Browse Topic: Electrical, Electronics, and Avionics

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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
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, Bermudez-Herrera, Leandro
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, Taco, Diana
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
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
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
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
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
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
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
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
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
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
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
Advanced Brake Force Distribution on trailers with Bendix Electronic Brake Controller2026-01-0797To be published on 09/14/2026
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel‑ends. Traditional brake force distribution strategies whether proportioned, electronic, or load‑adaptive do not account for these pad life differences. This can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO), especially in mixed‑fleet and multi‑trailer operations. This work introduces an advanced brake force distribution function, a mechatronic brake control concept that incorporates brake pad health information into supervisory brake force allocation. In the proposed architecture, the tractor is equipped with a Brake Control ECU, which collects brake pad life status from each trailer or dolly through Pad Wear Sensing ECUs over the Tractor‑Trailer CAN (TT‑CAN) network. The Pad Wear Sensing ECUs report pad wear information only and do not host braking
Ganesha, Vinodkumar
Machine Learning Prediction of Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing2026-01-0808To be published on 09/14/2026
Brake pad lifespan significantly influences environmental pollution and public health due to non-exhaust particulate emissions. Global vehicle production reached approximately 7 billion units in 2025, with brake systems generating an estimated 50,000 to 100,000 metric tons of wear debris annually, underscoring the need for improved durability and material optimization. This study investigates the capability of a Fully Connected Neural Network (FCNN) to predict brake pad wear and support formulation optimization. Controlled experiments were performed using a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) to simulate realistic braking conditions. Wear loss was measured under varying normal loads, sliding velocities, and interface temperatures. An FCNN was selected for its ability to capture nonlinear interactions between operating conditions and tribological responses. Despite a limited but high-quality dataset, the application of data standardization
Katakam, Abhishek, Eslamiat, Hossein, Kancharla, Sai Krishna, Filip, Peter
Bridging Human Expertise and Machine Intelligence in Chassis System Teardown Evaluation2026-01-0805To be published on 09/14/2026
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as
Divakaruni, Saikiran, Vaibhav, Veer, hansen, Scott, Hood, Trevor, Agrawal, Rahul
Time-Series Friction Torque Prediction for Drum Brake Systems via Mixed-Effects Random Forest Framework2026-01-0809To be published on 09/14/2026
The application of drum brake systems is expanding due to increasing cost competitiveness requirements in EV and purpose-built vehicle markets, as well as the introduction of EURO-7 particulate emission regulations. In spite of this trend, drum brake friction behavior is not yet fully understood because it is governed by complex and strongly coupled complicating mechanisms, including temperature history, braking conditions, and interactions between drum brake components. To address this gap, this study presents a method for developing a time-series friction torque prediction model using the Mixed-effects Random Forest (MERF) machine learning framework. Time-series data collected from sensors during drum brake dynamometer tests were analyzed to identify the key variables that govern the friction torque. Significant inputs were selected through Exploratory Data Analysis (EDA), considering test-to-test variability and potential mixed effects, and were then used to train and tune the MERF
Yoon, Jungro, Cho, Sunghyun, Kim, Wonjoon
Safe State Definition and Verification for Distributed Multi-Controller EMB Architectures2026-01-0800To be published on 09/14/2026
The Electro-Mechanical Brake (EMB) system is an essential technology for safe braking in modern vehicles. However, the adoption of multi-controller architectures has introduced new challenges to conventional Safe State strategies. Traditionally, the Safe State defined in functional safety means "function shutdown," and in accordance with ISO 26262 definition, aims for an "operational mode without risks exceeding reasonable levels." However, in the multi-controller architecture of EMB systems, the Fail-Operational Safe State concept is applied, where the system continues to provide limited functions even in the event of faults. It is essential to verify whether such operational modes actually satisfy the safety requirements of ISO 26262. This paper redefines the Safe State according to failure modes in EMB systems, analyzes system state transitions, and presents a coherence analysis methodology for validating the availability of resources required to provide limited functions in the
KIM, KANG SAN
Predicting Brake Squeal Occurrence Using Machine Learning with Temporal Feature Analysis of Braking-Torque Test Data2026-01-0812To be published on 09/14/2026
The automotive industry's paradigm shift toward autonomous driving and electrification has introduced new competitors threatening market dominance through differentiated value propositions. In this highly competitive landscape, delivering irreplaceable customer value requires providing sustainable and authentic luxury experiences. Quiet driving represents a tangible value that customers genuinely appreciate. Brake squeal—high-frequency noise arising from torque-induced friction behavior during braking—negatively impacts customer satisfaction and must be suppressed. Despite significant advances in brake squeal prediction modeling, the irregular nature of squeal generation mechanisms has prevented development of a generalized predictive model applicable to product development processes. Development and verification remain largely experimental. This limitation constrains early-phase design validation, as brake squeal is highly sensitive to chassis and braking system design. When squeal
Cho, Sunghyun, Yoon, Jungro, Kim, Yoon Cheol, Kim, Jeongkyu, KIM, SUNGHO, baek, SongYi, KIM, Won Joon, CHOI, KYUNG ROK
A Study on the Development of Brake Moan for CTBA Vehicles Using Simulation2026-01-0814To be published on 09/14/2026
Not only are customers' expectations for the development of various products, but also the demand for emotional quality improvement of the products is steadily increasing. Brake NVH, which is generated during vehicle braking, has a lot of influence on the emotional aspects of customers. There are several brake NVH such as judder, squeal, and moan. First, judder is phenomena of handle, brake pedal vibration and noise that occur during braking at the high-speed. The quality of judder has been improved a lot by applying judder detection and compensation logic in internal combustion engines and the heat load on the disc is reduced by regenerative braking in electric vehicles. So the judder problem hardly occurs these days. A squeal is a noise generated during braking at a low speed. The squeal is solved by tuning and shape change of a friction material in the high frequency band and by making the robust systems design such as caliper and disc in the low frequency band. Moan noise is low
KIM, SUNGHO, Kim, Jeongkyu, HWANG, JAEKEUN, Kang, Donghoon
Architecting Safety-Critical Systems for AI-Enabled Software-Defined Vehicles: A Compute, Timing, and Isolation Framework with a Brake System Case Study2026-01-0811To be published on 09/14/2026
Software-defined vehicle (SDV) platforms are reshaping safety-critical system design by consolidating braking and other motion-control functions on centralized heterogeneous edge compute that also executes physical-AI workloads. This consolidation breaks traditional assumptions of fixed ECUs and simple timing envelopes, complicating assurance of determinism, isolation, and fail-operational behavior for ASIL-D brake functions. Building on a decentralized brake-by-wire (BbW) architecture with dual controllers, redundant low-voltage power grids, and smart electromechanical brake corner actuators, this paper proposes a systems-level framework for architecting safety-critical functions in AI-enabled SDVs along three dimensions: compute, timing, and isolation. The framework classifies conventional and AI-based functions and maps them to heterogeneous compute classes; defines architectural patterns that combine safety islands, power-domain redundancy, and hardware partitioning to support
srinivasaraghavan, soumyasudharsan
Data to Decisions: An AI-Driven Test Intelligence Platform for Integrated Analysis of Raw Data, Test Reports, and Specification Compliance in Brake System Validation2026-01-0807To be published on 09/14/2026
Validation of brake systems is increasing in complexity due to electrification, software-defined architecture, integrated control modules, and higher functional safety requirements. Although physical testing remains the primary source of engineering evidence, interpretation of results including DVP&R/PVP&R compliance verification, anomaly detection, documentation, and milestone decision support continues to rely heavily on manual engineering analysis. This results in extended feedback cycles, inconsistent interpretation across teams, and limited traceability between raw data, reports, and governing specifications. To support engineers with more objective validation processes, there is a growing need for structured, data-driven intelligence that transforms dispersed test artifacts into actionable engineering decisions. This paper presents Data to Decisions, an AI-driven Test Intelligence Platform designed for integrated analysis of raw measurement data, test reports, validation plans
Divakaruni, Saikiran, Willey, Joseph, Srivastava, Namrata, Sankar, Arya, Namala, Divya, Gowtham, Rahul Sangani
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-0806To be published on 09/14/2026
As the industry is rapidly moving towards dry-by-wire architectures, accurate Electro-Mechanical Brake (EMB) models are increasingly critical for safety-relevant force control, diagnostics, and consistent performance over temperature and lining wear. However, widely used EMB identification methods often rely on linearized models or 'characteristic curve' calibrations that struggle to generalize across operating regions where friction, hysteresis, and compliance are strongly non-linear and temperature sensitive. Prior EMB force-estimation literatures addressed pad contact detection, hysteresis and thermal compensated clamp-force estimation, yet practical accuracy gaps remain during transients and under changing friction signatures. This work proposes a digital twin based on Physics-Informed Neural Networks (PINNs) that preserves the EMB's governing dynamics while learning both (i) physically meaningful parameters- such as effective system inertia, damping, stiffness etc., and (ii) a
Rai, Prakhar, Gadhvi, Tirth
A Study on TSC(Technical Safety Concept) design and verification in EMB(Brake By Wire) Systems2026-01-0801To be published on 09/14/2026
The Electro-Mechanical Brake (EMB) system, a dry-type Brake-by-Wire technology, eliminates hydraulic components by directly controlling friction braking through electrical actuators at each wheel. The architecture includes a Main Center Control Unit, a Backup Center Control Unit for redundancy, and four-Wheel Control Units, communicating via CAN FD. Compliance with ISO 26262 functional safety standards is critical due to the EMB system’s role in vehicle braking. As complexity increases, risks such as hardware failures and communication faults must be systematically addressed. This paper presents the design and verification of a Technical Safety Concept (TSC) for EMB systems in accordance with ISO 26262. Safety goals and Functional Safety Requirements derived from HARA analysis guide the System Architecture and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe mechanisms. Verification is performed at both system and vehicle levels
KIM, Dokun
Real-time Estimation of Brake Temperature using Physics-Informed Machine Learning Model2026-01-0810To be published on 09/14/2026
The current work presents a novel approach to estimate brake surface temperature in real time to aid in brake wear prognostics. Brake prognostics involve estimating the brake pad wear in real-time that helps in its predictive maintenance. Brakes are a safety critical system for vehicles, therefore, demands accurate and robust pad wear estimation, ensuring vehicle safety. However, it involves several challenges. The estimation of pad wear is fundamentally a two-stage process: the first stage involves the accurate prediction of brake pad surface temperature, while the second stage utilizes this thermal history to calculate cumulative material wear. A significant challenge in estimating brake pad wear without expensive sensor is that it is sensitive to the surface temperature prediction; any error in the thermal model propagates and compounds in the wear prediction stage. To identify surface temperature, traditional physical sensors are often cost-prohibitive or prone to failure in the
Gannavarapu, Shivadath, Pal, Anuj, Fan, Mengdi
Modeling Brake Torque Hysteresis for Chassis Controls Hardware In the Loop Simulation2026-01-0824To be published on 09/14/2026
It is hardly a new trend for on road, vehicle intensive tuning and testing of chassis control features such as Anti-Lock Brakes, Traction Control, and Electronic Stability Control to move away from vehicle testing and towards non-vehicle test platforms such as Hardware-In the Loop (HIL) simulations and even further into pure math-based simulations. However, a significant acceleration of these activities has been occurring recently in the automotive industry, reducing or eliminating calibration time on vehicles and amplifying the demand for highly representative, non-vehicle test platforms to calibrate and even to validate chassis controls features. In current state of the air HIL simulation, the input (brake pressure) to output (brake torque) of each wheel brake in a vehicle’s brake system is modeled relatively simplistically, including at most pressure and brake temperature sensitivities, usually in lookup table form. Each brake corner contains over 20 different friction interfaces
Antanaitis, David, Ridenour, Nick, Miller, Bryan, Karnjate, Timothy
Research and Application of Flush Adjustment for Multi-Link Retractable Hatch2026-99-1620To be published on 07/24/2026
The adjustment process for multi-link retractable hatches has long relied on personal experience, making it difficult to achieve precise and quantitative length adjustments. This limitation has consistently constrained the efficiency of the adjustment process. This paper aims to analyze the risks and shortcomings in the existing flush adjustment process, simplify the flush adjustment process into a mathematical model, and calculate the required adjustment amount of the actuator length. By simplifying the flush adjustment process and steps, the risk associated with the adjustment process can be reduced, and the efficiency of door step difference adjustment can be improved.
Deng, Qinwen, Shen, Yingdong, Wang, Zhihai, Li, Yixiao, Wei, Xingxu, Gao, Haosen
Analysis and Experimental Research on The Impact of the BangBang Valve Switch on the Flow Rate of the Storage and Supply System2026-99-1615To be published on 07/24/2026
This article focuses on a wide range of high-precision storage and supply systems. Under the rated flow rate of 2.928 mg / s of the proportional flow controller, the instantaneous flow fluctuation range of the BangBang valve reaches 2.963 mg / s, exceeding the control accuracy requirement of 1% for the proportional flow controller. By establishing mathematical models of the BangBang valve, proportional valve, and proportional flow controller for simulation analysis, the trend of the simulation results is consistent with the experimental results. Furthermore, considering the spatial layout and weight of the storage and supply system, this paper proposes a method to improve the accuracy of flow output by adding 180 mL of air capacity between the proportional valve and the proportional flow controller. Ultimately, the maximum flow fluctuation of the proportional flow controller at the moment of the BangBang valve opening and closing is 2.941 mg / s, which meets the control accuracy of the
Li, Zhong, Yan, Zelong, Huang, Tiankun
Hybrid Particle Swarm Optimization and Machine Learning Framework for Enhanced Prediction of Ground Settlement during Rectangular Pipe Jacking2026-99-1647To be published on 07/24/2026
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Hu, Shiwei, Hu, Rong, Zhang, Hong, Chen, Yi, Hu, Da
Influence of Modified Fly Ash Content on the Properties of Resin-Based Friction Materials2026-99-1657To be published on 07/24/2026
In this study, five resin-based brake pad samples with modified fly ash contents of 0%, 4%, 8%, 12%, and 16% were prepared to investigate the influence of fly ash content on the comprehensive performance of the friction materials. The tribological properties of all samples were evaluated under temperature conditions ranging from 100 °C to 350 °C, and their overall performance was assessed using five evaluation indices. Based on the AHP-MOORA algorithm, sample F12 exhibited the highest comprehensive weighted score of 0.11, followed by samples F0 and F8 with scores of 0.10 and 0.09, respectively, indicating a slight decline. In contrast, the comprehensive weighted scores of F4 and F16 were relatively low, at 0.05 and −0.01, respectively. Among the five composites, F12 demonstrated the best overall performance, with F0 and F8 ranking next, while F4 and F16 performed poorly. These results suggest that, within a certain range, increasing the fly ash content can enhance the comprehensive
Li, Xiaobiao, He, Kang, Zhao, Zhuanzhe, Wu, Bo, Sun, Fei
Fault Diagnosis of Packaging Machine Bearings Based on EEMD-CNN-BiLSTM2026-99-1601To be published on 07/24/2026
During the high-speed operation of packaging machines, if the abnormal components evolve into faults, the packaging machines often stop for inspection or even damage, causing production stagnation and huge economic losses. If key variables are predicted and faults are identified before the evolution of packaging machine failures, it is of great significance to ensure equipment safety and reduce maintenance costs and losses for enterprises. The purpose of fault prediction is to use the information modeling of equipment historical data to output the changes in key features before component failures in the future. Firstly, for the redundant data of multiple measurement points of the same variable in the packaging machine process variables, Pearson correlation analysis is used to obtain more accurate variable data. We reuse adaptive empirical mode decomposition (EEMD) for signal processing and feature extraction, reduce redundant information, use convolutional neural network (CNN) models
Wu, Aimin, Liu, Shixian, Zhao, Lihui, Liu, Zhao, Wu, Tao, Li, Lianbing
Research on a Rocket Projectile Impact Point Prediction Model Based on Improved Proportional Navigation Guidance Law2026-99-1602To be published on 07/24/2026
Rocket projectiles are a type of ammunition that get their power from rocket engines. Long-range guided rockets, in particular, hold great significance as they seem to mark the way forward in modern warfare. These guided projectiles take full advantage of the considerable range that long-range rockets offer and, at the same time, manage to achieve improved accuracy. This paper delves into a model that is used for predicting the impact point of rocket projectiles, with the application of the proportional navigation guidance law. It also undertakes an analysis of both the strengths and the weaknesses of this model. Through the formulation of equations related to the dynamics of the center of mass and some other supplementary equations, a rather comprehensive trajectory equation was worked out. When this trajectory was simulated, it brought about the creation of a firing table, which is of help in predicting the initial trajectory inclination angle.
Tao, Wenwen, Wang, Ru, Zhang, Liang, Pi, Runge
Design and Application of A Pressure Measurement System Based on Spacecraft Chemical Propellant Engine Plume Test2026-99-1623To be published on 07/24/2026
Spacecraft with chemical propellant engines, especially spacecraft for exploring extraterrestrial objects, need to carry out plume tests on the ground in order to determine the influence of engine plumes on spacecraft. An important purpose of the plume test is to accurately measure the pressure field in key parts of the spacecraft. In this paper, according to the pressure measurement requirements of the spacecraft plume test, the design of a pressure measurement system is carried out, which mainly includes a pressure measurement sensor, a pressure difference measurement sensor, a pipeline, a cable, a measuring instrument, a data acquisition instrument, upper measurement software, and so on. The designed pressure measurement system was successfully applied to the plume impact test of Chang'e VII, which provided important technical support for the development of the spacecraft.
Wu, Yue, Guo, Qinliang, Wu, Dongliang, Liu, Xiaoning, Tao, Dongxing, Lin, Boying, Xie, Zheng, Wei, Xi, Niu, Tong
Advanced Predictive Modeling of Pipeline Settlement in Rectangular Pipe Jacking Tunneling using Hybrid Deep Learning Techniques: A PSO-LSTM-Self Attention Approach2026-99-1648To be published on 07/24/2026
Nowadays, as computer technology makes quick progress, innovative algorithms like deep learning are getting used more and more in underground engineering and lots of other fields. When working on rectangular pipe jacking tunnel projects, accurately predicting the magnitude of pipeline settlement is really key to keeping the work moving smoothly. But traditional ground settlement prediction methods mainly rely on empirical formulas and numerical simulation software. When applied to tunnels with complex geometries, though, these methods usually don’t work as well as needed. To fix this problem, our study came up with a new model called PSO-LSTM-Self-Attention Mechanism (shortened to PSO-LSTM-SAM), specifically designed to predict pipeline settlement caused by rectangular pipe jacking work. This model takes the data collected from construction monitoring and uses that as the input for time series modeling work. That allows for in-depth analysis of real-time settlement data, and as a
Chen, Yi, Weng, Xiaoxuan, Zhang, Hong, Li, Yongsuo, Hu, Da
Evaluation of Structural Service Performance of Low-temperature Modified Asphalt Pavement based on Peavy-Duty traffic Engineering2026-99-1645To be published on 07/24/2026
Relying on the reconstruction project, the low-temperature modified asphalt pavement significantly reduces the construction temperature of the asphalt mixture by 40 °C compared with the traditional asphalt pavement, and improves the road performance of the material. By comparing the two mixture rolling schemes, the compaction effect of scheme 2 is better. For AC-13 mixture, the flexural tensile strength of USP-SBS composite modified asphalt mixture is 0.67 MPa higher than that of SBS modified asphalt mixture, and compared with SBS modified asphalt mixture, the final rut depth of USP-SBS composite modified asphalt mixture is 2.68 mm shallower than that of SBS modified asphalt mixture, and the total deformation rate is 43.8% lower than that of the latter. The post-construction quality evaluation shows that the stability of the low-temperature modified asphalt pavement test section under the bearing capacity and high-temperature-water coupling is better than that of the conventional road
Liu, Chuanfeng, Xu, Ke, Shi, Zheng, Hao, Jidong, Zhao, Liandi, Xianwei, Wang
Accurate Control of Refueling Volume for a New Type of Oil Change Device in the Reducer of Tobacco Machinery Equipment2026-99-1618To be published on 07/24/2026
In order to achieve precise control of refueling volume, improve oil change efficiency, reduce oil pollution and waste, a new oil change device for the reducer of the range hood equipment is studied. We design a new oil change device that integrates oil discharge and refueling functions based on the operating characteristics of the reducer in the range hood equipment. Using the rotational speed of the power pump and the flow rate of the oil pipeline as variables, we determine the refueling flow rate using a one-dimensional quadratic formula. Based on direct control theory, we optimize the relative position parameters of each component of the device, establish a control matrix, and achieve precise control. The experimental results show that the new oil change device exhibits good performance during both one-time oil discharge and refueling processes, meeting the precise control standards for refueling volume. The design and application of a new oil change device can effectively improve
He, Pengtao, Wei, Bo, Liang, Zhiyuan, Deng, Weiren, Liang, Wenbin, Xing, Yuquan
Modeling and Simulation Analysis of Liquid Hydrogen and Power Systems Based on Amesim2026-99-1608To be published on 07/24/2026
Hydrogen-powered aircraft primarily utilize the conversion of liquid hydrogen into gaseous hydrogen to replace aviation kerosene, where hydrogen is directly combusted to provide propulsion. This study applied Amesim software to establish a complicated model simulating the liquid hydrogen to gaseous hydrogen conversion and ignition combustion processes. The simulation contains converting liquid hydrogen into gaseous hydrogen through a heat exchanger and simulating the mixture of gaseous hydrogen and air in the engine combustion chamber, and then igniting the mixture. The pressure, temperature, and flow rate of gaseous hydrogen and air during the ignition and combustion process in the engine combustion chamber, as well as the outlet temperature of the combustion chamber, are simulated and analyzed. The results demonstrate that during the simulation process, the internal pressure of the liquid hydrogen storage tank, the outlet pressure and flow rate of the liquid hydrogen pump, and the
Gao, Pengfei, Wang, Lijian
Research on a Real-Time Pilot-Induced Oscillation Detector Based on a Convolutional Neural Network2026-99-1613To be published on 07/24/2026
This study aimed to develop a real-time Pilot-Induced Oscillation (PIO) detector by utilizing a Convolutional Neural Network (CNN) and applying it to flight test monitoring. For data training, a database of PIO samples is established. CNN has the advantage of lower complexity and automatic feature learning; it is chosen to develop the PIO detector. The results showed that the accuracy of the trained model has reached 93.71%, where the recall rate reaches 94.74%. To better assist the monitoring team, a simple software is designed based on the trained model for PIO detection, and it has been applied in flight test monitoring. In conclusion, this research demonstrates that the CNN Algorithm can be utilized in PIO discrimination and improve the monitoring capacity for flight testing.
Han, Yiwen, Liu, Chaoqiang
Trajectory Optimization for Micro Air Vehicle with Multiple Constraints2026-99-1632To be published on 07/24/2026
This study aims to solve the trajectory optimization problem of multi degree of freedom micro air vehicle (MAV) with the aim of improving its flight performance through technological innovation. A multi degree of freedom trajectory optimization (MDFTO) method with sideslip angle and angle of attack as the core control variables was proposed for multiple complex constraints in combat environment, such as terminal accuracy and overload limitation. This method can more accurately characterize and adapt the strong nonlinear, dynamic coupling and time varying characteristics of the MAV in high-speed maneuvering flight. In order to solve this MDFTO problem with multiple constraints and strong nonlinear characteristics efficiently, the hp adaptive pseudospectral method is used in this study, and is verified by simulations based on the GPOPS-II optimization platform. The algorithm has the advantages of highly accurate discrete state and control variables, efficient processing of path and
An, Zhichao, Ming, Chao, Wen, Guangbao
Optimization of Turbine Discrete Noise by Non-Uniformly Distributed Blade Design2026-99-1617To be published on 07/24/2026
The Mellin non-uniformly distributed moving blade method was adopted to conduct CFD numerical analysis and sample experimental tests on the axial-flow turbines before and after optimization using the uniformly distributed and non-uniformly distributed design methods, respectively. In the original design, five blades were evenly distributed in the 360° circumferential direction, and the non-uniformly distributed angles were 46°, 102°, 46°, 83°, and 83°. CFD numerical analysis shows that due to the low rotational speed of the turbine and the absence of a sealing structure at the blade tip, factors such as tip noise leakage and backflow have little impact, and the flow field pulsation is mainly caused by the blades themselves. The non-uniformly distributed design can significantly enhance the work-doing capacity of the blades. At 90% of the blade height, the torque can be increased by up to 60%, but at the same time, the axial force on the blades also increases accordingly. Near 80% - 90
Wu, Aiping, Ma, Tianli, Wang, Shiming, Ding, Chengling
Scaled-Down Strategy Analysis of Typical Components in Aircraft Power Supply Systems2026-99-1627To be published on 07/24/2026
Due to the constraints of manufacturing costs and cycles, it is difficult to simulate the full-scale operating conditions of aircraft electrical power systems. Usually, scaled-down low-power systems are used for prototype development and experimental research. This paper puts forward a systematic framework for developing scaled physical models of aircraft electrical power systems by using similarity theory. In this paper, according to the general design principles of DC-DC converters, a 10-kW low-power DC-DC converter and a 250-kW high-power DC-DC converter are designed. Mathematical models for both systems are developed, and a time-domain performance index analysis is carried out using the per-unit dynamic equivalence principle. A metric conversion model is established and equivalently mapped to a high-power DC-DC converter simulation model. The waveform consistency between the converted model and the aforementioned 250-kW high-power model is verified, which shows the validity of the
Ma, Huan, Ai, Fengming, Bi, Wenyan, Pei, Xiaoning, Liu, Liangliang
Optimization Design and Performance Verification Method of Antiaircraft Gun Fuze2026-99-1630To be published on 07/24/2026
In order to ensure that the high-caliber artillery ammunition fuses can successfully complete their combat tasks with high quality, it is necessary to optimize the design of their structure and conduct simulation verification of their performance. Through optimization design, this paper determined that the distance between the antenna plate and the wind cap of the proximity detonation module of the high-caliber artillery ammunition fuse is 6.2mm, and the thickness of the wind cap top is 12.9mm; it also determined that in the coaxial line feeding mode, a circular patch is used as the antenna shape, with the lowest return loss (reaching -38.5244 dB), which is conducive to the emission of electromagnetic wave energy; by introducing the methods and processes of intensity simulation analysis and aerodynamic thermal simulation analysis, as well as the methods of performance verification, this paper provides reference and guidance for the simulation analysis of similar systems.
Liu, Liwen, Sun, Zhangyi, Ning, Quanli, Cai, Canwei
Attitude Control of a Variant Flying Wing UAV Based on L1 Adaptive and Fuzzy Clustering2026-99-1858To be published on 07/17/2026
This study presents a full-envelope attitude-stabilisation and trajectory-tracking strategy for morphing flying-wing UAVs operating in highly nonlinear and strongly coupled conditions. The approach integrates fuzzy C-means (FCM) envelope partitioning with L1 adaptive control. Small-disturbance linear models are first generated at multiple altitude–Mach trim points; the FCM algorithm then performs unsupervised clustering in the state space, yielding representative subintervals that capture local flight-dynamic characteristics. The optimal cluster number and fuzziness exponent are selected using the partition coefficient, partition index, partition entropy, and Xie–Beni indices. For each sub-interval, an LQR baseline controller is designed and augmented by an L1 adaptive compensator, where a low-pass filter decouples adaptation from robustness to guarantee specified transient-performance bounds under matched/unmatched uncertainties, actuator saturation, and external disturbances. A feed
Tang, Longhao, Sun, Xiaoxu, Liu, Changlin
A Detection Method for 10N Thruster Ignition Anomaly Based on the Temperature Model2026-99-1814To be published on 07/17/2026
To solve a problem that ignition anomaly can’t be detected in time, based on the thermal equilibrium equation, the space heat flow, heater heating, propellant combustion, and thermal radiation to cryogenic space are considered to build an accurate ignition temperature method for the 10 N thruster by using on-orbit true temperature. Further, considering the error of measuring the thermistor, an envelope model for the 10 N thruster ignition temperature is established. Based on the above, a detection method for the 10 N thruster ignition anomaly of on-orbit satellites is proposed. The accuracy of the method is relatively high, and the absolute error is less than 3 degrees Celsius. An anomaly can be quickly detected when the 10N thruster ignition temperature deviates from the normal trend by 3–5 degrees celsius. The method is applied to a DFH-3 satellite, and the maximum difference of 10 N thruster ignition temperature between the theoretical values calculated by the proposed method and
Li, Liling, Tian, Huadong, Wei, Yubo, Fei, Di, Xing, Chao
Research on Clustering Data-Driven Intelligent Perception Algorithm for Micro Mobile Robots2026-99-1843To be published on 07/17/2026
To address the challenges faced by micro flapping-wing flying robots in visual navigation—specifically, the large volume of visual information and the difficulty in transforming it into usable intelligent visual data—this paper proposes a clustering-based data-driven approach for directional and image perception. The aim is to enable intelligent visual navigation for flapping-wing robots. The proposed method performs clustering analysis on gyroscope data from the flapping-wing robot to extract directional features. Simultaneously, it applies clustering techniques to visual images captured by the robot to identify intelligent features such as edges. This approach enables the robot to acquire multiple optimized perceptual data types, thereby enhancing the behavior control system. Through the use of clustering analysis, the method not only improves the effectiveness of visual navigation but also extracts features related to visual targets and environmental information, providing technical
Li, Zixuan, Ding, Wei, Zhang, Feng, Song, Min, Liu, Zhaoming, Miao, Lei, Liu, Haotian, Bai, Ning, Tian, Shen, Cui, Long, Wang, Hongwei
Aerodynamic Optimization Method Based on a Hierarchical Adaptive Activation Function Neural Network2026-99-1823To be published on 07/17/2026
Efficient optimization of aerodynamic shapes is a critical challenge in aircraft design. Traditional CFD-based optimization workflows suffer from high computational costs and low efficiency, which severely restricts their practical engineering application. In this paper, a novel aerodynamic optimization method based on a hierarchical neural network with adaptive activation functions is proposed. The network adopts learnable B-spline activation functions and is hierarchically constructed in accordance with the sharing status of B-spline control points. After being trained to achieve fast and accurate prediction of aerodynamic performance, the network can effectively replace the traditional CFD module in the optimization loop. The primary advantage of the proposed method is that it significantly reduces the computational cost during the optimization process while ensuring that the prediction accuracy is not compromised. This work thereby presents a novel strategy and technical framework
Liu, Di, Wang, Yongfeng, Wen, Hong, Wei, Yuanhang, Ma, Hengwei, Zhao, Runhui
Load Analysis of Flap and Slat Actuators in Aircraft High-Lift Systems2026-99-1802To be published on 07/17/2026
This study develops an end-to-end load analysis scheme for flap and slat actuators, which comprise the aircraft’s high-lift system, and the analysis results are directly integrated into hardware optimization. Because they shoulder heavy responsibilities during the takeoff and landing phases, whether they can remain rock-solid under complex aerodynamic conditions or even remain unmoved in emergencies is directly related to their overall safety performance. This work process is closely linked and includes three major links. First of all, according to the CCAR-25.301 standard, the load envelope under normal working conditions is sorted out, and the limit cases of abnormal faults are exhausted. Subsequently, ANSYS Workbench pulled silk and peeled off the cocoons to capture the peak stress at the engagement between the output shaft and the gear. In the end, the closed-loop verification of the customized test bench made the theoretical calculations and the hardware-measured data exactly the
Xu, Yuanze
Few-Shot Semantic Segmentation Method for Crater Detection on Small Bodies2026-99-1852To be published on 07/17/2026
Craters are the primary landmarks used for visual navigation in missions exploring small celestial bodies. However, obtaining high-quality, annotated crater data is often challenging due to limited imaging conditions and strict mission constraints. Conventional semantic segmentation models struggle with limited data and are challenging to train effectively. To overcome this limitation, this study introduces a few-shot segmentation approach for crater detection on small celestial bodies. Our method includes a prototype representation module that constructs class-level prototypes to quickly associate crater regions with their semantic features. This paper also designs an iterative learning module that gradually improves the segmentation output, helping the model better capture detailed edges and structures. Tests on a simulated few-shot dataset demonstrate that our method provides reliable and accurate crater segmentation, achieving a mean intersection-over-union (mIoU) of 88.7
Li, Shuai, Zhu, Shengying
Design of Carrier-Based Aircraft Landing Control Law Based on First-Order Linear Active Disturbance Rejection Control2026-99-1835To be published on 07/17/2026
This paper investigates the high-precision landing control problem of carrier-based aircraft. An Active Disturbance Rejection Control (ADRC) technique is employed to design longitudinal and lateral-directional landing control laws. The landing process is simulated by incorporating an airwake model, and the results are compared with those of a PID control law. The analysis demonstrates that the proposed ADRC controller reduces lateral deviation errors and significantly improves landing accuracy and success rate.
Yu, Jiayang, Yin, Yong
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