custom inertia test bench developed to overcome the limitations of conventional chassis dynamometers when testing light quadricycles, characterized by low mass, limited power output, and
Sementa, Paolo, Vaglieco, Bianca Maria, Altieri, Nunzio
EKF-Based Machine Learning Approach for Battery SOH Prediction in Commercial Vehicles Fleet2026-24-0002To be published on 09/21/2026
Estimating battery State of Health (SOH) from field data is essential to ensure successful operation and to increase the uptime of battery electric vehicles (BEVs). Most studies in the literature have proposed several methods for battery SOH estimation, relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world driving data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, have been collected across approximately 16 months of operation from 10 electric commercial vehicles. During this period, a battery degradation of up to 2.5 - 3% is observed within the fleet. Firstly, a model-based framework is introduced, in which a battery second-order Equivalent Circuit Model (ECM) has been coupled with an Extended Kalman Filter (EKF) to estimate the battery SOH. The results confirmed that the EKF accurately captures the
D'Agostino, Valerio, Pulvirenti, Luca, Shanker, Anirudh, Cardone, Massimo, Rizzoni, Giorgio, Vitale, Francesco
Neural-Network-Enhanced Air-Standard Thermodynamic Modeling for Spark-Ignition Engines Fueled with Low Heating Value Alternatives2026-24-0016To be published on 09/21/2026
This study presents a computational framework that integrates an air-standard thermodynamic engine model with artificial neural networks to predict the performance of spark-ignition (SI) engines operating with alternative fuels of reduced lower heating value (LHV). A deterministic thermodynamic simulator was developed in Excel, incorporating engine geometric parameters (compression ratio, bore, stroke, displacement), operating speed, and fuel properties, with particular emphasis on LHV as the dominant energetic descriptor. The model computes in-cylinder states, thermal efficiency, indicated mean effective pressure, and power output under idealized air-standard assumptions. To extend predictive capability beyond fixed-parameter analyses, a feedforward neural network was trained using datasets generated from systematic parametric sweeps of engine geometry, speed, and fuel LHV. The neural network captures nonlinear interactions between compression ratio, combustion energy release, and
Gutierrez, Marcos, Taco, Diana, Sampietro-Saquicela, Jose, Valencia-Ortiz, Nakira, Rodas, Federico
Life Cycle Assessment of Greenhouse Gas Emissions from a Plug-in Hybrid Electric Vehicle with Different Powertrain Configurations in Europe2026-24-0019To be published on 09/21/2026
Plug in hybrid electric vehicles play an important role in transportation decarbonization. Compared with battery electric vehicles, plug in hybrid electric vehicles generally have a lower production carbon footprint due to their smaller batteries, which require far less raw material. Despite their smaller capacity, these batteries are typically sufficient to cover most daily travel distances in pure electric mode. The hybrid powertrain can be configured in multiple ways depending on the number and position of electric machines within the driveline. These configuration differences significantly influence both the total carbon footprint and the use phase greenhouse gas emissions. In this study, we evaluate the life cycle greenhouse gas emissions of a plug-in hybrid electric vehicle with various powertrain configurations in the European context. All configurations share the same premium mid-size sport utility vehicle glider. Battery capacity ranges from 20 kWh to 45 kWh, enabling an
Nguyen, Duc-Khanh, Andersson, Simon, Kristoffersson, Annika
How micromobility is reshaping urban mobility: a systematic literature review2026-24-0025To be published on 09/21/2026
Micromobility is rapidly reshaping urban mobility by transforming travel behaviour, urban space, and transport systems. Its growing role in reducing car dependency and supporting low-carbon mobility has positioned cycling, e-scooters, and e-bikes as key components of sustainable urban transport. The aim of the study is to investigate how micromobility is reshaping urban mobility, particularly travel behaviour, urban mobility policy, urban infrastructure, and public transport integration. This study employs a systematic literature review (SLR) to provide a structured and transparent framework for synthesising existing research, identifying trends, and mapping knowledge gaps across multiple dimensions of urban mobility[MO1.1]. It combines inductive and deductive reasoning to ensure the review is both theoretically grounded and responsive to emerging patterns in the literature. The findings reveal that the most impactful areas of micromobility development are demand and changes in travel
Olkhova, Mariia, Comi, Antonio
A Framework for Co-Developing Generative AI and Functional Safety in ADAS2026-24-0028To be published on 09/21/2026
The convergence of Generative AI (GenAI) and Functional Safety (FuSa) under ISO 26262 is reshaping the landscape of Advanced Driver Assistance Systems (ADAS) by integrating intelligence with reliability. GenAI revolutionizes perception, prediction, and decision-making in dynamic environments, enabling vehicles to navigate complex real-world scenarios with unprecedented adaptability. However, its probabilistic nature, lack of transparency, and vulnerability to edge-case failures pose significant challenges in ensuring compliance with functional safety standards. This paper explores how GenAI enhances object detection, pedestrian prediction, and risk assessment, ultimately reducing accident rates and fostering safer autonomous operations. Furthermore, GenAI-driven synthetic data generation accelerates ADAS validation, allowing comprehensive testing of safety-critical scenarios beyond conventional datasets. However, integrating GenAI into safety-compliant systems requires overcoming
Jain, Yesha
Business approach to cope with automotive market uncertainty2026-24-0024To be published on 09/21/2026
The global automotive industry is facing an unprecedented convergence of uncertainties driven by geopolitical tensions, evolving trade policies, emissions related regulations, and increasingly volatile consumer demand. Shifting emissions legislation, including the EU’s tightened CO2 targets and long term plans to phase out internal combustion engines, is imposing strategic and financial pressures on automakers and suppliers as they navigate divergent regional regulatory trajectories. Demand side volatility further complicates the landscape. Consumer preferences are fluctuating due to economic pressures, infrastructure constraints, and uneven EV adoption patterns. While some markets show stagnation in battery electric vehicle uptake, hybrids are rising as consumers seek cost efficient alternatives amid uncertain energy and regulatory environments. Within this unstable context, the transition toward Software Defined Vehicles (SDVs) is emerging as a critical strategic response. SDVs
Cavanna, Filippo, Potenza, Luca
Fuel Cell Electric Vehicle-to-Home Strategy for Residential Power Supply Continuity and Load Management2026-24-0026To be published on 09/21/2026
Power supply continuity for household appliances during high-demand peaks or periods of low solar generation remains a critical challenge for modern residential energy systems. Hydrogen-powered Fuel Cell Electric Vehicles (FCEVs) offer a compelling solution by extending their role from sustainable mobility to versatile mobile energy storage units. This study investigates the Vehicle-to-Home (V2H) operation of a high-performance fuel cell vehicle designed to provide backup power and peak-shaving capabilities in a domestic environment during high-demand or grid overload conditions. To evaluate this integration, a comprehensive MATLAB/Simulink simulation model was developed for a residential microgrid. The system comprises a photovoltaic array, a parallel battery energy storage system, and a Fuel Cell Electric Vehicle modeled according to the technical specifications of the Toyota Mirai, equipped with a high-efficiency 128 kW fuel cell stack (330 cells) and a dedicated 5.6 kg hydrogen
Federici, Leonardo, Pistritto, Antonino, Cozzolino, Raffaello
Multi-sensor Data Fusion Based on Big Data Modelling2026-99-0568To be published on 07/10/2026
The collection of road high-frequency data often involves inputs from multiple sensors, such as stress and strain, and sampling of these data features a high sampling rate of up to 2,000 Hz. High-frequency sampling enables capturing of the internal stress and strain of the pavements when vehicles are passing and facilitates the analysis of the pavement structure and prediction of its long-term service performance. However, while the sensors are continuously collecting data, the time the vehicles pass is discrete and unpredictable, resulting in a large number of low information density or irrelevant data. Even when the massive high-frequency data are collected, challenges remain in data transmission, storage, and analysis—the challenges are attributable not only to the massive quantity and complexity of data from multiple sensors, but also to the inconsistent data formats, misaligned timestamps, and multi-sensor data fusion difficulties. In response to the challenges specified above, a
Gang, Jian, Zhang, Yue, Chen, Yinghao, Zheng, Xiaoyan, Wang, Taojie, Liu, Yilin, Guan, Wei, Wu, Jiangfeng
Traffic Flow Prediction System Based on Deep Learning2026-99-0549To be published on 07/10/2026
Traffic flow prediction is of great significance for improving the operation efficiency of the transportation system, optimizing travel experience and reducing traffic congestion. Traditional traffic flow prediction methods are difficult to capture the spatio-temporal nonlinear characteristics of traffic flow due to its simple model and insufficient feature extraction ability. Therefore, an intelligent traffic flow prediction system based on deep learning is proposed, constructs a deep learning model based on graph convolution and fusion of attention mechanism LSTM. Based on this, a traffic flow prediction system is implemented. Experiments show that, on the PeMSD4 and PeMSD4 datasets, the error of the model in RMSE and Mae indicators is significantly reduced compared with the traditional methods, which provides an efficient solution for traffic flow prediction and congestion analysis, and has both theoretical innovation and engineering practical value.
Tang, Zhan, Lu, Xiaoyu, Yang, Nian, Xiang, Xiaohong, Hou, Xiang, Peng, Xiaoli
Temperature Influence Analysis and Compensation Method of Hemisphere Resonator Gyroscope2026-99-0514To be published on 07/10/2026
Hemisphere resonant gyroscope (HRG) is a new type of vibration gyroscope with high precision, high reliability, and long lifespan. Improving the temperature stability of a hemispherical resonant gyroscope (HRG) has profound implications for navigation and guidance systems as well as airborne sensor technology. By optimizing temperature compensation algorithms or improving material thermal properties, the angular velocity measurement error caused by temperature drift can be significantly reduced, thereby improving the long-term positioning reliability of navigation systems in extreme temperature fluctuation scenarios. This article starts with the structure of the hemispherical resonant gyroscope, studies the temperature characteristics of the hemispherical resonator through formula theory, verifies and analyzes the temperature characteristics of the hemispherical resonant gyroscope through experiments, and designs a temperature compensation scheme. Through experimental data analysis
Wang, Jiachen, Chen, Pu, Yao, Zhiqiang, Zhang, Yi, Bai, Fan
Optimizing Autonomous Obstacle Avoidance with PON Architecture and SDN-Based Dynamic Scheduling2026-99-0592To be published on 07/10/2026
This study addresses the challenges of communication delays and system stability in autonomous obstacle avoidance (AOA) systems under next-generation vehicular electronic/electrical architectures. A centralized PON-based architecture is proposed, leveraging XGSPON technology to enhance bandwidth capacity and reduce electromagnetic interference, while rigorously analyzing worst-case in-vehicle communication (IVOC) delays. To mitigate latency impacts, a Software-Defined Networking (SDN)-driven dynamic scheduling strategy prioritizes safety-critical data streams (e.g., environmental perception, motion control) through adaptive resource allocation. Further integrated with a robust H-infinity LQR controller, the co-design framework ensures precise trajectory tracking and suppresses steering oscillations under communication uncertainties. Simulation tests validate the framework's efficacy, demonstrating significant reductions in loop delays and improved dynamic stability in complex scenarios
Wang, Wenwei, Han, Muchen, Cao, Wanke
Aircraft Noise Event Identification Technology Based on Single Acoustic Vector Sensor2026-99-0591To be published on 07/10/2026
With the rapid development of China’s civil aviation industry, the problem of airport noise has attracted widespread social attention. The requirement for the real-time monitoring and evaluation of acoustic environment around airports is becoming more and more intense. The identification of aircraft noise events in the complex acoustic environment surrounding the airport is the most critical technical problem in airport noise monitoring. However, the traditional noise source identification technology is difficult to be widely used in real-time monitoring system due to its large errors and complex deployment conditions. This paper presented an aircraft noise source identification technique based on a single acoustic vector sensor. The azimuth parameters of the noise source were estimated by the three-dimensional spatial positioning algorithm of sound pressure and particle vibration velocity combined with information processing, and the three-dimensional footprint of the noise event in
Hou, Jiayu, He, Tianlun, Zhu, Lin, Chen, Ying, Liu, Yinhui, Lv, Lei, Wang, Yuhao, Chen, Da
Ship Tunnel Navigation Evacuation Simulation and Evacuation Capability Evaluation2026-99-0572To be published on 07/10/2026
In order to solve the ship emergencies that may occur in the process of tunnel navigation, the tunnel pontoon-type bank wall evacuation channel proposed in a large navigation building is taken as the research object. Based on Pathfinder evacuation software, a numerical model of pedestrian evacuation for 500 passenger ships in emergency situations such as fire in the navigation tunnel is established, and the evacuation simulation analysis and evacuation ability evaluation are completed. The analysis shows that the emergency evacuation time of personnel is at least about 21 minutes, and the bottleneck of emergency evacuation equipment for personnel in the navigation tunnel is at the entrance of the pontoon escape. The results provide guidance and suggestions for the design optimization of the evacuation channel of the tunnel bank wall in the later period.
Tao, Ran, Li, Ran, Tang, Weibi, Hu, Zhifang, Qin, Pan
A Review of UAV Support Technologies for Low-Altitude Airspace Operation and Management2026-99-0597To be published on 07/10/2026
In recent years, with the low-altitude economy developing rapidly, the operation and management of low-altitude airspace has gradually become a hot topic. Unmanned aerial vehicles (UAVs) constitute a fundamental component of the low-altitude airspace ecosystem, significantly influencing its structure and functionality. The technological advancement of UAVs has fundamentally transformed the operational paradigm for low-altitude airspace management. This paper presents a comprehensive review of UAV-supported technologies in the context of low-altitude airspace operations and management. It systematically analyzes key technologies and applications of UAVs in areas such as airspace capacity and safety assessment, trajectory planning, and standardized flight management. Drawing from kinematic analysis and traffic flow theory, UAV density control and collision risk prediction offer quantitative insights into airspace capacity evaluation. Additionally, probabilistic analysis and simulation
Gong, Lei, Ma, Zhenxiao, Luo, Qin
Missile Pose Measurement Based on ArUco Markers2026-99-0576To be published on 07/10/2026
This paper presents a monocular vision-based system for high-precision missile pose measurement using ArUco markers and Perspective-n-Point (PnP) algorithms. By deploying 6 × 6 ArUco markers on a cylindrical missile mock-up, the system establishes 3D-2D correspondences between structured-light-scanned models and camera images to solve the PnP problem. The proposed approach integrates optimized ArUco marker recognition — leveraging adaptive thresholding, contour simplification, and grid-based validation — with the Efficient PnP (EPnP) algorithm to achieve real-time pose estimation. Experimental validation demonstrates angular accuracy of ± 0.3° in roll/pitch/yaw and positional accuracy of ± 2 mm within a 2 m range under controlled conditions. The system exhibits robustness against partial occlusions and motion blur, with degraded performance (± 1.2°, ± 5 mm) in extreme scenarios. Key innovations include a streamlined marker detection pipeline and adaptive pose refinement using Levenberg
Wang, Ruiyang, Zhang, Chaofan
Review of Power Management Technology for Turbine Hybrid Electric Propulsion Systems2026-99-0531To be published on 07/10/2026
The turbine hybrid electric propulsion system is an important form of green aviation. Unlike the single form of aviation power scheme, the hybrid energy system is flexible in architecture, uses two or more energy forms, and has diverse energy sources. Under different mission requirements, it needs to meet the requirements of mass balance, energy balance, and power demand, etc. Therefore, The control and distribution management between different energy systems have become the key to hybrid power, and power management technology is one of the key challenges in the development of aviation hybrid power control systems. This paper reviews the current structural forms of aviation turbine hybrid electric propulsion systems, analyzes the current research status of power management technology for aviation hybrid systems, and points out that the online power management method based on optimization is the best power management technology solution for turbine hybrid electric propulsion systems
Cai, Changpeng, Liu, Hao, Gu, Jiangwei, Li, Shunming, Zhang, Haibo
Research Review on Data Fusion-Based Ship Information Perception Technology2026-99-0571To be published on 07/10/2026
With the development of domestic vessel traffic service (VTS) systems, China has established a comprehensive maritime traffic management infrastructure. Marine sensing equipment, including radar, the automatic identification system (AIS), and electro-optical (EO) systems, provides diverse sources of ship information. In recent years, data fusion technology has attracted increasing attention for its potential to improve the accuracy and completeness of ship perception. This paper introduces key ship information sensing technologies and examines the distinct characteristics of each approach. It then reviews recent advances in three main areas: vision-based ship feature recognition, multi-source data association analysis, and ship motion prediction. Finally, the paper outlines prospective research directions, including the integration of additional data sources, real-time data processing, enhanced data security, and intelligent maritime decision-making.
Zhao, Kui, Song, Zheming, Huang, Yuantao
Design of a Quadrotor UAV Controller Based on Fractional-Order Sliding Mode2026-99-0585To be published on 07/10/2026
Aiming at the problems of model uncertainty, external disturbances and high-frequency chattering of traditional sliding mode control in complex working conditions for quadrotor unmanned aerial vehicles, this paper proposes a control strategy based on fractional-order sliding mode. The quadrotor UAV control system has problems such as parameter uncertainty, multi-input multi-output, and sensitivity to internal and external disturbances. Traditional PID control has certain limitations. Sliding mode control has the advantages of strong robustness and simple implementation. Fractional-order calculus has hereditary and memory properties. The combination of the two has better control performance for nonlinear systems. To further improve the trajectory tracking performance of quadrotor UAVs, a fractional-order sliding mode controller is designed based on fractional-order theory and traditional sliding mode control. Finally, multiple experiments are conducted in Matlab/Simulink, including
Liu, Jingyi, Zhou, Qi, Wang, Jiajia, Lu, Zhaona
Privacy-Preserving UAV-Based Traffic Data Acquisition Platform2026-99-0595To be published on 07/10/2026
This paper puts forward a Privacy-Preserving UAV-Based Traffic Data Acquisition Platform to address 1) privacy leakage, 2) limited scenario coverage, and 3) low traffic data utilization efficiency in urban traffic monitoring environments. Our system integrates three innovations: 1) Dynamic Privacy Masking (DPM) and Dual-Track acquisition (DTC), which hides sensitive information (e.g., faces, license plates or LPL) in real-time while preserving critical traffic data (e.g., vehicle density, speed), 2) traffic data Localization (DL) and Privacy-Enhanced Federated Learning (FEFL), enabling cross-regional collaboration without raw traffic data sharing by perturbing neural network updates with differential privacy (DP), and 3) Ground-Air Collaboration (GAC) and VPF (VPF), combining UAVs with ground sensors and digital twins (DTs) to cover blind spots (e.g., tunnels, extreme weather). Experimented on UA-DETRAC and CitySim traffic data-sets, the platform achieves 92% privacy compliance (GDPR
Zhang, Shilin, Yan, Ming
Research on Battery Environment Heat Dissipation Control System2026-99-0535To be published on 07/10/2026
Batteries generate a large amount of heat during operation, and if it cannot be dissipated in a timely and effective manner, it will seriously affect the performance, lifespan, and even safety of the battery. Therefore, battery heat dissipation has become a key challenge in the development of new energy vehicles. The traditional liquid cooling system has problems such as complex design and control, and the need to improve heat dissipation efficiency. To address these issues, this study proposes an optimized design scheme for battery environment heat dissipation control system based on liquid cooling heat dissipation system. This study first conducted an in-depth analysis of the thermal generation mechanism of lithium-ion batteries and studied existing examples of thermal management schemes. On this basis, an innovative forward and reverse circulation device was designed, combined with a liquid cooling heat dissipation structure. The Keil uVision4 programming software was used to write
Ding, Xvqiang, Ni, Yiwei, Gu, Chen, Zhang, Jin, Chen, Mingyang, Jiao, Yunxiao
Optimization of Preheating Energy Distribution for Low-Temperature Cold Starts in Diesel-Electric Hybrid Vehicles2026-99-0516To be published on 07/10/2026
To minimize energy input and preheating time, this study first analyzed the energy consumption of intake air, lubricating oil, and coolant preheating through simulations. Temperature rise data were collected under various heating parameters. Next, simulations evaluated the hybrid power system’s resistance characteristics immediately after startup and the combustion parameters during the first cycle post-ignition under different temperatures. The temperature thresholds for successful start-up were identified, defining the feasible domain for optimization. Optimization calculations aimed to minimize preheating time and energy input, constrained by maximum preheating power. Results show that intake air heating has the greatest impact on start-up success, followed by lubricating oil heating. It is recommended to increase energy allocation to intake air and lubricating oil heating. This optimized strategy reduces preheating time and energy input by approximately 26% without changing the
Wei, Shengchen, Zhao, Zhenfeng
Study of Matchmaking Matching of Multi -Tasking System Module in the Motor Caravan2026-99-0503To be published on 07/10/2026
As a special vehicle, motor caravans have high customer demand and expectations for product quality under current market conditions. At the same time, customers generally have strong demands for functional differentiation and modification. To meet the requirements, manufacturers need to redesign and construct the production process platform, including redesigning and transforming various functional unit modules on the vehicle. For example, the flexibility of production process platform systems, standardization of electrical interfaces, and modularization of functional units, etc. In the implementation process, by embedding flexible architecture into the existing universal process system, motor caravans modification can be flexibly organized according to customer orders and requirements while ensuring streamlined production. In the implementation process, the focus of the new installation process system is to match the electrical systems inside the vehicle, ensuring not only the
Li, Sheng
Adaptive Class-Incremental Learning Intelligent Fault Diagnosis Method for Unmanned Aerial Vehicle Rolling Bearings2026-99-0596To be published on 07/10/2026
As a key component of unmanned aerial vehicles (UAVs), the stable operation of motor bearings is of vital importance to the stability of UAVs. In view of the incomplete data set in the actual diagnosis process, samples not encountered during model training are highly likely to appear. This paper proposes an Adaptive Class-Incremental Learning(ACIL) intelligent fault diagnosis method. This method construct a ResNet framework embedded with Coordinate Attention as the base architecture for class-incremental learning. Furthermore, the Information Preservation Example Selection(IPES) method is utilized to alleviate catastrophic forgetting and update the model from the previous phase using knowledge distillation under coordinate attention. The effectiveness of this method is verified through experiments on the bearing test dataset. The results show that, both average incremental accuracy and average incremental forgetting rate achieve state-of-the-art performance, which means that the
Song, Ziyang, Lu, Jiantao, Wu, Wei, Li, Shunming
Research on Urban Bus Timetable Optimization Based on Deep Reinforcement Learning2026-99-0538To be published on 07/10/2026
Public transportation serves as a crucial component of urban mobility, contributing to the alleviation of urban congestion, reduction of travel expenses, and mitigation of air pollution. Nonetheless, the dynamic passenger demand and the complex traffic conditions render traditional bus timetables inadequate, leading to ineffective allocation of public transportation resources. Consequently, it is essential to create bus timetables that are responsive to actual traffic scenarios and fluctuating passenger demand. This study regards the bus timetable planning problem as a Markov decision-making process within a discrete time framework, proposing a deep reinforcement learning-based optimization model for bus timetables. In particular, the model is designed to account for both bus companies and passengers, incorporating a state space and reward calculation method that emphasizes passenger comfort. Then Deep Q-Network (DQN) methodology is employed to issue instructions on whether a bus
Xu, Jie, Xia, Dong, Yang, Jianxi, Wang, Bing
Traffic Crash Prediction and Causal Disentanglement by Machine Learning2026-99-0552To be published on 07/10/2026
Aiming at the problem of insufficient modeling of spatio-temporal heterogeneity in road traffic accident prediction, a dual task machine learning framework integrating geographical environment, location attributes and time periodicity is proposed. The dataset used in this study was derived from traffic accident records of Nanchang during 2019–2023. Firstly, geographical identifiers are generated by rounding and aggregating latitude and longitude coordinates. At the same time, the location type is processed by a one-hot encoding, so as to carry out spatial clustering analysis of accident hotspots. Compared with the North-South pattern, the contribution of geographical features shows a strong East-West trend. The kernel density heatmap identified Zone A and zone B as dual core high-risk areas. Secondly, the sinusoidal/cosine function is used to encode the time feature circularly, which effectively captures the daily change of the accident. The quantitative analysis of random forest
Luo, Jiang, Zhang, Yuxin, Li, Xin, Wu, Ronghai
Research on Predicting Occupant Height Using CNN-Based Pressure and Posture in Zero-Gravity Seats2026-99-0521To be published on 07/10/2026
Zero-gravity seats alleviate prolonged sitting fatigue by optimizing human body pressure distribution, but the correlation mechanism between body size parameters and pressure distribution remains unclear. This study proposes a deep learning model based on multimodal data fusion, combining pressure matrices and postural angle data to construct a convolutional neural network (CNN) with a height prediction error ⩽3 cm. Experiments collected pressure and posture data from 100 participants with diverse anthropometric percentiles. Through the fusion of features and the optimization of the model, the study managed to quantify how height and weight impact pressure gradients. The results indicate that the model achieved a prediction R2 value of 0.73, which confirms that there is a strong correlation between pressure distribution and body size parameters. The findings offer theoretical and technical support for the adaptive adjustment systems within intelligent cabins.
Bi, Tengfei, Nie, Jiacheng, Du, Changjiang, Ji, Yuechen, Wang, Song, Sun, Jiawei
Research on the Design of Lane Line Recognition and Deviation Warning System Based on Machine Vision2026-99-0566To be published on 07/10/2026
In order to reduce traffic accidents caused by cars straying from lanes, a lane line recognition and deviation warning system based on machine vision is designed. It mainly includes image preprocessing, lane line detection, and the design of a deviation warning model. “In this study, an ROS-based intelligent vehicle-mounted camera is adopted for road image collection. To reduce the computational load of data processing while guaranteeing the algorithm’s accuracy and reliability, grayscale conversion and region of interest (ROI) extraction are implemented to finish the image preprocessing stage. Additionally, a fusion strategy of global and local thresholds is introduced to enhance both the operational speed and detection accuracy of the algorithm” use the Canny operator for the edge feature extraction; and complete the fitted lane lines with the improved Hough transform. Finally, based on the Kalman filter and camera viewpoint conversion coefficient algorithm, the lane line offset is
Wang, Xufeng, Zhang, Chunshu, Wang, Yan, Chen, Yihui, Ji, Rui
Optimization Method for Emergency Rescue Resource Allocation in Two-Way Ten-Lane Expressway Based on Risk Assessment2026-99-0567To be published on 07/10/2026
The two-way ten-lane expressway has the significant characteristics of “large traffic volume, mixed vehicle types, and heavy loads”, which makes the impact of traffic flow status on accident risk present nonlinear characteristics. Traffic flow fluctuations not only directly affect the probability of accidents, but also amplify the spatiotemporal differences in rescue needs through mechanisms such as lane occupancy time and accident chain reactions. Therefore, the essence of resource allocation on a two-way ten-lane expressway is the “spatiotemporal matching problem between dynamic risks and limited resources”, which requires both quantifying the spatiotemporal evolution of risks and coping with the high uncertainty of the traffic system. Aiming at the problem of inefficiency of traditional empirical resource allocation under complex traffic conditions, this study proposes a dynamic optimization framework based on multidimensional risk assessment for emergency rescue resource allocation
Kan, Youjun, Cao, Yang, Shi, Xiaomin, Gao, Shangjie
A Novel Rotating Component Comprehensive Diagnostic Network Based on Stacked Auto Encoders and Sparse Filter Combined with Generative Adversarial Network2026-99-0534To be published on 07/10/2026
Nowadays, the majority of intelligent fault diagnosis approaches are still centered on individual faulty components, while only a limited number of models are capable of performing integrated diagnosis for rotating systems that consist of shafts, bearings, and gears. Under variable-speed operating conditions, the large scale of vibration data further complicates the process of effective feature extraction. To improve these challenges, this study develops a comprehensive diagnostic framework for rotating components, termed WGAN-SAFC. The proposed architecture integrates a Wasserstein Generative Adversarial Network (WGAN) with a hybrid structure of stacked autoencoders and sparse filtering (SAFC). SAFC integrates the feature-learning capability of SAE and the sparsity-driven representation of SF, while incorporating adversarial data generation to address sample imbalance and enhance fault diagnosis performance. Experimental verification on collected vibration datasets demonstrates that
Li, Shunming, Feng, Mengqi
Simulation Analysis of Pontoon Structure Based on Tunnel Bank Evacuation Channel2026-99-0541To be published on 07/10/2026
To meet the requirements for efficient evacuation during tunnel navigation, the pontoon of the tunnel bank wall evacuation channel in a large-scale navigation building is taken as the research object. The water body and water wave are simulated using the coupled Euler-Lagrangian method and the push-plate wave method, respectively. The water boundary is processed using the viscoelastic artificial boundary method, and a simulation analysis model of the pontoon under the combined action of water waves and load is established. The results show that the average relative vertical displacement of the pontoon is basically the same under the condition of water wave and no water waves, but the fluctuation range of the pontoon is larger under the condition of water waves. When there are water waves and different loads, the maximum Mises stress distribution of the pontoon is essentially the same, and both are less than 80 MPa, meeting the strength requirements and demonstrating the rationality of
Tang, Weibi, Qin, Pan, Li, Ran, Tao, Ran, Hu, Zhifang
Construction and Empirical Analysis of Coal Road Freight Price Index Using Big Data2026-99-0560To be published on 07/10/2026
Coal is an important component of China's energy structure, mainly transported by three modes: railway, waterway, and highway. In regional coal transportation, highway transport undertakes numerous collection-distribution tasks and medium-short distance transport, playing a vital and indispensable role. Considering the characteristics of the coal highway transportation market and the demand for price indices, a three-tiered coal highway freight price index system has been established, including individual indices, classified indices, and an overall index. Using order data from the logistics platform of the Coal Big Data Center, the coal highway freight price index is compiled by adopting the internationally Laspeyres chain method. The methodological selection has passed the ADF stationarity test. Economically, the coal highway freight price index is closely correlated with coal prices, with the correlation coefficient reaching over 0.7, which can reflect about the coal highway freight
Zhao, Nanxi, Wang, Xinzi, Rong, Haoyu
Research on the Dynamic Simulation Method of Gearbox Based on ADAMS and MATLAB2026-99-0519To be published on 07/10/2026
The gearbox is a key component of the mechanical transmission system, and its fault diagnosis is essential to the reliability of the equipment. However, obtaining fault samples under actual working conditions for gearbox fault diagnosis is challenging. In this paper, the rigid-flexible coupling dynamic simulation model of the gearbox is established, and the co-simulation of gear normal, crack, and breakage is carried out in the ADAMS and MATLAB environments. The comparison between the simulated and measured signals shows that the simulation method can accurately reflect the key characteristics, such as rotation frequency and meshing frequency, and verify its reliability and accuracy. The research results can provide effective data support for gearbox fault diagnosis and improve the operational safety of mechanical systems.
Li, Dongxiao, Zhang, Qianqi, Zhang, Zhongzheng, Li, Yongbo
Path Planning Algorithm for Self-Driving Cars Based on High-Precision Maps and Improved A*2026-99-0588To be published on 07/10/2026
To address the limitations of the traditional A* algorithm in lane-level navigation, we propose an autonomous vehicle path planning algorithm based on high-precision maps and an improved A* algorithm to ensure effective application in complex traffic environments. We construct a hierarchical high-precision map based on the Lanelet2 framework to achieve structured modeling of complex road environments. To address the adaptability issues of the A* algorithm in lane-level navigation, we propose optimization schemes, including heuristic function improvements, path segment division, and target point validity verification, to ensure that vehicles can autonomously change lanes on multi-lane roads. By combining dynamic programming (DP) and quadratic programming (QP), we ensure the safety and smoothness of the path. Simulation results demonstrate that the optimized algorithm enables smooth stopping and starting at traffic lights in structured road environments and autonomous lane changes on
Wang, Siyu, Zhou, Rong, Shi, Tian, Xu, Zhen, Zhao, Zhiguo
ARINC 834A-1 Internet Protocol Based Aircraft Data Interface Function (ADIF)ARINC834A-1 (Current)7/2/2026
This standard defines the services, the transport layers, and the suggested security mechanisms to enable communication between aircraft systems and an Electronic Flight Bag (EFB). Herein is the definition of a set of protocols and services for the exchange of aircraft avionics data across aircraft networks. The purpose is to define a common set of services to be used to access specific avionics data that will enhance EFB applications. Supplement 1 introduces bi-directional communication between the EFB and FMS. It also enables mutual Transport Layer Security, incorporating client authentication.
Airlines Electronic Engineering Committee
Model-Based Sensitivity Study of Thermal Runaway Onset and Severity in Lithium-Ion Cells: Effects of Geometry and Thermal Boundary Conditions2026-01-07557/1/2026
Thermal runaway assessment in automotive battery development is still largely driven by isolated abuse tests, while design decisions require quantitative insight into how cell geometry, material thresholds, and thermal boundary conditions influence thermal runaway onset and severity. This paper presents a systematic sensitivity study using a coupled electrochemical and thermal model augmented with Arrhenius-based decomposition reactions to represent the dominant exothermic pathways. Thermal runaway onset is defined using a temperature rise-rate criterion to distinguish gradual heating from runaway acceleration. Two trigger modes are considered: an internal short circuit initiated by nail penetration and an external heating trigger. Four parameter groups are investigated: cell length scaling, separator decomposition temperature, external heating power, and the convective heat transfer coefficient to the environment. For the nail-triggered internal short circuit, larger cells exhibit
Ceylan, Deniz, Kulzer, André Casal, Winterholler, Nina, Giek, Michael, Weinmann, Johannes
Dynamic Wireless Charging for Electric Vehicles2026-01-07487/1/2026
This paper investigates the electromagnetic and circuit-level performance of an inductive power transfer (IPT) system for dynamic wireless charging of electric vehicles (EVs). Key design parameters affecting power transfer efficiency (PTE) are examined through a simplified Series–Series (SS) compensated IPT model using a Double-D coil geometry with shielded ferrite backing, developed in MATLAB. The framework evaluates the effects of air gap, lateral misalignment, load resistance, and operating frequency on overall system efficiency. Results show that PTE is highly sensitive to spatial alignment, with significant efficiency losses at air gaps greater than 10 cm and misalignments beyond 15 cm. A combined 3D surface plot confirms the compounded nonlinear influence of both parameters. Load resistance analysis identifies an optimal range of approximately 10–15 Ω, while frequency analysis indicates peak performance near 85 kHz, consistent with standard guidelines. These findings validate
Abdelrahman, Marwan, Sodre, Jose Ricardo
Generating Machine-Processable Specifications from Natural Language Using Large Language Models in Automotive Commissioning and Testing2026-01-07687/1/2026
In vehicle production, commissioning and testing processes of electric and electronic components are essential for value creation and quality assurance. The emergence of software-defined vehicles, however, leads to an increased scope and complexity of these processes as software functions depend on electric and electronic components for perception, execution, and processing tasks. In this context, this paper tackles a common challenge: Software that is deployed in vehicle production to implement commissioning and testing processes is developed upon specifications that define prerequisites, procedures, and target results in natural language. Therefore, extensive human interpretation and manual translation into executable code are needed being susceptible to errors as well as time-consuming. The large number of vehicle configurations and rapid changes in vehicle software further complicate the development of commissioning and testing software, particularly as verbose textual dependency
Köhler, Katja, El Asad, Aiman, Hahn, Michael, Reuss, Hans-Christian
Identifying and Mitigating Resonance Effects in HV Powernets: A Comprehensive Investigation into Current Ripple Effects in HV Power Nets2026-01-07427/1/2026
HV Power nets of electric vehicles consist of various HV components such as batteries, inverters, auxiliaries and cables. During in-vehicle testing, multiple failures of an auxiliary inverter were observed, caused by resonance issues within the component filter. Initial investigations revealed that these resonances, absent during manufacturer testbench evaluations, were influenced by the vehicle power net and its impedance characteristics. To better understand the underlying causes and identify preventative measures, extensive simulations were performed. The results demonstrate a diminishing influence of the power net capacitance when significantly larger than the component capacitance. Also, they highlight the critical impact of cable inductance on the component resonance frequency when comparable to the component’s inductance. A simplified electrical equivalent circuit was used to derive an equation predicting the resonance frequency as a function of the component’s capacitance
Schmiel, Fabian, Aurand, Tobias, Koehnlechner, Benjamin, Zimmer, Markus
A Comprehensive CFD-Driven Approach to Battery Thermal Modeling and Cooling System Optimization2026-01-07527/1/2026
With the continued expansion of electric mobility, liquid-cooled thermal management systems have become indispensable for ensuring the performance, durability, and safety of automotive battery packs. This work presents a novel cooling-plate design that integrates offset strip-fin turbulators to enhance convective heat transfer between lithium-ion cells and the circulating coolant. A comprehensive multi-region CFD model of the full battery pack is developed, incorporating an implicit lumped-parameter representation of cell heat generation. The numerical predictions are validated against dedicated experimental measurements available in the literature. Subsequently, a parametric study is conducted in which the number of hydraulic sub-modules and the inlet/outlet configurations are systematically varied to generate all feasible design permutations. The resulting configurations are compared to assess thermal performance and to quantify the benefits—as well as the potential penalties
Montenegro, Gianluca, Onorati, Angelo, Della Torre, Augusto, Tariq, Muhammad Hasnain, Bonetti, Elisa
UMV Peoplemover: Towards a Modular Fully Automated Vehicle Research Platform2026-01-07847/1/2026
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated
Pohl, Eric, Schmid, Fabian, Münster, Marco, Siefkes, Tjark, Stuebler, Tillmann, Mohammed, Shawan
An Application-Oriented Process Model for Selecting Uncertainty Quantification Methods in Machine Learning2026-01-07737/1/2026
Uncertainty quantification (UQ) is increasingly recognized as essential when machine learning (ML) is employed in domains that are safety-relevant, cost-intensive, or legally binding, such as the product engineering of battery electric vehicle (BEV) energy systems. UQ methods aim to estimate the aleatoric, epistemic or both uncertainties associated with the predictions of a machine learning model. However, the landscape of UQ methods is diverse and rapidly evolving, with no single approach proving optimal across all tasks. Consequently, the selection of methods in practice is often driven by experience, constrained by limited comprehensive knowledge, time, and implementation capacity. This paper introduces an application-oriented process model supporting data scientists in selecting UQ methods in ML by adapting the SPALTEN [1] problem-solving methodology and the Algorithm Selection Process Model (ASPM) into an Algorithm Selection Process Model for Uncertainty Quantification (UQ-ASPM
Holderied, Niklas, Hörtling, Stefan, Bause, Katharina, Düser, Tobias
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