Modern vehicle integration has become exponentially more difficult due to the complicated structure of designing wiring harnesses for multiple variants that have diverse design iterations and requirements. This paper proposes an AI-driven solution for addressing variant complexity. By using Convolutional Networks and Deep Neural Networks (CNN & DNN) to generate harness routing using defined specifications and constraints, the proposed solution uses minimal human intervention, substantially less time, and enables less complexity in designing. AI trained modelled systems can generally even predict failures in production methods which also reduces downtime and increases productivity. The new AI system automatically converts design specifications to manufacturable design specifications to avoid confusion with design parameters, by optimizing concepts with connector placements, grommet fittings, clip alignments, and other tasks. The solution coping with the inherent dynamic complexity of

N, Rishi Kumaar, Patil R, Bharath, Rajavelu, Vivek, Ramachandran, Vignesh, Mohanty, Lalit, Padmarajan, Vishnu

AI/ML Based Audio Classification and Transcription for Automotive and Off-Highway Systems

2025-28-0323

11/06/2025

Modern automotive systems generate a wide range of audio-based signals, such as indicator chimes, turn signals, infotainment system audio, navigation prompts, and warning alerts, to facilitate communication between the vehicle and its occupants. Accurate Classification and transcription of this audio is important for refining driver aid systems, safety features, and infotainment automation. This paper introduces an AI/ML-powered technique for audio classification and transcription in automotive environments. The proposed solution employs a hybrid deep learning architecture that leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs), trained using labeled audio samples. Moreover, an Automatic Speech Recognition (ASR) model is integrated for transcribing spoken navigation prompts and commands from infotainment systems. The proposed system delivers reliable results in real-time audio classification and transcription, facilitating better automation and

Singh, Shwetha, Kamble, Amit, Mohanty, Ananta, Kalidas, Sateesh

A Physics-Guided Neural Network Approach for EGR Cooler Temperature Estimation: Addressing ECU Model Discrepancies

2025-28-0393

10/30/2025

As the automotive industry explores alternative powertrain options to curb emissions, it is pertinent to refine existing technologies to improve efficiency. The Exhaust Gas Recirculation (EGR) system is one of the pivotal components in emission control strategies for Internal Combustion Engines (ICE). The EGR cooler is crucial in thermal management strategies, as it lowers the temperature of recirculated exhaust gases before feeding it along with fresh air, thereby reducing nitrogen oxides (NOx) emissions. Precise estimation of the EGR cooler outlet temperature is crucial for effective emission control. However, conventional Engine Control Unit (ECU) models fall short, as they often show discrepancies when compared to real-world test data. These models rely on empirical relationships that struggle to capture precisely the transient effect, and real time variation in operating conditions. To address these limitations and improve the accuracy of ECU based model, various signal processing

Kumar, Amit, Kumar, Raman, Manojdharan, Arjungopal, Challa, Krishna, Kramer, Markus

Optimizing Electric Vehicle Motor Testing with Fine Gaussian SVM and Bilayered Neural Network

2025-28-0397

10/30/2025

Electric vehicles are shaping the future of the automotive industry, with the drive motor being a crucial component in their operation. Ensuring motor reliability requires rigorous testing using specialized test benches to validate key performance parameters. However, inefficiencies in the helical gear configuration within these test systems have led to frequent malfunctions, affecting production flow. This study focuses on optimizing the motor test bench by refining critical design parameters through vibration signal analysis and machine learning techniques. Vibrational data is collected under different gear configurations, utilizing an accelerometer integrated with a Data Acquisition (DAQ) system and MATLAB-based directives for seamless data collection. Machine learning classifiers, including Fine Gaussian SVM and Bilayered Neural Network, are applied to categorize signals into normal and faulty conditions, both with and without a 0.25 KW load. The analysis reveals that SVM achieves

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

Smart Energy Management System for Hybrid Electric Vehicles: Integrating Fuel Cells and Ultra-Capacitors using Neural Network

2025-28-0365

10/30/2025

This paper offers a state-of-the-art energy-management strategy specifically developed for FCHEV focusing on robustness under uncertain operations. Currently, energy management strategies try to optimize fuel economy and take into account the sluggish response of fuel cells (FCs); however, they mostly do so assuming all system variables are explicit and deterministic. In real-world operations, however, a variety of sources may cause the uncertainty in power generation, energy conversion, and demand interactions, e.g., the variation of environmental variables, estimated error, and approximation error of system model, etc., which accumulates and adversely impacts the vehicle performance. Disregarding these uncertainities can result in overestimation of operating costs, overall efficiency and overstepped performance limitations, and, in serious cases can cause catastrophic system breakdown. To mitigate these risks, the current work introduces a neural network-based energy management

Deepan Kumar, Sadhasivam, M, Boopathi, R, Vishnu Ramesh Kumar, Karthick, K N, R, Nithiya, R, Krishnamoorthy, V, Dayanithi

Diagnostics Methodology for Turbocharger System Using Data Driven Models

2025-28-0404

10/30/2025

In a conventional powertrain driven by Internal combustion (IC) engines, turbocharger (TC) is a key component for enhancing performance and efficiency. Predominantly turbochargers are used to serve multiple purposes of downsizing, increased power, better fuel efficiency, reduced emissions, and improved performance at high altitudes. TC is responsible for fulfilling the air mass requirement of the engine at different operating conditions. Failure of TC system leads to abnormal engine operation. If the TC hardware is beyond repair, the associated replacement cost is very high. Ultimately, a predictive diagnostics approach is required to identify the issue with TC so that the failure of TC could be avoided. The proposed methodology uses advanced artificial intelligence technique called recurrent neural network (RNN) and long short-term memory (LSTM) network for predicting faults in a typical TC system. In this study, actual values of TC speed and boost pressure are obtained from physical

Jagtap, Virendra Shashikant, Ganguly, Gourav, Mitra, Partha, Patidar, Sachin

Research on the Layout and Robustness Testing of Electric Vehicle Battery Swap Stations Based on Graph Neural Networks: A Case Study of the Main Urban Area of Nanchang City

2025-99-0008

10/17/2025

With the rapid increase in the number of electric vehicles, the rational placement of battery swapping stations has become a critical issue in optimizing urban transportation infrastructure. This paper proposes a site selection optimization method based on Graph Neural Networks (GNN). By constructing an urban transportation graph model grounded in Points of Interest (POI) and road traffic data, the method analyzes battery swapping station layout plans and validates their robustness and scalability. Taking the main urban area of Nanchang City as a case study, the research integrates data on POI distribution and land-use functional diversity within buffer zones to construct a graph structure. It then employs GNN for node classification to identify optimal battery swapping station locations. Experimental results show that, compared to traditional methods, the proposed approach improves site selection accuracy by 15% and enhances optimization efficiency by 20%. This method can provide

Zeng, Yi, Yi, Xinyu

Strength Prediction of Self-Piercing Riveted Joints Using Practical Regression and Bayesian Neural Network

2025-01-5068

10/10/2025

Self-piercing riveting (SPR) is a key joining method in multi/thin-material automotive structures, yet accurately predicting the mechanical strength of SPR joints remains challenging due to numerous influencing factors. Empirical engineering equations [1] provide a foundation for estimating lap-shear and cross-tension strength but require several geometric parameters that are often unavailable in the design phase. To address this limitation, we extract and leverage the core physical relationships embedded in these formulas. By reformulating the dependence of joint strength on the yield strength and total thickness of the sheet stack as practical regression models, we enable strength prediction using only commonly available material properties. Furthermore, a Bayesian convolutional neural network (BCNN) model is developed to incorporate additional material features, offering improved prediction accuracy and uncertainty quantification.

Soproni, Istvan, Womack, Darren, Liu, Zongyue, Balaji, Ashwin, Kulange, Deepak

Deep Reinforcement Learning with Behavioral Cloning for Path Tracking of Four-Wheel Steering Vehicles

10-10-01-0001

10/09/2025

When the vehicle system performs trajectory tracking control, it presents relatively complex nonlinear coupling dynamics characteristics. The traditional coordination algorithm relying on a simplified linear model is mostly unable to deal well with the actual nonlinear dynamic behaviors. In contrast, reinforcement learning (RL) method will derive the optimal strategy by means of interaction with the environment. This eliminates the need for accurate vehicle modeling. These methods use all of the nonlinear approximation capabilities of deep neural networks and can effectively reflect the complex relationship between vehicle state and control actions. The framework itself supports multidimensional input processing and continuous operation space optimization because of the development of parallel processing architectures. In order to reduce the motion jitter caused by the direct generation of front and rear wheel angles by the network, this article uses steering angle increments as

Ren, Gaotian, Wang, Yangyang

ML-Based System Level Optimization of EV Cooling Circuit

2025-01-0376

10/07/2025

Efficient thermal management is vital for electric vehicles (EVs) to maintain optimal operating temperatures and enhance energy efficiency. Traditional simulation-based design approaches, while accurate, are often computationally expensive and limited in their ability to explore large design spaces. This study introduces a machine learning (ML)-based optimization framework for the design of an EV cooling circuit, targeting a 5°C reduction in the maximum electric motor temperature. A one-dimensional computational fluid dynamics (1D-CFD) model is utilized to generate a Design of Experiments (DOE) matrix, incorporating key parameters such as coolant flow rate and heat exchanger dimensions. A Radial Basis Function (RBF) neural network is trained on the simulation data to serve as a surrogate model, enabling rapid performance prediction. Optimization is performed using the Non-Dominated Sorting Genetic Algorithm II (NSGA2), yielding three distinct design solutions that meet the thermal

Paul, Kavin, Ganesan, Arul, Mansour, Youssef

Fine-Tuning Yolov8 for Accurate Unmanned Aerial Vehicle Detection with Digital Twin Simulation

2025-01-0473

09/16/2025

Drones, or Unmanned Aerial Vehicles (UAVs) pose an increasing threat to military ground vehicles due to their precision strike capabilities, surveillance functions, and ability to engage in electronic warfare. Their agility, speed, and low visibility allow them to evade traditional defense systems, creating an urgent need for advanced AI-driven detection models that quickly and accurately identify UAV threats while minimizing false positives and negatives. Training effective deep-learning models typically requires extensive, diverse datasets, yet acquiring and annotating real-world UAV imagery is expensive, time-consuming, and often non-feasible, especially for imagery featuring relevant UAV models in appropriate military contexts. Synthetic data, generated via digital twin simulation, offers a viable approach to overcoming these limitations. This paper presents some of the work Duality AI is doing in conjunction with the Army’s Program Executive Office Ground Combat Systems (PEO GCS

Mejia, Felipe, Shah, Sunil, Young, Preston C., Brunk, Andrew T.

Commander’s Decision Analysis Tool for Maintenance: Applications of Unsupervised Learning with Pattern Characterization (ULPC) in Vehicle Health Assessment

2025-01-0490

09/16/2025

Within the military maintenance cycle, commanders and units struggle with understanding the operational readiness of their fleets from a data driven perspective. Many unsupervised learning techniques have been developed with applications for vehicle maintenance with pattern classification. In this paper, Predictive Maintenance using Unsupervised Learning with Pattern Characterization (ULPC) is proposed to classify the overall health of the platform system and subsystems. In this model, the key features are selected using an intelligent pre-processing system for signal classification for each subsystem. Next the data is processed and compared to a normalcy baseline dataset using the unsupervised machine learning (ML) model. Operational data collected post-baseline is then processed through a Recurrent Neural Network (RNN) and clustered. An overall “normalcy” metric is calculated to show the difference in operation when compared to the baseline patterns. This normalcy servers as an

Bailey, Jeffrey, Cabrey, Connor, Hsu, Charles

Using Artificial Neural Networks for Predicting Vehicle Survivability within a Virtual Simulation

2025-01-0482

09/16/2025

A unique contribution the U.S. Army currently provides is what is known as Virtual Experiments (VEs). A VE consists of a large group of active-duty soldiers who participate in a video game simulating a battlefield scenario. During these simulations, the soldiers are provided with novel protective vehicle capabilities in an effort to evaluate their effectiveness on the battlefield. However, these VEs take a significant amount of time to conduct and are expensive. Using Artificial Neural Networks (ANNs) this study looks to predict vehicle survivability based on a limited amount of VE data. The results entail an overall predictive accuracy of 76.8% using only two ANN input features and provides a framework for the eventual addition of more VE datasets.

O’Bruba, Joseph

Leveraging Spectral Unmixing for Improved Mobility in Vegetated Environments

2025-01-0480

09/16/2025

The success of off-road missions for ground vehicles depends heavily on terrain traversability, which in turn requires a thorough understanding of soil characteristics a key component being soil moisture content. When large areas need to be analyzed, satellite imagery is often used, although this approach typically reduces the spatial resolution. This decrease of spatial resolution creates what are known as mixed pixels, when two or more classes or features are in a single pixel’s area, which can lead to noisier data and lower accuracy models. This paper investigates using linear spectral unmixing as a way to help clean / mitigate noisy data to yield better predictive models. Hyperspectral remote sensing from the Hyperion satellite platform and ground truth from the International Soil Moisture Network (ISMN) are used for the dataset. This study found that soil moisture content prediction, comparing the mixed multilayer perceptron (MLP) model with an unmixing approach revealed a 10–30

Ewing, Jordan, Jayakumar, Paramsothy, Kasaragod, Anush, Oommen, Thomas

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

ML-based Identification of Lactones for Blend Studies and Combustion Performance in a Spark-Ignition CFR Engine

2025-24-0071

09/07/2025

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

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

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

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

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.

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

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

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

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

Items per page:

1 – 50 of 1375