The identification of sustainable fuels that exhibit optimal physico-chemical properties, can be synthesized from widely available feed-stocks, enable cost-effective large-scale production, and integrate seamlessly with existing infrastructure is essential for reducing global carbon emissions. Given their high energy density, efficient handling, and versatility across applications, renewable liquid fuels remain a critical component of even the most ambitious energy transition scenarios. Lactones, cyclic esters derived from the esterification of hydroxycarboxylic acids, feature a ring structure incorporating both a carbonyl group (C=O) and an ether oxygen (O). Variations in ring size and carbon chain length significantly influence their physicochemical properties, which in turn affect their performance in internal combustion engines. According to predictive models based on artificial neural networks, valerolactone, hexalactone, and heptalactone isomers show promise as fuels in spark

Sirna, Amanda, Loprete, Jason, Ristow Hadlich, Rodrigo, Assanis, Dimitris, Patel, Rutvi, Mack, J. Hunter

Flexible an Low Cost Fuel Consumption Prediction in Heavy-Duty Vehicles Using Machine Learning

2025-24-0121

09/07/2025

This study presents a novel approach for predicting fuel consumption in heavy-duty vehicles using a Machine Learning-based model, which is based on feedforward neural network (FFNN). The model is designed to enhance real-time vehicle monitoring, optimize route planning, and reduce both operational costs and environmental impact, making it particularly suitable for fleet management applications. Unlike traditional physics-based approaches, the FFNN relies solely on a refined selection of input variables, including vehicle speed, acceleration, altitude, road slope, ambient temperature, and engine power. Additionally, vehicle mass is estimated using a methodology presented elsewhere and is included as an input for a better generalization of the consumption model. This parameter significantly impacts fuel consumption and is particularly challenging to obtain for heavy-duty vehicles. Engine power is derived from both engine torque and speed (RPM), ensuring a direct relationship with fuel

Vicinanza, Matteo, Pandolfi, Alfonso, Arsie, Ivan, Giannetti, Flavio, Polverino, Pierpaolo, Esposito, Alfonso, Paolino, Antonio, Adinolfi, Ennio Andrea, Pianese, Cesare, Frasci, Valentino

Data-Driven Modeling of H2-SCR Catalysts for Real-Time Estimation of NOx Reduction Efficiency

2025-24-0084

09/07/2025

Achieving zero emissions across transportation is a tremendous challenge. The upcoming Euro 7/VII standards, set to be enforced in 2025, will mandate further reduction in ICEs exhaust emissions. Thus, additional improvements and potential new technologies and fuels are needed to design ultra-low emissions vehicles. Hydrogen seems to be a very attractive fuel, thanks to its high lower heating value, clean combustion, and extremely low pollutant emissions, due to the zero-carbon content. Nevertheless, NOx emissions are still an issue in hydrogen fueled engines and optimized lean-burn combustion and suitable after-treatment NOx reduction are mandatory to reach high specific power and efficiency and near zero NOx emissions, thus enabling H2-ICE powered vehicles to be zero-impact emitting technology solution. Selective Catalytic Reduction by using NH3 as the reducing agent is the most effective control technology for NOx abatement. Nevertheless, ongoing research and innovation are critical

Crispi, Maria Rosaria, Conde Cortabitarte, Carla, Occhicone, Alessio, Piqueras, Pedro, Arsie, Ivan, Pianese, Cesare

Reducing Range Prediction Uncertainty in Heavy-Duty Electric Vehicles Using a Route-Informed Transformer-Based Model

2025-24-0122

09/07/2025

Electrification of heavy-duty on-road trucks used for regional freight transportation is a viable option for fleets to reduce operation and maintenance costs and lower their carbon footprint. However, there is considerable uncertainty in projecting their daily range because highly variable payload mass, among other factors, confounds battery state of charge (SOC) prediction algorithms. Previous work by the authors proposed an electric vehicle range prediction model based on two parallel recurrent neural networks (RNNs). The first RNN used mean-variance estimation to output a predicted mean and variance, and the second used bounded interval estimation to provide bounds on the SOC required to complete a trip. The dual RNN approach resulted in estimating the remaining range and error bands of the SOC over the route. The previous work was limited because it did not incorporate driving conditions, like road type and ambient temperature, that affect driver behavior and energy consumption

Jayaprakash, Bharat, Eagon, Matthew, Northrop, William F.

Battery Datasets for Neural Network-Based State-of-Health Estimators: A Review and Implementative Workflow

2025-24-0139

09/07/2025

Management of battery systems for electric vehicles has great importance to ensure safe and efficient operation. State-of-Charge and State-of-Health (SoH) are fundamental parameters to be taken under control even though they cannot be directly measured during vehicle operation. Some control approaches have gained increasing interest thanks to advances in sensor availability, edge computing and the development of big data. In particular, SoH estimation through machine learning (ML) and neural networks (NNs) has been thoroughly investigated due to their great flexibility and potential in mapping non-linear relations within data. The numerous studies available in the literature either employ different extracted features from data to train NNs, or directly use measurement signals as input. Additionally, many studies available in the literature are based on a limited number of publicly available datasets, which mainly encompass cylindrical battery cells with small capacity. Starting from

Chianese, Giovanni, Capasso, Clemente, Veneri, Ottorino

Use of Model-Based Datasets and Neural-Networks to Support Real-Time Driving Suggestions System for Components Preservation in Battery Electric Vehicles

2025-24-0115

09/07/2025

Nowadays, Battery Electric Vehicles (BEVs) are considered an attractive solution to support the transition towards more sustainable transportation systems. Although their well-known advantages in terms of overall propulsion efficiency and exhaust emissions, the diffusion of BEVs on the market is still reduced by some technical bottlenecks. Among those, the uncertainty about the expected durability of the vehicle's onboard battery packs plays a key role in affecting customer choice. In this context, this paper proposes the use of model-based datasets for training a driving support system based on machine learning techniques to be installed on board. The objective of this system is to acquire vehicle, environmental, and traffic information from sensor’ networks and provide real-time smart suggestions to the driver to preserve the remaining useful life of vehicle components, with particular reference to the battery pack and brakes. For the generation of the training dataset, first, a set

Bernardi, Mario Luca, Capasso, Clemente, Iannucci, Luigi, Sequino, Luigi

Machine Learning-Enhanced Combustion Modelling: Predicting Ethanol Effects in a Single-Cylinder Research Engine

2025-24-0026

09/07/2025

Recent studies highlight the urgent need to reduce greenhouse gas (GHG) emissions to mitigate the impacts of global warming and climate change. As a major contributor, the transport sector plays a vital role in these efforts. Ethanol emerges as a promising fuel for decarbonising hard-to-electrify propulsion sectors, thanks to its sustainable production pathways and favourable physical and combustion properties, such as energy density, rapid burning velocity, and high knock resistance. This work proposes a methodology to enable the possibility of replicating the combustion behaviour of ethanol in a 1D CFD simulation environment representative of a single-cylinder research engine. Spark-ignition combustion is simulated through the Eddy Burn-Up combustion model previously calibrated for standard fossil gasoline. The combustion model features a laminar flame speed neural network, trained and tested through reference chemical kinetics simulations. The combustion model showed great accuracy

Ferrari, Lorenzo, Sammito, Giuseppe, Fischer, Marcus, Cavina, Nicolò

Methods for Estimating Coasting Resistance of Heavy-Duty Commercial Vehicles under Various Loads

02-19-01-0005

08/19/2025

This research primarily addresses the issue of resistance model setting for chassis dynamometers or EIL (engine-hardware-in-the-loop) systems under various loads. Based on the data available from the heavy-duty commercial vehicle coast-down test reports, this article proposes three methods for estimating coasting resistance. For heavy-duty commercial vehicles that have not undergone the coast-down test, this article proposes the GA-GRNN (AC) model to predict coasting resistance. Compared to the GA-BPNN model proposed by previous studies, the new model, which achieves 93% prediction accuracy, demonstrates higher estimation accuracy. For heavy-duty commercial vehicles that have undergone the coast-down test, the coasting equal power method proposed in this article can estimate the coasting resistance under various loads. The accuracy and stability of the new method are verified by several coast-down tests. Compared to the existing method proposed by existing scholars, the new method has

Liang, Xingyu, Sun, Shangfeng, Li, Tengteng, Zhao, Jianfu

An Artificial Neural Network–Assisted Hybrid Design Approach for Induction Motors in Vehicular Application

14-14-03-0017

08/06/2025

This article presents an artificial neural network (ANN)–based hybrid design methodology for motors used in electric vehicle applications. The proposed method uses ANN to achieve a semi-optimized motor geometry, followed by the drive cycle analysis for the desired vehicle. For this, a large pool of motor design data is used as a training set for the ANN. The semi-optimized motor geometry is further processed for power factor improvement, overall motor efficiency, and electromagnetic noise reduction. The proposed method reduces the overall complexity of the iterative motor design and optimization process. The implementation of the method is demonstrated with a case study wherein a 110 kW three-phase induction motor is designed for an electric bus using the NREL drive cycle. The performance of the motor is verified using a finite element analysis motor using Maxwell ANSYS. The work described in this article was motivated by the complexities of the iterative motor design process, which

Makkar, Yash, Kumar, Rajendra, Sah, Bikash, Kumar, Praveen

Ferroelectric Material Adapts to Light Stimuli by Reorganizing Atomic Connectivity

TBMG-53548

08/01/2025

“Today’s supercomputers and data centers demand many megawatts of power,” said Haidan Wen, a Physicist at the U.S. Department of Energy (DOE) Argonne National Laboratory. “One challenge is to find materials for more energy-efficient microelectronics. A promising candidate is a ferroelectric material that can be used for artificial neural networks as a component in energy-efficient microelectronics.”

Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles

EPR2025014

07/18/2025

Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and

Zheng, Ling

Integrating High-Fidelity Urea–Water Solution—Computational Fluid Dynamics Simulations for Fast Three-Dimensional Selective Catalytic Reactor Modeling Using Artificial Neural Networks

03-18-05-0030

07/12/2025

This article presents a novel approach to enhance the accuracy and efficiency of three-dimensional (3D) selective catalytic reduction (SCR) simulations in monolith reactors by leveraging high-fidelity urea–water solution computational fluid dynamics (UWS-CFD) data. The focus is on estimating the nonuniformity of NH₃ at the SCR inlet, crucial for achieving optimal performance in aftertreatment systems. Due to its high computational cost, a CFD-only approach is not feasible for transient drive cycle simulations aiming to accurately predict SCR NOx conversion and NH₃ slip by accounting for the nonuniform NH₃ distribution at the SCR inlet. Therefore, the development of reduced order or fast models is of prime importance. By employing artificial neural networks (ANNs), we establish a framework that eliminates the need for computationally expensive CFD calculations, allowing for swift and precise 3D SCR simulations under various injection, mixing region, and exhaust conditions. The

Mishra, Rohit, Gundlapally, Santhosh, Wahiduzzaman, Syed

Data-Driven Vehicle Specification of Conventional and Electric Heavy-Duty Commercial Trucks for Improved Energy Consumption

02-18-03-0018

07/09/2025

In the heavy-duty commercial trucks sector, selecting the most energy-efficient vehicle can enable great reductions of the fleet operating costs associated with energy consumption and emissions. Customization and selection of the vehicle design among all possible options, also known as “vehicle specification,” can be formulated as a design space exploration problem where the objective is to find the optimal vehicle configuration in terms of minimum energy consumption for an intended application. A vehicle configuration includes both vehicle characteristics and powertrain components. The design space is the set of all possible vehicle configurations that can be obtained by combining the different powertrain components and vehicle characteristics. This work considers Class 8 heavy-duty trucks (gross combined weight up to 36,000 kg). The driving characteristics, such as the desired speed profile and the road elevation along the route, define the intended application. The objective of the

Villani, Manfredi, Pandolfi, Alfonso, Ahmed, Qadeer, Pianese, Cesare

Deep Reinforcement Learning–Based Control Strategy for Electro-Hydrostatic Active Suspension

15-18-03-0016

07/09/2025

A DRL (deep reinforcement learning) algorithm, DDPG (deep deterministic policy gradient), is proposed to address the problems of slow response speed and nonlinear feature of electro-hydrostatic actuator (EHA), a new type of actuation method for active suspension. The model-free RL (reinforcement learning) and the flexibility of optimizing general reward functions are combined with the ability of neural networks to deal with complex temporal problems through the introduction of a new framework called “actor-critic”. A EHA active suspension model is developed and incorporated into a 7-degrees-of-freedom dynamics model of the vehicle, with a reward function consisting of the vehicle dynamics parameters and the EHA pump–valve control signals. The simulation results show that the strategy proposed in this article can be highly adapted to the nonlinear hydraulic system. Compared with iLQR (iterative linear quadratic regulator), DDPG controller exhibits better control performance, achieves

Wang, Jiawei, Guo, Huiru, Deng, Xiaohe

Prediction of Hazardous Gaseous Emissions from a Gasoline Engine during Cold Starts Using Machine Learning Methods

2025-01-0321

07/02/2025

Internal combustion engines generate higher exhaust emissions of hazardous gases during the initial minutes after engine start. Experimental data from a state-of-the-art turbo-charged 3-cylinder, 999 cc gasoline engine are used to predict cold start emissions using two Machine Learning (ML) models: a Multilayer Perceptron (MLP) which is a fully connected neural network and an Encoder-Decoder Recurrent Neural Network (ED-RNN). Engine parameters and various temperatures are used as input for the models and NOx (Nitrogen Oxides), CO (Carbon monoxide) and unburned hydrocarbon (UHC) emissions are predicted. The dataset includes time series recordings from the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) and four Real Diving Emissions (RDE) cycles at ambient and initial engine temperatures ranging from -20 °C to +23 °C. In total, 21 cases are considered, consisting of eight different ambient temperatures and five distinct driving cycles. Each case consists of a sequence of 2500

Mangipudi, Manoj, Denev, Jordan A., Bockhorn, Henning, Trimis, Dimosthenis, Koch, Thomas, Debus, Charlotte, Götz, Markus, Zirwes, Thorsten, Hagen, Fabian P., Tofighian, Hesam, Wagner, Uwe, Braun, Samuel, Lanzer, Theodor, Knapp, Sebastian M.

Deep Learning–Based Prediction of Suspension Dynamics Performance in Multi-Axle Vehicles

15-18-03-0014

06/19/2025

This article is mainly to present a deep learning–based framework for predicting the dynamic performance of suspension systems for multi-axle vehicles, which emphasizes the integration of machine learning with traditional vehicle dynamics modeling. A multitask deep belief network deep neural network (MTL-DBN-DNN) was developed to capture the relationships between key vehicle parameters and suspension performance. Numerical simulation–generated data were utilized to train the model. This model also showed better prediction accuracy and computational speed compared to traditional deep neural network (DNN) models. Full sensitivity analysis has been performed in order to understand how different vehicle and suspension parameters may affect suspension dynamic performance. Furthermore, we introduce the suspension dynamic performance index (SDPI) in order to measure and quantify overall suspension performance and the effectiveness of multiple parameters. The findings highlight the

Lin, Bo-Yi, Lin, Kai-Chun

Design Space Exploration of a Continuous Rubber Track System via Surrogate Modeling

02-18-04-0023

05/26/2025

Continuous rubber track systems for heavy applications are typically designed using multiple iterations of full-scale physical prototypes. This costly and time-consuming approach limits the possibility of exploring the design space and understanding how the design space of that kind of system is governed. A multibody dynamic simulation has recently been developed, but its complexity (due to the number of model’s inputs) makes it difficult to understand and too expensive to be used with multi-objective optimization algorithms (approximately 3 h on a desktop computer). This article aims to propose a first design space exploration of continuous rubber track systems via multi-objective optimization methods. Using an existing expensive multibody dynamic model as original function, surrogate models (artificial neural networks) have been trained to predict the simulation responses. These artificial neural networks are then used to explore the design space efficiently by using optimization

Faivre, Antoine, Rancourt, David, Plante, Jean-Sébastien

Hybrid Approaches to Software Reliability: Evaluating and Enhancing Prediction Models

2025-01-5024

05/06/2025

Software reliability prediction involves predicting future failure rates or expected number of failures that can happen in the operational timeline of the software. The time-domain approach of software reliability modeling has received great emphasis and there exists numerous software reliability models that aim to capture the underlying failure process by using the relationship between time and software failures. These models work well for one-step prediction of time between failures or failure count per unit time. But for forecasting the expected number of failures, no single model will be able to perform the best on all datasets. For making accurate predictions, two hybrid approaches have been developed—minimization and neural network—to give importance to only those models that are able to model the failure process with good accuracy and then combine the predictions of them to get good results in forecasting failures across all datasets. These models once trained on the dataset are

Mahdev, Akash Ravishankar, Lal, Vinayak, Muralimohan, Pramod, Reddy, Hemanjaneya, Mathur, Rachit

Application of AI/ML in Hydrobush Tuning to Enhance Overall Value Proposition

2025-01-0132

05/05/2025

In the highly competitive automotive industry, optimizing vehicle components for superior performance and customer satisfaction is paramount. Hydrobushes play an integral role within vehicle suspension systems by absorbing vibrations and improving ride comfort. However, the traditional methods for tuning these components are time-consuming and heavily reliant on extensive empirical testing. This paper explores the advancing field of artificial intelligence (AI) and machine learning (ML) in the hydrobush tuning process, utilizing algorithms such as random forest, artificial neural networks, and logistic regression to efficiently analyze large datasets, uncover patterns, and predict optimal configurations. The study focuses on comparing these three AI/ML-based approaches to assess their effectiveness in improving the tuning process. A case study is presented, evaluating their performance and validating the most effective method through physical application, highlighting the potential

Hazra, Sandip, Khan, Arkadip Amitava

Mode Shape Identification Using Graph Neural Networks for Vehicle Structure Design

2025-01-0131

05/05/2025

This paper introduces a novel, automated approach for identifying and classifying full vehicle mode shapes using Graph Neural Networks (GNNs), a deep learning model for graph-structured data. Mode shape identification and naming refers to classifying deformation patterns in structures vibrating at natural frequencies with systematic naming based on the movement or deformation type. Many times, these mode shapes are named based on the type of movement or deformation involved. The systematic naming of mode shapes and their frequencies is essential for understanding structural dynamics and “Modal Alignment” or “Modal Separation” charts used in Noise, Vibration and Harshness (NVH) analysis. Current methods are manual, time-consuming, and rely on expert judgment. The integration of GNNs into mode shape classification represents a significant advancement in vehicle modal identification and structure design. Results demonstrate that GNNs offer superior accuracy and efficiency compared to

Tohmuang, Sitthichart, Swayze, James L., Fard, Mohammad, Fayek, Haytham, Marzocca, Piergiovanni, Bhide, Sanjay, Huber, John

A Comparative Analysis of Fault Diagnosis by Vibration Signals for Critical Gear Components in Electric Vehicle Motor Testing Machines Using Machine Learning Algorithms

2025-01-0040

05/05/2025

Electric vehicles (EVs) are shaping the future of mobility, with drive motors serving as a cornerstone of their efficiency and performance. Motor testing machines are essential for verifying the functionality of EV motors; however, flaws in testing equipment, such as gear-related issues, frequently cause operational challenges. This study focuses on improving motor testing processes by leveraging machine learning and vibration signal analysis for early detection of gear faults. Through statistical feature extraction and the application of classifiers like Wide Naive Bayes and Coarse Tree, the collected vibration signals were categorized as normal or faulty under both loaded (0.275 kW) and no-load conditions. A performance comparison demonstrated the superior accuracy of the wide neural networks algorithm, achieving 95.3%. This methodology provides an intelligent, preventive maintenance solution, significantly enhancing the reliability of motor testing benches.

S, Ravikumar, Sharik, N, Syed, Shaul, V, Muralidharan, D, Pradeep Kumar

A Comprehensive Review of Vehicle and Road Condition Estimation Techniques

10-09-02-0014

04/30/2025

This article reviews the key physical parameters that need to be estimated and identified during vehicle operation, focusing on two key areas: vehicle state estimation and road condition identification. In the vehicle state estimation section, parameters such as longitudinal vehicle speed, sideslip angle, and roll angle are discussed, which are critical for accurately monitoring road conditions and implementing advanced vehicle control systems. On the other hand, the road condition identification section focuses on methods for estimating the tire–road friction coefficient (TRFC), road roughness, and road gradient. The article first reviews a variety of methods for estimating TRFC, ranging from direct sensor measurements to complex models based on vehicle dynamics. Regarding road roughness estimation, the article analyzes traditional methods and emerging data-driven approaches, focusing on their impact on vehicle performance and passenger comfort. In the section on road gradient

Chen, Zixuan, Duan, Yupeng, Wu, Jinglai, Zhang, Yunqing

Road Surface Adaptive Stationary Steering Coordinated Control of Four-Wheel Hub Motor Vehicles

2025-01-5031

04/29/2025

The wheel hub motor–driven electric vehicle, characterized by its independently controllable wheels, exhibits high torque output at low speeds and superior dynamic response performance, enabling in-place steering capabilities. This study focuses on the control mechanism and dynamic model of the wheel hub motor vehicle’s in-place steering. By employing differential torque control, it generates the yaw moment needed to overcome steering resistance and produce yaw motion around the steering center. First, the dynamic model for in-place steering is established, exploring the various stages of tire motion and the steering process, including the start-up, elastic deformation, lateral slip, and steady-state yaw. In terms of control strategy, an adaptive in-place steering control method is designed, utilizing a BP neural network combined with a PID control algorithm to track the desired yaw rate. Additionally, a control strategy based on tire/road adhesion ellipse theory is developed to

Huang, Bin, Cui, Kangyu, Zhang, Zeyang, Ma, Minrui

Study on the Optimal Pre-Chamber Geometry for Active Pre-Chamber Gas Engines

2024-32-0023

04/18/2025

In a pre-chamber engine, fuel in the main-chamber is ignited and combusted by the combustion gas injected from the pre-chamber. Therefore, further fuel dilution is possible and thermal efficiency can be also improved. However, adding a pre-chamber to an engine increases the number of design parameters which have a significant impact on the main combustion and the exhaust gas. Then, in this study, the optimum geometry of the pre-chamber in an active pre-chamber gas engine was investigated. The considered parameters were the volume of pre-chamber, the diameter of a nozzle hole, and the number of nozzle holes. 18 types of pre-chambers with different geometries were prepared. Using these pre-chambers, engine experiments under steady conditions were conducted while changing the conditions such as engine speeds, mean indicated pressure and air excess ratio. Based on the experimental data, neural network models were constructed that predict thermal efficiency, NOx and CO emissions from the

Yasuda, Kotaro, Yamasaki, Yudai, Sako, Takahiro, Takashima, Yoshitane, Suzuki, Kenta

Development of a Robust Framework for Fault Detection and Quantification in Automotive Drum Brakes Using Vibration Measurements and ANN

10-09-03-0024

04/15/2025

Passenger safety is of utmost importance in the automotive industry. Hence, the health of the components, especially the brake system, should be effectively monitored. On account of the significance of artificial intelligence in recent times, any brake fault resulting during operation can be accurately detected using a combination of advanced measurement techniques and machine learning algorithms. The current study focuses on developing and evaluating a robust framework to quantify and classify the faults of a general automotive drum brake. For this purpose, a new experiment for a drum brake, which can be operated under a controlled environment with known levels of faults, is developed. The experiment is instrumented to measure the fundamental dynamic signals (such as brake torque, the angular velocity of the brake drum, and brake shoe accelerations) during a braking event. The response signals from several experiments with various faults and operating conditions serve as the input

Yella, Akash, Bharinikala, Yuva Venkat Ajay, Sundar, Sriram

Elongation Prediction of Die-Cast Aluminum Alloy Based on 3D Convolutional Neural Network Model

05-18-04-0032

04/09/2025

This study aims to predict the impact of porosities on the variability of elongation in the casting Al-10Si-0.3Mg alloy using machine learning methods. Based on the dataset provided by finite element method (FEM) modeling, two machine learning algorithms including artificial neural network (ANN) and 3D convolutional neural network (3D CNN) were trained and compared to determine the optimal model. The results showed that the mean squared error (MSE) and determination coefficient (R2) of 3D CNN on the validation set were 0.01258/0.80, while those of ANN model were 0.28951/0.46. After obtaining the optimal prediction model, 3D CNN model was used to predict the elongation of experimental specimens. The elongation values obtained by experiments and FEM simulation were compared with that of 3D CNN model. The results showed that for samples with elongation smaller than 9.5%, both the prediction accuracy and efficiency of 3D CNN model surpassed those of FEM simulation.

Zhang, Jin-sheng, Zheng, Zhen, Zhao, Xing-zhi, Gong, Fu-jian, Huang, Guang-sheng, Xu, Xiao-min, Wang, Zhi-bai, Yang, Yutong

Research on Dangerous Driving Behavior Recognition Algorithm Based on LSTM

2025-01-8204

04/01/2025

To address the issue of high accident rates in road traffic due to dangerous driving behaviors, this paper proposes a recognition algorithm for dangerous driving behaviors based on Long Short-Term Memory (LSTM) networks. Compared with traditional methods, this algorithm innovatively integrates high-frequency trajectory data, historical accident data, weather data, and features of the road network to accurately extract key temporal features that influence driving behavior. By modeling the behavioral data of high-accident-prone road sections, a comprehensive risk factor is consistent with historical accident-related driving conditions, and assess risks of current driving state. The study indicates that the model, in the conditions of movement track, weather, road network and conditions with other features, can accurately predict the consistent driving states in current and historical with accidents, to achieve an accuracy rate of 85% and F1 score of 0.82. It means the model can

Huang, Yinuo, Zhang, Miaomiao, Xue, Ming, Jin, Xin

Bi-Level Control Co-Design for Parallel Electric-Hydraulic Hybrid Vehicles

2025-01-8582

04/01/2025

This study presents a control co-design method that utilizes a bi-level optimization framework for parallel electric-hydraulic hybrid powertrains, specifically targeting heavy-duty vehicles like class 8 semi-trailer trucks. The primary objective is to minimize battery energy consumption, particularly under high torque demand at low speed, thereby extending both battery lifespan and vehicle driving range. The proposed method formulates a bi-level optimization problem to ensure global optimality in hydraulic energy storage sizing and the development of a high-level energy management strategy. Two nested loops are used: the outer loop applies a Genetic Algorithm (GA) to optimize key design parameters such as accumulator volume and pre-charged pressure, while the inner loop leverages Dynamic Programming (DP) to optimize the energy control strategy in an open-loop format without predefined structural constraints. Both loops use a single objective function, i.e. battery energy consumption

Taaghi, Amirhossein, Yoon, Yongsoon

Combine HD Map Information to Enhance Road Topology Reasoning by Offline Supervised Training and on-Line Ensemble Learning

2025-01-8021

04/01/2025

Topology reasoning plays a crucial role in understanding complex driving scenarios and facilitating downstream planning, yet the process of perception is inevitably affected by weather, traffic obstacles and worn lane markings on road surface. Combine pre-produced High-definition maps (HDMaps), and other type of map information to the perception network can effectively enhance perception robustness, but this on-line fused information often requires a real-time connection to website servers. We are exploring the possibility to compress the information of offline maps into a network model and integrate it with the existing perception model. We designed a topology prediction module based on graph attention neural network and an information fusion module based on ensemble learning. The module, which was pre-trained on offline high-precision map data, when used online, inputs the structured road element information output by the existing perception module to output the road topology, and

Kuang, Quanyu, Rui, Zhang, Zhang, Song, Yixuan, Gao

A Suspension Model Based on Semi-Recursive Method and Data-Driven Compliance Characteristics

2025-01-8282

04/01/2025

Many methods have been proposed to accurately compute a vehicle’s dynamic response in real-time. The semi-recursive method, which models using relative coordinates rather than dependent coordinates, has been proven to be real-time capable and sufficiently accurate for kinematics. However, not only kinematics but also the compliance characteristics of the suspension significantly impact a vehicle’s dynamic response. These compliance characteristics are mainly caused by bushings, which are installed at joints to reduce vibration and wear. As a result, using relative or joint coordinates fails to account for the effects of bushings, leading to a lack of compliance characteristics in suspension and vehicle models developed with the semi-recursive method. In this research, we propose a data-driven approach to model the compliance characteristics of a double wishbone suspension using the semi-recursive method. First, we create a kinematic double wishbone suspension model using both the semi

Zhang, Hanwen, Duan, Yupeng, Zhang, Yunqing, Wu, Jinglai

Development of Ultra-Fast AI-Driven Diesel Engine Model for Real-Time Optimization

2025-01-8397

04/01/2025

Combustion engines and hybrid systems remain important in sectors like light- and heavy-duty vehicles, where performance, range, or cost limitations play a major role. Optimizing diesel engine efficiency and reducing emissions is critical. However, classical physics-based 0D/1D models are computationally demanding and are hardly applicable for real-time purposes. In this study, a calibrated 1D diesel engine model is suggested for transformation into a neural network architecture to enable real-time optimization. The model divides the engine into intake, exhaust, and combustion sections, each modeled by different neural networks. One of the advantages of this modular and layered approach is the flexibility to change individual components without needing to retrain every single model. Long Short-Term Memory (LSTM) networks are used to capture transient phenomena, such as thermal inertias that arise in the combustion process and gas flow dynamics. The training data was generated by

Frey, Markus, Itzen, Dirk, Sautter, Johannes, Weller, Louis, Hagenbucher, Timo, Yang, Qirui, Grill, Michael, Kulzer, Andre Casal

Course of Action (COA) Generation for Robotic Military Ground Vehicles

2025-01-8338

04/01/2025

Course of action (COA) generation for robotic military ground vehicles is required to support autonomous operations in well-structured and non-structured environments. Traditional pathing algorithms such as Dijkstra, A*, Hybrid A*, or D* are exhaustive and well structured, and as a result, a single COA may be derived if one exists. Traditional path-planning algorithms have been optimized to identify paths that achieve a single scalar objective (duration, distance, energy, etc.). The algorithms are not natively able to account for multi-objective cost considerations. Military operations represent multi-objective optimization problems, impacted by time, space, and atmospherics. The battlefield is dynamic and ever-changing, thus pathing algorithms must incorporate multi-objective costs and constraints and be provided in near-real-time or real-time. For this reason, the use of a genetic algorithm (GA) and Artificial Intelligence/ Machine Learning (AI/ML) were investigated for COA

Jane, Robert, Ferrying, Zane, Janat, Tsegaye, James, Corey

Image-Based Machine Learning Methods in Materials Microstructure and Failure Analysis

2025-01-8324

04/01/2025

Image-based machine learning (ML) methods are increasingly transforming the field of materials science, offering powerful tools for automatic analysis of microstructures and failure mechanisms. This paper provides an overview of the latest advancements in ML techniques applied to materials microstructure and failure analysis, with a particular focus on the automatic detection of porosity and oxide defects and microstructure features such as dendritic arms and eutectic phase in aluminum casting. By leveraging image-based data, such as metallographic and fractographic images, ML models can identify patterns that are difficult to detect through conventional methods. The integration of convolutional neural networks (CNNs) and advanced image processing algorithms not only accelerates the analysis process but also improves accuracy by reducing subjectivity in interpretation. Key studies and applications are further reviewed to highlight the benefits, challenges, and future directions of

Akbari, Meysam, Wang, Andy, Wang, Qigui, Yan, Cuifen

Load-Deflection Evaluation of Bump Stoppers Using a Machine Learning Approach

2025-01-8208

04/01/2025

The accurate prediction and evaluation of load-deflection behavior in bump stoppers are critical for optimizing driving performance and durability in the automotive industry. Traditional methods, such as extensive experimental testing and finite element analysis (FEA), are often time-consuming and costly. This paper introduces a machine learning-based approach to efficiently evaluate load-deflection curves for bump stoppers, thereby streamlining the design and testing process. By leveraging a comprehensive dataset that includes historical test results, material properties, and geometrical dimensions, various machine learning methods, including Gradient boosting, Random forest & XG Boost were trained to predict load-deflection behavior with high accuracy. This approach reduces the reliance on extensive physical testing and simulations, significantly enhancing the design optimization process, leading to faster development cycles and more precise performance predictions. A case study is

Hazra, Sandip, Tangadpalliwar, Sonali

Driver Distraction Recognition Based on the Information Bottleneck Theory and Graph Convolutional Networks

2025-01-8112

04/01/2025

Driver distraction remains a leading cause of traffic accidents, making its recognition critical for enhancing road safety. In this paper, we propose a novel method that combines the Information Bottleneck (IB) theory with Graph Convolutional Networks (GCNs) to address the challenge of driver distraction recognition. Our approach introduces a 2D pose estimation-based action recognition network that effectively enhances the retention of relevant information within neural networks, compensating for the limited data typically available in real-world driving scenarios. The network is further refined by integrating the CTR-GCN (Channel-wise Topology Refinement Graph Convolutional Network), which models the dynamic spatial-temporal relationships of human skeletal data. This enables precise detection of distraction behaviors, such as using a mobile phone, drinking water, or adjusting in-vehicle controls, even under constrained input conditions. The IB theory is applied to optimize the trade

Zhang, Ji, Bai, Yakun

A Lateral Motion Controller for Smart Chassis Addressing Structural Uncertainty

2025-01-8197

04/01/2025

In order to make lateral motion robust and stable for smart chassis, a lateral motion controller is proposed in this paper taking structural uncertainty into account. Firstly, a new lateral error model is developed to describe the lateral motion problem. Secondly, the kernel of the lateral motion controller is active disturbance rejection control method, that is, a second order linear tracking differentiator is embedded between lateral error model and first order linear active disturbance rejection control (FO-LADRC). And every module of the lateral motion controller has been designed in detail, which contains first-order linear tracking differentiator (LTD1), second-order linear tracking differentiator (LTD2), linear extended state observer (LESO), and linear state error feedback (LSEF). Finally, six typical scenarios with two conditions are designed to validate the controller referencing to the test standard, considering the structural uncertainty, including wheelbase length and

Li, Yishuai, Lu, Jun, Xu, Shuiling, Ming, Jinghong, Yu, Qin, Wang, Xiaoliang, Zeng, Dequan, Hu, Yiming, Yu, Yinquan, Yang, Jinwen, Jiang, Zhiqiang

Hydro Bushing Model Identification Using Physics-Informed Neural Networks

2025-01-8263

04/01/2025

Advances in computer aided engineering and numerical methods have made modeling and analyzing vehicle dynamics a key part of vehicle design. Over time, many tools have been developed to model different vehicle components and subsystems, enabling faster and more efficient simulations. Some of these tools use simplified mathematical models to achieve the desired performance. These models depend on model identification methods to determine the parameters and structure that best represent a system based on observed data. This work focuses on the development of a model identification for hydro bushings, a crucial component in nearly all ground vehicles. It introduces an innovative approach to identifying the dynamic properties of hydro bushings using the rapidly evolving physics-informed neural networks. The developed physics-informed network incorporates physical laws into its training process, allowing for an improved mapping of a hydro bushing’s excitation to its dynamic response. The

Koutsoupakis, Josef, Ribaric, Adrijan, Nolden, Ingo, Karyofyllas, George, Giagopoulos, Dimitrios

Multi-Objective Optimization of the Electric Bus Body Frame Based on the PSO-BP-MOMVO Approach

2025-01-8652

04/01/2025

This paper focuses on the design optimization of a commercial electric bus body frame with steel-aluminum heterogeneous material orienting the performances of strength, crashworthiness and body lightweight. First, the finite element (FE) model of the body frame is established for static and side impact analysis, and the body frame is partitioned into several regions according to the thickness distribution of the components. The thicknesses of each region are regarded as the variables for the sensitivity analysis by combining the relative sensitivity method and the Sobol index method, and nine variables to which the performance indexes are more sensitive are selected as the final design variables for design optimization. Then the surrogate models are developed, and in order to improve the accuracy of the surrogate models, a model-constructing method called the particle swarm optimization BP neural network (PSO-BP) data regression prediction is proposed and formulated. In this method

Yang, Xiujian, Tian, Dekuan, Cui, Yan, Lin, Qiang, Song, Yi

Study on an Urban Ramp Driving Cycle Using Self-Organizing Map Neural Network

2025-01-8259

04/01/2025

To tackle the issue of lacking slope information in urban driving cycles used for vehicle performance evaluation, a construction method for urban ramp driving cycle (URDC) is formulated based on self-organizing map (SOM) neural network. The fundamental data regarding vehicles driving on typical roads with urban ramp characteristics and road slopes were collected using the method of average traffic flow, which were then pre-processed and divided into short-range segments; and twenty parameters that can represent the operation characteristics of vehicle driving on urban ramp were selected as the feature parameters of short-range segments. Dimension of the selected feature parameters was then reduced by means of principal component analysis. And a SOM neural network was applied in cluster analysis to classify the short-range segments. An URDC with velocity and slope information were constructed by combination of short-range segments with highly relevant coefficients according to the

Yin, Xiaofeng, Wu, Zhimin, Liang, Yiming, Wang, Peng, Xie, Yu

Hyperparameter-Optimized Neural Network for Precise Battery State of Charge Estimation

2025-01-8210

04/01/2025

Accurate estimation of the state of charge (SoC) of battery cells is crucial for the efficient management and longevity of battery systems, particularly in electric vehicles and renewable energy storage. This paper presents an approach utilizing a nonlinear autoregressive exogenous (NARX) model to estimate the SoC of battery cells. The proposed method leverages hyperparameter optimization to determine the optimal configuration of the neural network, including the number of neurons, the number of hidden layers, the number of feedback loops, the best activation function, and the most effective learning rate. The primary objective of this research is to minimize the estimation error of the SOC to within 2%, thereby enhancing the reliability and performance of battery management systems. The hyperparameter optimization process involves a systematic search and evaluation of various configurations to identify the most effective neural network architecture. This process is critical as it

Saini, Sandeep, Admane, Chinmay

Integrated Nonlinear Observer and Prescribed Performance Control for a Four In-Wheel Motor-Driven Electric Vehicles Based on Stability Boundary and Road Information

2025-01-8298

04/01/2025

To effectively improve the performance of chassis control of a four in-wheel motor (IWM)-driven electric vehicles (EVs), especially in combing nonlinear observer and chassis control for improving road handling and ride comfort, is a challenging task for the IWM-driven EVs. Simultaneously, inaccurate state-based control and uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IWM-driven EVs become a hot topic in both academia and industry. To issue the above mentioned, the paper proposes a novel observer-based prescribed performance control to improve IWM-driven EVs chassis performance under the double lane change steering. Firstly, a nonlinear nine degree-of-freedom of full-car model is developed to describe vehicle chassis dynamics, and the proposed

Wang, Zhenfeng, Long, Jiarong, Li, Shengchong, Zhang, Xiaoyang, Zhao, Binggen

Neural Network Based Modeling and Model Predictive Control for Reduction in Diesel Emissions

2025-01-8369

04/01/2025

The paper illustrates the process and steps in the development of a neural network-based economic Model Predictive Control (MPC) strategy for reducing diesel engine feed gas emissions. This MPC controller performs fuel limiting and modifies intake manifold pressure and exhaust gas recirculation (EGR) rate set-points to the inner loop air path controller to reduce engine-out oxides of nitrogen (NOx) and Soot emissions. We examine two Recurrent Neural Network (RNN) options for a control-oriented emissions model which are based on a multi-layer perception (MLP) architecture and a long short-term memory (LSTM) architecture. These RNN models are trained for use as prediction models in MPC. Both models are defined in input-output form, assuming that measurements/estimates of current values of NOx and Soot are available. We discuss and compare their training using PyTorch. The formulation of economic MPC is detailed, including the definition of the cost function and soft constraints

Zhang, Jiadi, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato

Fuel Consumption Estimation Using Spatio-Temporal Modeling and Traffic Flow Predictions: A Comparative Analysis

2025-01-8101

04/01/2025

Effective traffic management and energy-saving techniques are increasingly needed as metropolitan areas grow and traffic volumes rise. This work estimates fuel consumption over three selected routes in an urban context using spatio-temporal modeling essentially building on a previously developed approach in traffic prediction and forecasting. A weighted adjacency matrix for a Graph Neural Network (GNN) is constructed in the original approach which combines graph theory frameworks with travel times obtained from average speeds and distances between traffic count stations. Next, the traffic flow estimate uncertainty is measured using Adaptive Conformal Prediction (ACP) to provide a more reliable forecast. This work predicts fuel consumption under different scenarios by utilizing Monte Carlo simulations based on the expected traffic flows providing insights into energy efficiency and the best routes to take. The study compares passenger vehicles' and heavy-duty trucks' mean fuel

Patil, Mayur, Moon, Joon, Hanif, Athar, Ahmed, Qadeer

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