With the introduction of the Euro 7 regulation, non-exhaust emissions – particularly those arising from brake and tire abrasion – will be regulated and subject to emission limits for the first time. This presents significant challenges not only for OEMs striving to meet these targets within the given timeframe, but also for suppliers, who must develop innovative solutions for the precise measurement, analysis, and mitigation of these emissions. To address this, it is essential to establish and industrialize new testing methodologies as structured, scalable, and cost-efficient processes. Beyond pure measurement capability, service providers in this domain are increasingly expected to serve as feedback mechanisms – identifying process limitations, proposing targeted improvements, and thereby enabling continuous development in line with evolving technical and regulatory requirements. In this context, AVL is pursuing a holistic development strategy that integrates brake emission

Grojer, Bernd

Innovative Full-GaN Inverter Development for 800V/100kW Automotive Applications

2025-24-0133

09/07/2025

In the rapidly advancing field of EV applications, the design of high-efficient inverters is one of the key factors in improving overall vehicle performance. This paper presents the design of a three-level (3-L) automotive inverter based on GaN technology, aimed at enhancing the performance and efficiency of electric vehicles (EVs). GaN components, sourced from Cambridge GaN Devices (CGD), are utilized to leverage their superior switching characteristics and efficiency. The work is supported by both simulation and experimental results, which confirm the advantages of integrating GaN components and the 3-L inverter topology. The findings demonstrate improved performance, lower losses, and enhanced overall efficiency, making this design a promising solution for the future of EV power electronics.

Battiston, Alexandre, Aghaei Hashjin, Saeid, Findlay, John, Haje Obeid, Najla, Siad, Ines

Methodological Approach to Battery Electric Vehicle Design: Energy-Based Model Development from a Detailed Digital Twin

2025-24-0098

09/07/2025

On the path to the decarbonization of the transport sector, the development of electric vehicles (EVs) is crucial to meeting the targets set by international regulatory bodies. EVs operate with zero tailpipe emissions and offer high energy efficiency and flexibility; however, challenges remain in achieving a fully sustainable electricity supply. In this context, powertrain design plays a fundamental role in determining vehicle performance and mission feasibility, which are strongly influenced by operating conditions and application characteristics, such as driving profiles and ambient temperature. A key challenge is the optimal sizing of components, particularly the battery pack and the electric motor. Therefore, a structured and methodological approach to powertrain design is essential to ensuring an optimal configuration. To this end, the project focuses on an integrated approach based on a master-and-slave modeling framework applied to a light-duty commercial vehicle at two levels

Bartolucci, Lorenzo, Cennamo, Edoardo, Grattarola, Federico, Lombardi, Simone, Mulone, Vincenzo, Tribioli, Laura, Aimo Boot, Marco

Advanced Energy and Thermal Management Control Strategy for Heavy-Duty Fuel Cell Powertrains

02-18-03-0022

08/14/2025

Medium- and heavy-duty fuel cell electric vehicles (FCEV) have gained attention over the battery electric vehicles, offering long vehicle range, fast refueling times, and high payload capacity. However, FCEVs face challenges of high upfront system cost and fuel cell system durability. To address the cost sensitivity of the fuel cell powertrain, it is imperative to maximize the operating efficiency of the energy and thermal management system while meeting the fuel cell durability requirements. This article presents an advanced adaptive control strategy for each of the energy and thermal management systems of a FCEV to maximize operating efficiency as well as vehicle performance. The proposed adaptive energy management strategy builds upon a real-time equivalent consumption minimization strategy (ECMS), which is updated based on a horizon prediction algorithm using GPS and navigation data of the route. The algorithm predicts the battery state of charge (SOC) for a defined horizon, which

Batool, Sadaf, Baburaj, Adithya, Sadekar, Gaurav, Joshi, Satyum, Franke, Michael

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

Structural Modification for Personally Licensed Vehicles to Meet the Transportation Needs of Persons with Disabilities

J1725_202506 (Current)

06/30/2025

This SAE Information Report applies to structural integrity, performance, drivability, and serviceability of personally licensed vehicles not exceeding 10000 pounds GVWR such as sedans, crossovers, SUVs, MPVs, light trucks, and van-type vehicles that are powered by gas and alternative fuel such as electric, plug-in hybrid, or hybrid technologies. It provides engineering direction to vehicle modifiers in a manner that does not limit innovation, and it specifies procedures for preparing vehicles to enhance safety during vehicle modifications. It further provides guidance and recommendations for the minimum acceptable design requirements and performance criteria on general and specific structural modifications, thereby allowing consumers and third-party payers the ability to obtain and purchase equipment that meets or exceeds the performance and safety of the OEM production vehicle.

Adaptive Devices Standards Committee

Developing Military Light Utility Vehicle Performance Based on Air Semi-Active Suspension System Using Recurrent Neural Network–Based Controller

10-09-04-0030

06/19/2025

This research presents a semi-active suspension system that combines an air spring and a magneto-rheological (MR) fluid damper to produce both active force and variable damping rates based on the road conditions. The suspension system used for the military light utility vehicle (MLUV) has seven degrees of freedom. A nonlinear model predictive control system generates the desired active force for the air spring control signal, while the linear quadratic regulator (LQR) estimates the target tracking of the intended damping force. The recurrent neural network is designed to develop a controller for an identification system. To achieve the optimal voltage for the MR damper without log time, it is used to simultaneously determine the active control force of the air spring by modifying the necessary damping force tracking. The MLUV suspension system is integrated with the traction control system to improve overall vehicle stability. A fuzzy traction controller adjusts the throttle angle

Shehata Gad, Ahmed

Optimal Onboard Battery Charging Strategy for Hotel Load Management in Mild-Hybrid Heavy-Duty Truck

02-18-03-0017

06/12/2025

Heavy-duty trucks idling during the hotel period consume millions of gallons of diesel/fuel a year, negatively impacting the economy and environment. To avoid engine idling during the hotel period, the heating, ventilation, and air-conditioning (HVAC) and auxiliary loads are supplied by a 48 V onboard battery pack. The onboard battery pack is charged during the drive phase of a composite drive cycle, which comprises both drive and hotel phases, using the transmission-mounted electric machine (EM) and battery system. This is accomplished by recapturing energy from the wheels and supplementing it with energy from the engine when wheel energy alone is insufficient to achieve the desired battery state of charge (SOC). This onboard battery pack is charged using the transmission-mounted EM and battery system during the drive phase of a composite drive cycle (i.e., drive phase and hotel phase). This is achieved by recapturing wheel energy and energy from the engine when the wheel energy is

Huang, Ying, Hanif, Athar, Ahmed, Qadeer

Automobile Wheel Alignment and Wheel Balancing, Second Edition

R-59106/12/2025

With over thirty years of hands-on experience in designing, developing, and patenting products in the wheel alignment industry, the author brings invaluable expertise to the second edition of this essential guide. Perfectly suited for mechanics, technicians, engineers, and even vehicle owners, this book goes beyond the basics to offer a comprehensive understanding of wheel alignment and its critical role in vehicle performance. The importance of proper wheel alignment and balancing cannot be overstated—incorrect alignment leads to premature tire wear and reduced safety. This updated edition provides in-depth coverage on wheel alignment techniques for light vehicles, commercial trucks, and trailers, with clear illustrations to help readers recognize and address various types of tire wear. The book explores everything from the mechanics of alignment to the impact of air pressure and tire rotation on longevity. With the growing need for vehicle maintenance, this guide empowers

R., Mananathan

NVH Improvement of BEV Transmission Using Housing Modal Analysis Co-Relation and Design Optimization

2025-01-0106

05/05/2025

Battery Electric Vehicles (BEVs) are extremely sensitive in terms of NVH requirements. While the engine is being replaced with an almost silent electric motor, the transmission noise appears persistent and demands more silent transmission. This has raised demand for improvement in design as well as manufacturing quality. Various innovations are being made to drive an improvement in the NVH. The following paper will discuss the improvement in NVH achieved through a design optimization of the housing using modal analysis. Firstly, the NVH results were co-related with the modal analysis and the cause for the dominant peak in amplitude of the NVH graph associated with the housing modes were mapped. A simple Excel based correlation matrix is used to map the list of all Eigenfrequencies of housing and its corresponding gear tooth frequency. Further optimization is done in housing design to defer the modal frequencies and another NVH test was run. It was proven that housing design

Pingale, Abhijeet Satish, Deshpande, Prasannakumar

Electromagnetic and NVH Analysis of PMSM with Eccentricity and Rotor Magnetization Variations

2025-01-0047

05/05/2025

Permanent magnet synchronous motors (PMSM) are among the most promising motors in electric vehicles due to their high torque density and efficiency. This paper is devoted to detailed electromagnetic investigations of permanent magnet synchronous motor, accounting for specific rotor eccentricity and uneven magnetization. A series of simulations are performed for a 90 HP interior PMSM to investigate the changes in the radial and tangential forces when the rotor is perfectly aligned or with static, dynamic, and mixed eccentricities. Besides, the influence of uneven magnetization due to manufacturing, demagnetization, and magnet deterioration is discussed. The forces are then used to load a vibro-acoustic model to evaluate the impact on the noise, vibration, and harshness (NVH) performance and predict the radiated sound power level for the different conditions.

Hadjit, Rabah, Kebir, Ahmed, Felice, Mario

Evaluation of Automotive Closure Design for High Frequency Acoustic Performance

2025-01-0049

05/05/2025

A good Noise, Vibration, and Harshness (NVH) environment in a vehicle plays an important role in attracting a large customer base in the automotive market. Hence, NVH has been given significant priority while considering automotive design. NVH performance is monitored using simulations early during the design phase and testing in later prototype stages in the automotive industry. Meeting NVH performance targets possesses a greater risk related to design modifications in addition to the cost and time associated with the development process. Hence, a more enhanced and matured design process involves Design Point Analysis (DPA), which is essentially a decision-making process in which analytical tools derived from basic sciences, mathematics, statistics, and engineering fundamentals are used to develop a product model that better fulfills the predefined requirement. This paper shows the systematic approach of conducting a Design Point Analysis-level NVH study to evaluate the acoustic

Ranade, Amod A., Shirode, Satish V., Miskin, Atul, Mahamuni, Ketan J., Shinde, Rahul, Chowdhury, Ashok, Ghan, Pravin

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

Pursuit of Realistic Vehicle Acceleration Sounds Based on Discomfort Index

2024-32-0055

04/18/2025

Contemporary Japanese society relies heavily on vehicles for transportation and leisure. This has led to environmental concerns owing to vehicle emissions, prompting a shift toward environmentally friendly alternatives, such as clean diesel and electric vehicles. Clean diesel vehicles aim to reduce harmful emissions, whereas electric vehicles are favored because of their minimal emissions and quiet operation. However, the lack of engine noise in electric vehicles can make it difficult for drivers to perceive speed changes, potentially increasing the risk of accidents, and simply amplifying all sounds is not viable because it may cause discomfort. Therefore, this study explored how deviations from expected engine sounds affect the perceived sound quality and vehicle performance assessment. Unlike traditional gasoline-powered and clean diesel vehicles, electric vehicles produce very little running noise, which makes road surface noise more prominent. Given the novelty of electric

Nitta, Misaki, Ishimitsu, Shunsuke, Fujikawa, Satoshi, Iwata, Kiyoaki, Niimi, Mayuko, Kikuchi, Masakazu, Matsumoto, Mitsunori

T.R.I.C.K. 2.0: Enhanced Vehicle Dynamics Analysis and Estimation Harnessing Advanced Vehicle Sensors

10-09-03-0025

04/09/2025

Automotive signal processing is dealt with in several contributions that propose various techniques to make the most out of the available data, typically for enhancing safety, comfort, or performance. Specifically, the accurate estimation of tire–road interaction forces is of high interest in the automotive world. A few years ago the T.R.I.C.K. tool was developed, featuring a vehicle model processing experimental data, collected through various vehicle sensors, to compute several relevant virtual telemetry channels, including interaction forces and slip indices. Following years of further development in collaboration with motorsport companies, this article presents T.R.I.C.K. 2.0, a thoroughly renewed version of the tool. Besides a number of important improvements of the original tool, including, e.g., the effect of the limited slip differential, T.R.I.C.K. 2.0 features the ability to exploit advanced sensors typically used in motorsport, including laser sensors, potentiometers, and

Napolitano Dell’Annunziata, Guido, Farroni, Flavio, Timpone, Francesco, Lenzo, Basilio

Learning-Based Energy Management Strategy of Fuel Cell City Buses Considering Power Source Degradation

02-18-02-0012

04/02/2025

Fuel cell vehicles (FCVs) offer a promising solution for achieving environmentally friendly transportation and improving fuel economy. The energy management strategy (EMS), as a critical technology for FCVs, faces significant challenges of achieving a balanced coordination among the fuel economy, power battery life, and durability of fuel cell across diverse environments. To address these challenges, a learning-based EMS for fuel cell city buses considering power source degradation is proposed. First, a fuel cell degradation model and a power battery aging model from the literature are presented. Then, based on the deep Q-network (DQN), four factors are incorporated into the reward function, including comprehensive hydrogen consumption, fuel cell performance degradation, power battery life degradation, and battery state of charge deviation. The simulation results show that compared to the dynamic programming–based EMS (DP-EMS), the proposed EMS improves the fuel cell durability while

Song, Dafeng, Yan, Jinxing, Zeng, Xiaohua, Zhang, Yunhe

Coordinated Control Strategy for Longitudinal, Lateral, and Vertical Motions of Distributed Electric Vehicles Based on Model Predictive Control

2025-01-8300

04/01/2025

Distributed electric vehicles, equipped with independent motors at each wheel, offer significant advantages in flexibility, torque distribution, and precise dynamic control. These features contribute to notable improvements in vehicle maneuverability and stability. To further elevate the overall performance of vehicles, particularly in terms of handling, stability, and comfort, this paper introduces an coordinated control strategies for longitudinal, lateral, and vertical motion of distributed electric vehicles. Firstly, a full-vehicle dynamics model is developed, encompassing interactions between longitudinal, lateral, and vertical forces, providing a robust framework for analyzing and understanding the intricate dynamic behaviors of the vehicle under various operating conditions. Secondly, a vehicle motion controller based on Model Predictive Control is designed. This controller employs a sophisticated multi-objective optimization algorithm to manage and coordinate several critical

Jia, Jinchao, Yue, Yang, Sun, Aobo, Liu, Xiao-ang

A Comparative Study of Optimization Algorithms for 6DOF Design in Electric Vehicle Powertrain Mounting Systems

2025-01-8650

04/01/2025

Optimizing engine mounting systems is a complex task that requires balancing the isolation of vehicle vibrations with controlling powertrain movement within a limited dynamic envelope. Six Degrees of Freedom (6DOF) optimization is widely used for mounting stiffness and location optimization. This study investigates the application of various optimization algorithms for 6DOF analysis in engine mount design, where the system’s stochastic behaviour and probabilistic characteristics present additional challenges. Selecting an appropriate optimization framework is essential for achieving accurate and efficient NVH results. Recent advancements in research have introduced several 6DOF optimization algorithms to determine the optimal stiffness and location of engine mounts. The study evaluates a range of optimization methods, including Simultaneous Hybrid Exploration that is Robust, Progressive and Adaptive (SHERPA), Quadratic Programming (QP), Genetic Algorithm (GA), Particle Swarm

Hazra, Sandip, Khan, Arkadip

Sensory-Driven Design Method for Personalizing Ride Comfort Tailored to Individual Vibration Perception

2025-01-8273

04/01/2025

This study aims to develop a design method that tailors the ride comfort and design variables of vehicle components according to individual differences in vibration perception. In conventional development, variations in vibration perception have been recognized; however, quantification methods remain undeveloped, preventing designs from being adapted to individual driver perceptions. The two unresolved problems include the uniformization of vibration perception in sensory performance modeling, which predicts sensory scores from vehicle vibrations, and design approaches that focus on minimizing vehicle vibrations without considering vibration perception. First, the authors’ previous study quantified the existence of individual differences in vibration perception through sensory scores obtained from ride simulator experiments involving 24 non-expert drivers using vibrations derived from a uniform vibration perception. Hierarchical clustering identified four perception groups; however

Kikuchi, Hironobu, Inaba, Kazuaki

Tube-MPC Based Path Tracking for I Vehicle Considering Data-Driven Stable Region and External Disturbances

2025-01-8262

04/01/2025

Path tracking is a key function of intelligent vehicles, which is the basis for the development and realization of advanced autonomous driving. However, the imprecision of the control model and external disturbances such as wind and sudden road conditions will affect the path tracking effect and even lead to accidents. This paper proposes an intelligent vehicle path tracking strategy based on Tube-MPC and data-driven stable region to enhance vehicle stability and path tracking performance in the presence of external interference. Using BP-NN combined with the state-of-the-art energy valley optimization algorithm, the five eigenvalues of the stable region of the vehicle β−β̇ phase plane are obtained, which are used as constraints for the Tube-MPC controller and converted into quadratic forms for easy calculation. In the calculation of Tube invariant sets, reachable sets are used instead of robust positive invariant sets to reduce the calculation. Simulation results demonstrates that the

Zhang, Haosen, Li, Yihang, Wu, Guangqiang

Active and Passive Security Generalization Algorithm and Typical Case Analysis

2025-01-8744

04/01/2025

In order to effectively predict the vehicle safety performance and reduce the cost of enterprise safety tests, a generalized simulation model for active and passive vehicle safety was proposed. The frontal driver-side collision model under the intervention of the Autonomous Emergency Braking (AEB) was created by using the MADYMO software. The collision acceleration obtained from the sled test was taken as the original input of the model to conduct simulation for the working conditions under different sitting postures of the human body. The injury values of various parts of the Hybrid III 50th dummy were read. Based on the correlation between the two, an active and passive simulation model was established through the Back Propagation (BP) neural network. The input of the model was the inclination angle centered on the dummy's waist, and the output was the acceleration of the dummy's head. The results showed that the comprehensive prediction accuracy rate exceeded 80%. Therefore, the

Ge, Wang, fengyao, LV

Evaluation of Different Powertrain Topologies for a Premium Plug-in Hybrid Electric Vehicle

2025-01-8531

04/01/2025

Plug-in hybrid electric vehicles combine the benefits of both battery electric and internal combustion engine drivetrains. There are multiple possibilities for hybrid configurations, each with its own advantages and disadvantages. In this study, two newly developed traction electric machines were employed alongside a gasoline engine in various hybrid configurations. These configurations, ranging from P1 to P4 and their combinations, were evaluated in terms of vehicle performance, energy consumption, and emissions. The impact of battery capacity was also examined. With a larger battery providing higher discharge power, the electric acceleration time significantly decreases from around 8.6 seconds to approximately 5.2 seconds as the battery capacity increases from 20 kWh to 40 kWh in configurations featuring two traction electric machines. In hybrid mode, the reduction in acceleration time is less pronounced, with a decrease of around 0.7 seconds compared to the configuration with a 20

Nguyen, Duc-Khanh, Tokat, Alexandra, Kristoffersson, Annika, Olsson, Jan-Ola

Stability-Based Coordinated Control for Distributed Drive Electric Vehicle Trajectory Tracking and Handling Stability

2025-01-8307

04/01/2025

To address the issue of poor yaw stability in distributed drive electric vehicles under extreme trajectory tracking conditions, this paper proposes a novel control approach that coordinates upper-layer trajectory tracking and stability control with lower-layer active front steering (AFS) and direct yaw moment control (DYC). Firstly, a stability domain boundary is defined in the β−β̇phase plane, and the instability factor is derived based on boundary line characteristics. This factor is used as a weight in the objective function to establish a model predictive control (MPC) for trajectory tracking and handling stability, thereby adjusting the control target weights for both objectives. Secondly, fuzzy logic is used to change the boundary of the phase plane transition field according to the vehicle state to dynamically adjust the intervention timing of the stability control, while AFS and DYC control are used to modify the front wheel steering angle and yaw moment control in the MPC

Dou, Jingyang, Wu, Jinglai, Zhang, Yunqing

Machine Learning-Based Digital Twin for Optimizing Automotive Components

2025-01-8206

04/01/2025

This paper presents a Digital Twin approach based on Machine Learning (ML), aimed at creating software-based sensors to reduce the auxiliary devices of the vehicle and enabling predictive maintenance, thus reducing carbon footprint. The solution is applied to the electric Lubrication Oil Pump (eLOP), a crucial component within a vehicle's powertrain system. The proposed eLOP Digital Twin integrates ML-based sensors to estimate critical parameters such as temperature, pressure and flow rate, reducing the reliance on physical sensors and associated hardware. This approach minimizes manufacturing complexity and cost, enhancing energy efficiency during both production and operation. Furthermore, the Digital Twin facilitates predictive maintenance by continuously monitoring the component's performance, enabling early detection of potential failures and optimizing maintenance schedules. This leads to lower energy consumption and reduced emissions throughout the component's lifecycle. The

Khan, Jalal, D'Alessandro, Stefano, Tramaglia, Federico, Fauda, Alessandro

Fully Automated Global Powertrain Optimization for Electric Truck Using Active Learning Algorithm and Backwards Simulation Approach

2025-01-8536

04/01/2025

Electric trucks, due to their weight and payload, need a different layout than passenger electric vehicles (EVs). They require multiple motors or multi-speed transmissions, unlike passenger EVs that often use one motor or a single-speed transmission. This involves determining motor size, number of motors, gears, and gear ratios, complicated by the powertrain system’s nonlinearity. The paper proposes using a stochastic active learning approach (Bayesian optimization) to configure the motors and transmissions for optimal efficiency and performance. Backwards simulation is applied to determine the energy consumption and performance of the vehicle for a rapid simulation of different powertrain configurations. Bayesian optimization, was used to select the electric drive unit (EDU) design candidates for two driving scenarios, combined with a local optimization (dynamic programming) for torque split. By optimizing the electric motor and transmission gears, it is possible to reduce energy

Chen, Bicheng, Wellmann, Christoph, Xia, Feihong, Savelsberg, Rene, Andert, Jakob, Pischinger, Stefan

Design and Evaluation of an Adjustable Ackermann Rate Steering System for Formula Student Racing Cars

2025-01-8000

04/01/2025

FSAE is a competition designed to maximize car performance, in which the steering system is a key subsystem, and the steering system performance directly affects the cornering performance of the car. The driver relies on the steering system for effective handling, which is also crucial for cornering and achieving faster lap times. Therefore, while improving the performance of the steering system, it is crucial to match the vehicle design to the driver's habits. Traditionally, steering systems typically use an Ackermann rate between 0% and 100% to offset the slip angle caused by tire deformation, thus achieving the purpose of reducing tire wear. Calculations have shown that a 40-60% Ackermann rate provides a similar compensation effect with little difference in tire wear. The traditional steering design method also does not consider the driver's driving habits and feedback, which is not conducive to the improvement of the overall performance of the car. In FSAE's figure-of-eight loops

Wu, Hailin, Li, Mingyuan

Study on Optimal Shift Schedule for Single-shaft Parallel PHEVs Considering Driver’s Performance Expectation

2025-01-8577

04/01/2025

To take into account the drivers’ performance expectations in the comprehensive performance optimization of plug-in hybrid electric vehicles (PHEVs), we proposed an optimization method for the shift schedule of single-shaft parallel PHEVs considering drivers’ demands on both dynamic and economic performance. In accordance with torque distribution strategies developed for different working modes, the modes switching logic is formulated based on the demand torque along with the engine torque characteristics and the state of charge (SOC) of power battery. And a quantification model for driver’s intention is proposed using a fuzzy inference approach, which can compute the driver's dynamic and economic performance expectations using the driver's operation characteristics and vehicle status as input. With the help of a linear weighting method using the performance expectations as weights, a comprehensive performance evaluation function is constructed as the optimization objective of shift

Yin, Xiaofeng, Li, Hong, Zhang, Jinhong, Lei, Yulong

The Impact of GCI Engines on Long-haul Trucks in the US and China

2025-01-8396

04/01/2025

This study evaluates the impacts of the gasoline compression ignition (GCI) engine on heavy duty long-haul trucks in both the Chinese and US markets. The study examines various aspects such as vehicle performance requirements, fuel consumption, emissions, and ownerships costs, and how they influence the implementation and impact of new technologies in these markets. By considering a wide variety of drive cycles, including standard regulatory cycles and real-world cycles, the study aims to identify the impact of varying degrees of powertrain electrification using diesel and GCI engines on fuel consumption and emissions. Additionally, this paper explores the viability of powertrain electrification in long-haul trucks by analyzing factors such as levelized cost of driving (LCOD), manufacturing costs, and energy costs. These considerations play a crucial role in determining the economic feasibility and attractiveness of electrification technologies in various driving scenarios and market

Nieto Prada, Daniela, Vijayagopal, Ram, Yan, Ziming, Sari, Rafael, He, Xin

Path Tracking of Autonomous Vehicles with Model Predictive Control (MPC) Optimized with Particle Swarm Optimization (PSO)

2025-01-8036

04/01/2025

This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of

Eldesouky, Dina M.Mustafa, Abdelaziz, Taha Helmy, Mohamed, Amr.E

Aerodynamic Simulation of a Parametrically Deformed Rotating Tire

2025-01-8765

04/01/2025

As the automotive industry increasingly shifts toward electrification, reducing vehicle drag becomes crucial for enhancing battery range and meeting consumer expectations. Additionally, recent regulations such as WLTP can require car manufacturers to provide reliable drag data for vehicles as they are configured, as is the case in Europe. Vehicle and tire manufacturers can assess tire impacts on vehicle performance through testing. However, to improve designs, it is essential to identify which tire features influence the flow field and overall vehicle performance. Physical tests measure tire behavior under load, but isolating contact patch and tire bulge effects is difficult, as both change together. Simulation allows independent analysis of these factors—something that physical testing alone cannot achieve. This paper investigates the aerodynamic impact of realistic tire deformation parameters—specifically, bulge and contact patch deformations—using PowerFLOW® from Dassault Systèmes

Martinez Navarro, Alejandro, Parenti, Guido, Shock, Richard

Mechanical Property Analysis of Triply Periodic Minimal Surface Structure with a Novel Hybrid Structure

2025-01-8215

04/01/2025

Triply periodic minimal surface (TPMS) structure, demonstrates significant advantages in vehicle design due to its excellent lightweight characteristics and mechanical properties. To enhance the mechanical properties of TPMS structures, this study proposes a novel hybrid TPMS structure by combining Primitive and Gyroid structures using level set equations. Following this, samples were fabricated using selective laser sintering (SLS). Finite element models for compression simulation were constructed by employing different meshing strategies to compare the accuracy and simulation efficiency. Subsequently, the mechanical properties of different configurations were comprehensively investigated through uniaxial compression testing and finite element analysis (FEA). The findings indicate a good agreement between the experimental and simulation results, demonstrating the validity and accuracy of the simulation model. For TPMS structures with a relative density of 30%, meshing with S3R

Tang, Haiyuan, Xu, Dexing, Sun, Xiaowang, Wang, Xianhui, Wang, Liangmo, Wang, Tao

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

Revolutionizing Commercial Vehicles: Integrating Software-Defined Vehicle Architecture for Enhanced Electrical and Electronic Systems

2025-01-8138

04/01/2025

Commercial Vehicle (CV) market is growing rapidly with the advancement of Software-Defined Vehicles (SDVs), which provide greater level of flexibility, efficiency and integration of AI & cutting-edge technology. This research provides an in-depth analysis of E&E architecture of CVs, focusing on the integration of SDV-based technology, which represents the transition from hardware-focused to a more dynamic, software-focused methodology. The research begins with the fundamental concepts of E&E architecture in CVs, including virtualization, centralized computing, feature based ECU, CAN and modular frameworks which are then upgraded to meet various operational and customer requirements. The capacity of SDV-based architecture designs to scale to handle heavy duty commercial vehicles is a primary focus, with an emphasis on ensuring the safety and security, to defend against potential vulnerabilities. Furthermore, the integration of real-time data processing capabilities and advanced E&E

Saini, Vaibhav, Jain, Ayushi, Meduri, Pramoda, Solutions GmbH, Verolt Technology

Impact of Hybrid Electric Vehicles on Energy Consumption and Emission Reduction for Agricultural Applications

2025-01-8540

04/01/2025

As the agricultural industry seeks to enhance sustainability and reduce operational costs, the introduction of mild hybrid technology in tractors presents a promising solution. This paper focuses on downsizing internal combustion (IC) engine, coupled with integration of electric motor, to reduce fuel consumption and meet stringent emission regulations while maintaining power requirement for agricultural applications in India. The hybridization aims to deliver instant power boosts during peak loads and capitalizes on energy recovery during part loads and braking. Furthermore, the idle avoidance feature minimizes fuel consumption during periods of inactivity thus improving fuel efficiency. The hybridization also aims to hybridize auxiliary systems for flexible power management, enabling operation of either engine, auxiliaries, or both as needed. A newly developed hybrid supervisory control prototype efficiently manages electric power and mechanical power, enabling intelligent management

Prasad, Lakshmi P., PS, Satyanarayana, Paygude, Tejas, Gangsar, Purushottam, Thakre, Mangesh, Choudhary, Nagesh, Gitapathi, Ajinkya

A Study of Tire Handling and Ride Performance Map According to Tire Tread Wear State

2025-01-8760

04/01/2025

The electric vehicle market, vehicle ECU computing power, and connected electronic vehicle control systems continue to grow in the automotive industry. The results of these advanced and expanded vehicle technologies will provide customers with increased cost savings, safety, and ride quality benefits. One of these beneficial technologies is the tire wearing prediction. The improved prediction of tire wear will advise a customer the best time to change tires. It is expected that this prediction algorithms will be essential part for both the optimization of the chassis control systems and ADAS systems to respond to changed tire performance that varies with a tire’s wear condition. This trend is growing, with many automakers interested in developing advanced technologies to improve product quality and safety. This study is aimed at analyzing the handling and ride comfort characteristics of the tire according to the depth of tire pattern wear change. The handing and ride comfort

Kim, Changsu, Kwon, Seungmin, Sung, Dae-Un, Ryu, Yonghyun, Ko, Younghee

Performance Evaluation and Thermal Analysis of Various Electric Drives for Automotive Electric Propulsion

14-14-02-0010

03/26/2025

Electric vehicles (EVs) represent a significant stride toward environmental sustainability, offering a multitude of benefits such as the reduction of greenhouse gas emissions and air pollution. Moreover, EVs play a pivotal role in enhancing energy efficiency and mitigating reliance on fossil fuels, which has propelled their global sales to unprecedented heights over the past decade. Therefore, choosing the right electric drive becomes crucially important. The main objective of this article is to compare various conventional and non-conventional electric drives for electric propulsion in terms of electromechanical energy conversion ratio and the thermal response under continuous [at 12 A/mm2 and 6000 rpm] and peak [at 25 A/mm2 and 4000 rpm] operating conditions. The comparative analysis encompasses torque density, power density, torque pulsation, weight, peak and running efficiencies of motor, inverter and traction drive, electromechanical efficiency, and active material cost. This

Patel, Dhruvi Dhairya, Fahimi, Babak, Balsara, Poras T.

Advancing Oil Maintenance Practices: Insights from Viscosity Analysis

2025-01-5019

03/21/2025

There is a lack of data to support the efficacy of traditional mileage and time-based criteria for oil changes in vehicles. In this study, used-oil samples from 63 vehicles were collected and analyzed. Besides dynamic viscosity, viscosity index and activation energy were evaluated as measures of thermal stability of viscosity. The results revealed that mileage and time of use are not significantly correlated with (p > 0.05) and are thus poor indicators of oil viscosity and viscosity thermal stability measures. These findings highlight the limitations of current criteria and underscore the need for new sensing and evaluation methods to reduce costs, waste, and environmental impact while ensuring vehicle performance.

Salvi, Nilesh, Tan, Jinglu

Experimental Methodology for Tire Ride Analysis Based on Outdoor Cleat Testing

10-09-03-0023

03/12/2025

Handling and ride comfort optimization are key vehicle design challenges. To analyze vehicle performance and investigate the dynamics of the vehicle and its subcomponents, we rely heavily on robust experimental data. The current article proposes an outdoor cleat test methodology to characterize tire dynamics. Compared to indoor procedures, it provides an effective tire operating environment, including the suspensions and the vehicle chassis motion influence. In addition, it overcomes the main limitation of existing outdoor procedures, the need for dedicated cleat test tracks, by using a set of removable cleats of different sizes. A passenger vehicle was equipped with sensors including an inertial measurement unit, a noncontact vehicle speed sensor, and a wheel force transducer, providing a setup suitable to perform both a handling test routine and the designed cleat procedure, aimed at ride testing and analysis. Thus, the outdoor cleat test data were compared with indoor test

Gravante, Gerardo, Napolitano Dell’Annunziata, Guido, Barbaro, Mario, Farroni, Flavio

Driver Cognitive Distraction Recognition Based on Multi-Source Data from Simulated Driving Experiments

2025-01-7140

02/21/2025

Nowadays, cognitive distraction in the process of driving has become a frequent phenomenon, which has led to a certain proportion of traffic accidents, causing a lot of property losses and casualties. Since the fact that cognitive distraction is mostly reflected in the driver's reception and thinking of information unrelated to driving, it is difficult to recognize it from the driver's facial features. As a result, the accuracy of prediction is usually lower relying solely on facial performance to detect cognitive distraction. In this research, fifty participants took part in our simulated driving experiment. And each participant conducted the experiment in four different traffic scenarios using a high-fidelity driving simulator, including three cognitive distraction scenarios and one normal driving scenarios. Firstly, we identified the facial performance indicators and vehicle performance indicators that had a significant effect on cognitive distraction through one-way ANOVA. Then we

Qu, Chixiong, Bao, Qiong, Qu, Qikai, Shen, Yongjun

Deep Learning-Enhanced Direct Torque Control of BLDC Motors for Optimized Electric Vehicle Performance

12-08-04-0036

02/12/2025

Electric vehicles (EVs) represent a promising solution to reduce environmental issues and decrease dependency on fossil fuels. The main drawback associated with the direct torque control (DTC) scheme is that it is incapable of improving the efficiency and response time of the EVs. To overcome this problem, integrating deep learning (DL) techniques into DTC offers a valuable solution to enhance the performance of the drive system of EVs. This article introduces three control methods to improve the output for DTC-based BLDC motor drives: a traditional proportional–integral for speed controller (speed PI), a neural network fitting (NNF)-based speed controller (speed NNF), and a custom neural (CN) network-based speed controller (speed CN). The NNF and CN are DL techniques designed to overcome the limitations of conventional PI controllers, such as retaining the percentage overshoot, settling times, and improving the system’s efficiency. The CN controller reduced the torque ripple by 15

Patel, Sandesh, Yadav, Shekhar, Tiwari, Nitesh

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