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

Experimental Simulation of a Real Mission for Identifying a Telematics-Based Diagnostic Model in Connected Light Commercial Vehicles

2025-24-0124

09/07/2025

Remote monitoring of commercial vehicles is taking an increasingly central position in automotive companies, driven by the growth of the on-road freight transportation sector. Specifically, telematics devices are increasingly gaining importance in monitoring powertrain operability, performance, reliability, sustainability, and maintainability. These systems enable real-time data collection and analysis, offering valuable support in resolving issues that may occur on the road. Moreover, the fault codes, called Diagnostic Trouble Codes (DTCs), that arise during actual road driving constitute fundamental information when combined with several engine parameters updated every second. This integration provides a more accurate assessment of vehicle conditions, allowing proactive maintenance strategies. The principal goal is to deliver an even faster response for resolving sudden issues, thus minimizing vehicle downtime. High-resolution data transmission and failure event information

D'Agostino, Valerio, Cardone, Massimo, Mancaruso, Ezio, Rossetti, Salvatore, Marialto, Renato

Influence of MPC Based Driver Types on the Energy Consumption of Battery Electric Vehicles

2025-24-0113

09/07/2025

Electric vehicles are increasingly important for emission reduction and the promotion of sustainable mobility. Despite their advantages over conventional vehicles, the energy consumption of electric vehicles is heavily influenced by various factors such as driving behavior, elevation profile, and environmental conditions. In particular, the driving style plays a crucial role in determining range and energy consumption. This influence is also observed in the context of the Interreg project FreeE-Bus. This project focuses on the development of optimized charging management for electric buses in the public transport system of the Lake Constance region. Due to strict data protection regulations that prevent a detailed analysis of driver data, assessing the impact of driving styles is difficult. This paper addresses this issue by developing an innovative driver model that simulates different driver types and analyzes their effects on energy consumption. The driver model employs a Model

Konzept, Anja, Reick, Benedikt, Miller, Marius, Rautenberg, Philip, Störzer, Martin

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

Adaptive Path Planning and Control of Autonomous Sweeping Vehicle Considering the Error of Perception

10-09-04-0035

08/19/2025

This article presents a path planning and control method for a cost-effective autonomous sweeping vehicle operating in enclosed campus. First, to address the challenges from perception, an effective obstacle filtering algorithm is proposed, considering the elimination of false detection and correction of object position. Based on it, the adaptive sampling–based path planner and pure pursuit controller are developed. Not only an adaptive cost-weighting mechanism is introduced by TOPSIS algorithm to determine the desired trajectory as a multi-objective optimization problem, but also the adaptive preview distance is designed according to the trajectory curvature and vehicle state. The real-vehicle tests are implemented in typical scenario. The results show that the 87.8% effective edge-following rate is achieved in curved paths, and 22.93% cleaning coverage is improved for cleaning coverage. Therefore, the proposed method is effective and reliable for cost-effective autonomous sweeping

Lei, WuKun, Yang, Bo, Pei, Xiaofei, Zhang, Yang, Zhou, HongLong

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

Machine Learning Applications in the Development and Analysis of Suspension Systems of Road Vehicles

2025-36-0330

08/01/2025

This paper aims to explore the application of machine learning techniques to the analysis of road suspension systems, with particular emphasis on mechanical leaf spring suspensions. These systems are essential for vehicle performance, as they guarantee comfort and stability while driving, and they have an intrinsically complex and non-linear dynamic behavior. Because of this complexity, traditional approaches often prove costly and insufficient to represent operating conditions. In this context, machine learning techniques stand out for their ability to learn patterns from experimental data, allowing the modelling of non-linear phenomena that characterize road implement suspensions. One of the main contributions of this study is the demonstration that machine learning algorithms are capable of identifying complex patterns to represent the behavior of the system, as well as facilitating the detection of anomalies and potential faults in the suspension system, contributing to predictive

Colpo, Leonardo Roso, Molon, Maicon, Gomes, Herbert Martins

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.”

Development of a SaaS Platform for Fleet Management

2025-36-0331

08/01/2025

Technological solutions to monitor and manage fleets are important to increase efficiency and reduce cargo transport costs. This work presents a SaaS (Software as a Service) platform for fleet management, aimed at optimizing operational efficiency and improving vehicle safety. It distinguishes itself as an innovative solution by integrating various functionalities related to cargo transport into a unified environment. The platform allows for route tracking, with different alert notifications generated from sensors, virtual geographic fences, driver identification, and smart cameras. Tire management is another critical aspect that encompasses the unique identification of each tire and its association with vehicles, along with monitoring data such as mileage, speed, temperature, pressure, tread wear, retreading, and performance based on distance traveled. Alerts for tire rotation, tread depth measurements, and excessive tread wear enhance performance management, while key performance

Fonseca, Murilo L., Mochiutti, Eric, Rosa, Rodrigo K., Benczik, Paulo H., Gonçalves, Vitor M., Zanolli, Willians S.

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

Brief Communication: Measurement of Boundary Layer Turbulent Transition Using an Acoustic Microphone Unit

01-18-02-0011

07/16/2025

In this article the transition of a laminar boundary layer (BL) over a flat plate is characterized using an acoustic technique with a pitot probe linked to a microphone unit. The probe was traversed along a BL plate at a fixed wind tunnel flow velocity of 5.5 m/s. A spectral analysis of the acoustic fluctuations showed that this setup can estimate the streamwise location and length of the BL transition region, as well as the BL thickness, by using the intermittency similitude approach. Further work is required to quantify the uncertainty caused by signal attenuation within the data acquisition system.

Lawson, Nicholas John, Zachos, Pavlos K.

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

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