To select appropriate lightweight materials and optimize their integration with battery enclosure components for enhanced performance and weight reduction, this study proposes a material selection strategy driven by mechanical property indices combined with the CRITIC-weighted TOPSIS method. Initially, a decision matrix incorporating bending stiffness indices was established based on the deformation characteristics of battery enclosures, focusing on commonly used metallic materials. The CRITIC-weighted TOPSIS method was employed to standardize data dimensions, determine objective weight coefficients, and calculate relative closeness coefficients for candidate material screening. Subsequently, sensitivity analysis identified critical components significantly influencing operational conditions, followed by integrated material and dimensional optimization to determine the optimal solution. The optimized battery enclosure achieved a weight reduction of 15.56 kg, with a reduction rate of

Liu, Junfeng, Kang, Yuanchun

Road Noise Improvement Method for Battery Electric Vehicles Based on Bushing Stiffness Optimization

10-09-04-0032

06/23/2025

The chassis bushing is one of the key components affecting the vibration isolation efficiency of a vehicle, and a comprehensive optimization method combining the experimental process and transmission path analysis (TPA) is proposed to reduce the low- and medium-frequency road noise response in the passenger compartment of a battery electric vehicle (BEV). First, the noise signals were obtained in the vehicle road noise test under two working conditions of 40 and 60 km/h at uniform speeds on rough road surfaces. Then, the excitation transmission path was analyzed based on the structural noise transmission model, and the chassis bushing parts with more considerable vibration isolation contribution were screened out. By matching the stiffness values of the chassis bushings in the optimization problem through experimental methods, the optimization scheme reduces the stiffness of the front swing arm bushing and the rear longitudinal arm bushing by 30%. Additionally, a flexible connection is

Liu, Ke, Liao, Yinghua, Wang, Hongrui, Zhou, Junchao

Learning-Based Predictive Controller for Connected and Automated Hybrid Electric Vehicles

14-14-02-0015

06/19/2025

This article aims at presenting a learning-based predictive control strategy for hybrid electric vehicles (HEVs) in the presence of uncertainty, where the controller structure and energy efficiency of the HEV is simultaneously optimized. The proposed approach includes development of a Bayesian optimization (BO)–based control structure optimization method, followed by an eco-driving–based hierarchical robust energy management strategy (EMS) development for connected and automated HEVs. To apply the learning-based strategy online, we also introduce an approach with approximate cost function for the BO to reduce training and computation time and improve energy in a given trip. The control structure is described by a parameter vector, which is updated, using BO, in an episodic fashion with the performance of the EMS and the computation time. With the current control structure, the hierarchical EMS includes a high-level powertrain energy manager that takes long-term decisions, and a low

HomChaudhuri, Baisravan, Iranzo Juan, Ignacio

Numerical Simulation and Parametric Optimization of Engine/Powertrain Mounts: Identifying Key Design Variables to Reduce NVH Noise

2025-01-0251

06/16/2025

Engine and powertrain mounts are vital for isolating vibrations and reducing the transmission of Noise, Vibration, and Harshness (NVH) from the engine to the vehicle structure. Despite technological advancements, addressing NVH issues related to tribological factors continues to pose significant challenges in automotive engineering. This study aims to systematically identify and optimize design parameters of engine/powertrain mounts to minimize NVH levels using CAE tools and parametric optimization techniques in Abaqus and Isight, respectively. The purpose of this research is to investigate the correlation between various design parameters of powertrain mounts and their impact on NVH characteristics. Specific attention is focused on noises such as clunking, banging, or thumping that emerge from the engine bay under dynamic conditions like acceleration, braking, or turning. These sounds often occur as the engine moves excessively due to worn mounts, making unintended contact with other

Ganesan, Karthikeyan, Seok, Sang Ho

Sound Granular Synthesis Method for Sc-Fi Sound Quality of Electric Vehicles

2025-01-0253

06/16/2025

Since the powertrain systems of electric vehicles (EVs) lack the traditional engine sound, their NVH performance differs from that of conventional fuel-powered vehicles, making the use of active sound design (ASD) systems increasingly common to provide compensatory sound. With the increasing demand for ASD systems, sci-fi sounds are emerging as a design direction to enhance the acoustic feedback of powertrain systems and to elevate the futuristic and immersive driving experience of vehicles. A method for generating sci-fi soundscapes using a granular synthesis algorithm is proposed in this paper. First, based on the designed sci-fi target sound characteristics, a sound grain generation method using the adaptive principal frequency technique is proposed, and the overlap-and-add (OLA) method is employed to synthesize the sound grains. Then, to enhance the sound continuity and smoothness during the sound synthesis process, a method for optimizing a composite cosine window function using a

Liu, Dezhu, Liu, Zhien, Xie, Liping, Lu, Chihua

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

A Microgrid Vehicle-to-Grid Energy Scheduling Optimization Algorithm Based on Non-Dominated Sorting Genetic Algorithm II

2025-01-5035

05/30/2025

With the rapid expansion of the electric vehicle (EV) market, the frequency of grid-connected charging has concentrated primarily during peak hours, notably from 7:00 a.m. to 10:00 a.m. and 6:00 p.m. to 10:00 p.m., resulting in substantial demand surges during both morning and evening periods. Such uncoordinated charging patterns pose potential challenges to the stability and economic efficiency of power systems. As vehicle-to-grid (V2G) technology advances, facilitating bidirectional energy exchange between EVs and smart grids, the need for optimized control of EV charging and discharging behaviors has become critical to achieving effective peak shaving and valley filling in the grid. This paper proposes a microgrid energy scheduling optimization algorithm based on existing smart grid and EV charging control technologies. The method establishes a multi-objective optimization model with EVs’ 24-h charging and discharging power as decision variables and microgrid load rate, load

Fan, Longyu, Chen, Yuxin, Zhang, Dacai

Improving the Working Performance of Sliding Friction Pairs Using Optimal Asymmetric Textures

04-18-03-0016

05/29/2025

Optimizing the parameters of asymmetric textures (AT) designed on the surface of sliding frictional pairs (SFP) can make each texture more reasonably distributed. Thereby, the oil film thickness can be more stable; and the lubrication and load ability of SFP can be improved. To clarify this issue, based on the SFP’s lubricating model added by AT using the rectangular structure, parameters of AT including the angle between the horizontal axe and bottom surface (φij), the angle between the lateral axe and bottom surface (γij), and texture’s depth (hij) are optimized. The study results show that the parameters of φij, γij, and hij of AT optimized can create the p (hydrodynamic pressure of liquid) better than the symmetric textures. Significantly, the pmax and load ability of the liquid in the SFP using optimal AT have been greatly increased compared to the liquid in the SFP using the symmetric textures. Accordingly, the results are an important reference for the design and distribution of

Wang, Cuifang, Zhang, Lu

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

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR2025008

05/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Ercisli, Safak

Optimization of Stainless Steel Casting Process Based on Numerical Simulation

05-19-01-0006

05/10/2025

Due to the increasing precision requirements for stainless steel castings in the current industrial field, we take stainless steel as the object, use numerical simulation to analyze the manufacturing process of castings, and explore the mechanism of related defects and preventive measures. The results indicate that in the process optimization of small castings, the maximum shrinkage and porosity of the conventional scheme, the optimization scheme with the addition of cold iron and insulation riser, and the optimization scheme with the improved pouring system combined with the optimal parameters are 1.83%, 1.64%, and 1.42%, respectively. The optimal pouring temperature, pouring speed, and shell preheating temperature of medium- and large-sized castings are: 1620°C, 1.5 kg/s, and 1100°C, respectively. According to the aforementioned findings, the study raises the standard of precision production for stainless steel, and fuel the growth of the precision casting sector.

Huang, Jie, Zhang, Hongshan

Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment

12-09-01-0004

05/06/2025

The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency

Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng

Simulation and Experimental: Enhanced Stability Control of Electric Vehicle Based on Phase Plane Boundary Analysis

10-09-02-0017

05/05/2025

To optimize vehicle chassis handling stability and ride safety, a layered joint control algorithm based on phase plane stability domain is proposed to promote chassis performance under complicated driving conditions. First, combining two degrees-of-freedom vehicle dynamics model considering tire nonlinearity with phase plane theory, a yaw rate and side slip angle phase plane stability domain boundary is drew in real time. Then based on the real-time stability domain and hierarchical control theory, an integrated control system with active front steering (AFS) and direct yaw moment control (DYC) is designed, and the stability of the controller is validated by Lyapunov theory. Finally, the lateral stability of the vehicle is validated by Simulink and CarSim simulations, real car data, and driving simulators under moose test and pylon course slalom test. The experimental results confirm that the algorithm can enhance the maneuverability and ride safety for intelligent vehicles.

Liao, Yinsheng, Zhang, Zhijie, Su, Ailin, Zhao, Binggen, Wang, Zhenfeng

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

Hybrid 3D Sound Intensity Measurements and Simulations Applied to a Truck Rear Axle

2025-01-0105

05/05/2025

This study presents a novel methodology for optimizing the acoustic performance of rotating machinery by combining scattered 3D sound intensity data with numerical simulations. The method is demonstrated on the rear axle of a truck. Using Scan&Paint 3D, sound intensity data is rapidly acquired over a large spatial area with the assistance of a 3D sound intensity probe and infrared stereo camera. The experimental data is then integrated into far-field radiation simulations, enabling detailed analysis of the acoustic behavior and accurate predictions of far-field sound radiation. This hybrid approach offers a significant advantage for assessing complex acoustic sources, allowing for quick and reliable evaluation of noise mitigation solutions.

Fernandez Comesana, Daniel, Vael, Georges, Robin, Xavier, Orselli, Joseph, Schmal, Jared

Application of Hybrid SEA in Vehicle NV Package Optimization

2025-01-0050

05/05/2025

As India’s economy expands and road infrastructure improves, the number of car owners is expected to grow substantially in the coming years. This market potential has intensified competition among original equipment manufacturers (OEMs) to position their products with a focus on cost efficiency while delivering a premium user experience. Noise and Vibration (NV) performance is a critical differentiator in conveying a vehicle's premiumness, and as such, NV engineers must strategically balance the achievement of optimal acoustic performance with constraints on cost, mass, and development timelines. Traditionally, NV package optimization occurs at the prototype or advanced prototype stage, relying heavily on physical testing, which increases both cost and time to market. Furthermore, late-stage design changes amplify these challenges. To address these issues, this paper proposes the integration of Hybrid Statistical Energy Analysis (HSEA) into the early stages of vehicle development

Rai, Nitesh, Mehta, Makrand, Ravindran, Mugundaram

Numerical Analysis of Variable Cross Section Weather-Strip Contact Sealing Performance & Stick Slip Behavioral Study

2025-01-0135

05/05/2025

This study focuses on the numerical analysis of weather-strip contact sealing performance with a variable cross-sectional design, addressing both static and dynamic behaviors, including the critical issue of stick-slip phenomena. By employing finite element modeling (FEM), the research simulates contact pressures and deformations under varying compression loads, DCE (Door Closing Efforts) requirements, typical in automotive applications. The analysis evaluates how changes in the cross-sectional shape of the weather-strip affect its ability to maintain a consistent sealing performance, especially under dynamic vehicle operations. The study also delves into stick-slip behavior, a known cause of noise and vibration issues, particularly improper/ loosened door-seal contact during dynamic driving condition. This study identifies key parameters influencing stick-slip events, such as friction coefficients, material stiffness, surface interactions, sliding velocity, wet/dry condition

Ganesan, Karthikeyan, Seok, Sang Ho, Sun, Hyang Sun

Navigating Evolving Vehicle NVH Challenges with Application of Emerging Technologies in Artificial Intelligence and Machine Learning

2025-01-0130

05/05/2025

The multifaceted, fast-paced evolution in the automotive industry includes noise and vibration (NVH) behavior of products for regulatory requirements and ever-increasing customer preferences and expectations for comfort. There is pressing need for automotive engineers to explore new and advanced technologies to achieve a ‘First Time Right’ product development approach for NVH design and deliver high-quality products in shorter timeframes. Artificial Intelligence (AI) and Machine Learning (ML) are trending transformative technologies reshaping numerous industries. AI enables machines to replicate human cognitive functions, such as reasoning and decision-making, while ML, a branch of AI, employs algorithms that allow systems to learn and improve from data over time. The purpose of the paper is to show an approach of using machine learning techniques to analyze the impact of variations in structural design parameters on vehicle NVH responses. The study begins by executing the Design of

Miskin, Atul R., Parmar, Azan, Raj, Sonia, Himakuntla, Uma Maheswar

Designing Rubber Flaps for Resonance Management: High-Frequency Tuning in Electric Vehicles Using AI/ML Approaches

2025-01-0125

05/05/2025

High-frequency whine noise in electric vehicles (EVs) is a significant issue that impacts customer perception and alters their overall view of the vehicle. This undesirable acoustic environment arises from the interaction between motor polar resonance and the resonance of the engine mount rubber. To address this challenge, the proposal introduces an innovative approach to predicting and tuning the frequency response by precisely adjusting the shape of rubber flaps, specifically their length and width. The approach includes the cumulation of two solutions: a precise adjustment of rubber flap dimensions and the integration of ML. The ML model is trained on historical data, derived from a mixture of physical testing conducted over the years and CAE simulations, to predict the effects of different flap dimensions on frequency response, providing a data-driven basis for optimization. This predictive capability is further enhanced by a Python program that automates the optimization of flap

Hazra, Sandip, Khan, Arkadip

Data-Driven Machine Learning Approach for Rattle Risk Mitigation

2025-01-0126

05/05/2025

In the modern automotive industry, squeak and rattle issues are critical factors affecting vehicle perceived quality and customer satisfaction. Traditional approaches to predicting and mitigating these problems heavily rely on physical testing and simulation technologies, which can be time-consuming and resource-intensive, especially for larger models. In this study, a data-driven machine learning approach was proposed to mitigate rattle risks more efficiently. This study evaluated a floor console model using the traditional simulation-based E-line method to pinpoint high-risk areas. Data generation is performed by varying material properties, thickness, and flexible connection stiffness using the Hammersley sampling algorithm, creating a diverse and comprehensive dataset for generating a machine learning (ML) model. Utilizing the dataset, the top contributing variables were identified for training the ML models. Various machine-learning models were developed and evaluated, and the

Parmar, Azan, Rao, Sohan, Reddy, Hari Krishna

Asymmetric Gear Blank Optimization to Reduce Gear Noise for Electric Drive Units

2025-01-0079

05/05/2025

Gear whine has emerged as a significant challenge for electric vehicles (EVs) in the absence of engine masking noise. The demand from customers for premium EVs with high speed and high torque density introduces additional NVH risks. Conventional gear design strategies to reduce the pitch-line velocity and increase contact ratio may impact EV torque capacitor or its efficiency. Furthermore, microgeometry optimization has limited design space to reduce gear noise over a wide range of torque loads. This paper presents a comprehensive investigation into the optimization of transfer gear blanks in a single-speed two-stage FDW electric drive unit (EDU) with the objective of reducing both mass and noise. A detailed multi-body dynamics (MBD) model is constructed for the entire EDU system using a finite-element-based time-domain solver. This investigation focuses on the analysis and optimization of asymmetric gear blank design features with three-slot patterns. A design-of-experiment (DOE

He, Song, Bahk, Cheonjae, Li, Bo, Du, Isaac, Patruni, Pavan Kumar, Baladhandapani, Dhanasekar

Modeling Snap-Fit Stiffness for Improved BSR Predictions in Automotive Design

2025-01-0089

05/05/2025

Squeak and Rattle (S&R) issues present significant challenges in the automotive industry, negatively affecting the perceived quality of vehicles. Early identification of these issues through rigorous testing protocols—such as auditory assessments and dynamic simulations—enables the development of more robust systems while optimizing resource use. Finite Element Method (FEM) simulations are crucial for identifying S&R issues during the design phase, allowing engineers to address potential problems before the creation of physical prototypes. By developing high-fidelity virtual models and accurately simulating flexible connections, these simulations effectively capture rattle effects, enhancing prediction reliability. Traditional snap stiffness calculations typically employ a cantilever-based formulation, which is suitable for simple snap-fit designs but insufficient for more complex geometries that require enhanced stiffness. To address this limitation, the proposed methodology utilizes

Rao, Sohan, Elangovan, Pranesh, Reddy, Hari

Investigation and Application of Tire Model to Optimize Road Noise

2025-01-0111

05/05/2025

Tires have a significant impact on vehicle road noise. The noise in 80~160Hz is easily felt when driving on rough roads and has a great relationship with the tire structural design. How to improve the problem through tire simulation has become an important issue. Therefore, this paper puts forward the concept of virtual tire tuning to optimize the noise. An appropriate tire model is crucial for road noise performance, and the CDtire (Comfort and Durability Tire) model was used in the article. After conducting experimental validation to get an accurate tire model, adjust the parameters and structure of the tire model to generate alternative model scenarios. The transfer function of the tire center was analyzed and set as the evaluation condition for tire NVH (Noise, vibration, and harshness) performance. This enabled a comparison among various model scenarios to identify the best-performing tire scenario in focused frequency whose transfer function needed to be lowest. Manufacture the

Zhang, Ben, Yu Sr, Jing, Chen, Qimiao, Liu, Xianchen, Gu, Perry

Virtual Gear Noise Analysis and Case Design Investigation for Concept Parallel Axis Drive Unit

2025-01-0102

05/05/2025

For electric vehicles, it is critical to develop drive units that produce a minimal amount of noise while meeting efficiency needs for a given application. Modern computational resources and accumulated experience allow for engineers to evaluate gear noise early in the development process and influence the design of the drive unit. This paper documents a high-fidelity virtual engineering approach to evaluate gear noise in a concept parallel axis drive unit and provide learnings to influence the design of external structures to improve NVH performance. By using the latest simulation tools to calculate and visualize the noise and vibration characteristics of the drive unit, designers and developers can implement design changes in optimization iterations to reduce noise and vibration. Gear harmonic response is firstly analyzed through a system model which considers structural deflection and misalignment, then a FE housing model is incorporated which is used for noise radiation evaluation

Lima, Luiz, Shi, Zhenghong, Xu, Hai, Reynolds, Craig, Miller, John

Experimental Investigation on Genetic Algorithm Optimized Proportional Integral Derivative Based Active Suspension System

2025-01-0014

05/05/2025

In this work, Genetic Algorithm (GA) optimized Proportional Integral Derivative (PID) controller is employed in the active suspension. The PID gain values are optimally tuned based on the objective function by the Integral Time Absolute Error (ITAE) criteria of various suspension measures like vehicle body displacement, suspension and tire deflections. The proposed GAPID controller is experimentally validated through the 3-DOF quarter-car (QC) test rig model. The fabricated model with passive suspension system (PASS) and active suspension system (ACSS) with an electrical actuator is presented. The schematic representation of the fabricated test set-up with and without ACSS is also illustrated. Further, simulation and experimental response of the fabricated model with and without ACSS are compared. It is identified that the proposed GAPID controller attenuates the sprung mass acceleration by about 41.64 % and 29.13 % compared with PASS for the theoretical as well as experimental cases

A, Arivazhagan, Kandavel, Arunachalam

Active Noise Control with Head Tracking System

2025-01-0012

05/05/2025

In active noise control, the control region size (same meaning as zone of control) decreases as the frequency increases, so that even a small moving of the passenger's head causes the ear position to go out of the control region. To increase the size of the control region, many speakers and microphones are generally required, but it is difficult to apply it in a vehicle cabin due to space and cost constraints. In this study, we propose moving zone of quiet active noise control technique. A 2D image-based head tracking system captured by a camera to generate the passenger's 0head coordinates in real time with deep learning algorithm. In the controller, the control position is moved to the ear position using a multi-point virtual microphone algorithm according to the generated ear position. After that, the multi-point adaptive filter training system applies the optimal control filter to the current position and maintains the control performance. Through this study, it is possible to

Oh, ChiSung, Kang, Jonggyu, Kim, Joong-Kwan

Directivity Driven Simulation Methodology for Efficient Acoustic Encapsulation

2025-01-0048

05/05/2025

Noise reduction at the source level is key to achieve the overall vehicle level interior targets. This paper presents a novel approach that integrates directivity analysis with simulation techniques to optimize acoustic encapsulation design for automotive sound sources to achieve the targeted radiation levels. The foundation for this methodology is to measure the angular distribution of sound pressure levels around the noise source so called Directivity, at every frequency of interest and determine the most effective acoustic encapsulation to achieve the targeted sound radiation. Accurate measurement of directivity in physical testing with fine angular resolutions can be complex and expensive, this study utilizes numerical simulation techniques using FEA to mitigate the challenges in mid frequency range. The scope of the study is focused on mid frequency sound pressure levels between 500-2500 Hz, which are determined to be significant contributors to overall DU noise. The first step is

Kaluvakota, Srikanth, Ghaisas, Nikhil, Pilz, Fernando

Hybrid RLDA Methodology for Passenger Car Engine Mount Applications

2025-01-0095

05/05/2025

In the development of engine mounting systems for passenger cars, accurately capturing dynamic loads during real-world driving conditions is crucial for optimizing performance, durability, and NVH (Noise, Vibration, and Harshness) characteristics. This paper introduces an innovative approach that integrates load cell and strain gauge technologies for Road Load Data (RLD) acquisition, specifically designed for engine mounting applications. By combining load cells and strain gauges, this method offers a comprehensive solution for measuring both direct forces and the resulting strains on engine mounts, providing a more detailed understanding of the load profiles. Load cells capture the overall forces exerted on the engine mounts, while strategically placed strain gauges measure local deformations and stress distributions within the mounts. This dual-method approach enables precise correlation of force and strain data, enhancing the accuracy of load calculations under various driving

Hazra, Sandip, Khan, Arkadip Amitava, Mohare, Gourishkumar

The DANA EV NVH Laboratory

2025-01-0007

05/05/2025

A cutting-edge EV powertrain NVH laboratory has been established at Dana Incorporated’s world headquarters in Ohio, significantly enhancing its capabilities in EV powertrain NVH development. This state-of-the-art, industry-leading facility is specifically designed to address diverse NVH requirements for EV powertrain development and validation processes. This capability substantially reduces development time for new drivetrain systems. Key features of the laboratory include a hemi-anechoic chamber, two AC asynchronous load motors, an acoustically isolated high-speed input motor, and two battery emulators capable of accommodating both low and high-voltage requirements. The NVH laboratory enables engineers to evaluate system performance and correlate results with digital twin models. This capability supports the optimization of NVH characteristics at both the system and component levels, as well as the refinement of CAE models for enhanced design precision. This paper details the design

Cheng, Ming-Te, Zugo, Chris

Image Processing-Based Chip Detection by KIZKI Algorithm and Its Optimization

2025-01-0168

05/02/2025

The process of producing aircraft parts involves the drilling of aluminum alloys. This creates a large amount of chips, which are removed using air, but sometimes they still remain within the holes. This is checked by inspectors through visual inspection. However, the quality of human inspection varies based on skill level and fatigue. Thus, image-based inspection should be used to stabilize and further improve inspection quality. This study aims to build a framework for chip detection based on image processing. Taking into account on-site implementation, the system must have low installation and running costs and be standalone. Therefore, we adopt the KIZKI algorithm, which satisfies these conditions. KIZKI means awareness in Japanese. This is a model of human peripheral vision and saccades. It does not require training like AI and can achieve high-speed and high-performance detection using a low-performance computer. In other words, there is no need for a computer with an expensive

Iinuma, Marin, Sato, Junya, Tsuji, Masahiko

Optimization of Gusset Geometry for Racecar Frame Design

2025-01-5033

05/01/2025

This research optimizes sheet metal gusset geometry to support a suspension pickup point in a Formula SAE racecar. The sheet metal gusset design incorporates an external radial cut-out and an internal triangular cut-out, each of which can be adjusted in size to optimize the stiffness to mass ratio. A finite element analysis was set up using a heavy braking load case, which applied 3900 N to the suspension point being supported by the gusset. A parametric optimization (finite element analysis) was run in SolidWorks to gather mass and stiffness data for each of the 143 designs under the prescribed load case. The parametric optimization was run on both a simulated front hoop and a test fixture, which showed a similar trend in their results. Experimental testing was performed on three designs. The gusset profiles were waterjet and TIG welded to the test fixture tube frames. The results of the test agreed with the simulation results with a discrepancy of less than 10% in all cases. The

Burggraf, Jacob, Willerth, Stephanie Michelle, Yu, Bosco

A Novel Model for Short-Term Traffic Flow Prediction on Highways Utilizing Multi-Head Attention–BiLSTM–BiGRU

12-09-01-0002

04/24/2025

With the development of intelligent transportation systems and the increasing demand for transportation, traffic congestion on highways has become more prominent. So accurate short-term traffic flow prediction on these highways is exceedingly crucial. However, because of the complexity, nonlinearity, and randomness of highway traffic flows, short-term prediction of its flows can be difficult to achieve the desired accuracy and robustness. This article presents a novel architectural model that harmoniously fuses bidirectional long–short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and multi-head attention (MHA) components. Bayesian optimization (BO) is also used to determine the optimal set of hyperparameters. Based on the PeMS04 dataset from California, USA, we evaluated the performance of the proposed model across various prediction intervals and found that it performs best within a 5-min prediction interval. In addition, we have conducted comparison and ablation

Chen, Peng, Wang, Tao, Ma, Changxi, Chen, Jun

Advancements in Lightning Strike Damage Modeling: Finite Element Model to Predict Thermal Damage Induced on Composites

01-18-02-0007

04/21/2025

Modern aircraft, ships, and offshore structures are increasingly constructed using fiber-reinforced composite materials. However, when subjected to lightning strikes, these materials can suffer significant structural and functional damage due to their electrical and thermal properties. This study aims to develop a novel finite element (FE) model to minimize the error in estimating the thermal damage caused during lightning strikes. This will aid in design and optimization of lightning protection systems. The developed model introduces a simplified numerical approach to model the lightning arc interaction with CFRP laminate. The existing FE model includes idealized loading conditions, leading to high error in estimation of severe damage area and in-depth damage. The proposed methodology incorporates a more realistic lightning-induced loading pattern to improve accuracy. Several cases are analyzed using available FE methods and compared to the proposed model (case 6) to evaluate the

Sontakkey, Akshay, Kotambkar, Mangesh, Kaware, Kiran

Fatigue Analysis of Motorcycle Rear Swing Arm on Different Road Surfaces

2024-32-0046

04/18/2025

The rear swing arm, a crucial motorcycle component, connects the frame and wheel, absorbing the vehicle’s load and various road impacts. Over time, these forces can damage the swing arm, highlighting the need for robust design to ensure safety. Identifying potential vulnerabilities through simulation reduces the risk of failure during the design phase. This study performs a detailed fatigue analysis of the swing arm across different road conditions. Data for this research were collected from real-vehicle experiments and simulation analyses, ensuring accuracy by comparing against actual performance. Following CNS 15819-5 standards, road surfaces such as poorly maintained, bumpy, and uneven roads were tested. Using Motion View, a comprehensive multi-body dynamic model was created for thorough fatigue analysis. The results identified the most stress-prone areas on the swing arm, with maximum stress recorded at 109.6N on poorly maintained roads, 218.3N on bumpy surfaces, and 104.8N on

Chiou, Yi-Hau, Hwang, Hsiu-Ying, Huang, Liang-Yu

Local and Global Entropy Generation of Topographically Optimized Porous Reactors in Reaction-Diffusion Systems Considering Coupling Effects between Heat and Mass Transfer

2024-32-0069

04/18/2025

As the automotive sector shifts towards cleaner and more sustainable technologies, fuel cells and batteries have emerged as promising technologies with revolutionary potential. Hydrogen fuel cell vehicles offer faster refueling times, extended driving ranges, and reduced weight and space requirements compared to battery electric vehicles, making them highly appealing for future transportation applications. Despite these advantages, optimizing electrode structures and balancing various transport mechanisms are crucial for improving PEFCs’ performance for widespread commercial viability. Previous research has utilized topology optimization (TO) to identify optimal electrode structures and attempted to establish a connection between entropy generation and topographically optimized structures, aiming to strengthen TO numerical findings with a robust theoretical basis. However, existing studies have often neglected the coupling of transport phenomena. Typically, it is assumed that a single

Tep, Rotanak Visal Sok, Long, Mengly, Alizadeh, Mehrzad, Charoen-amornkitt, Patcharawat, Suzuki, Takahiro, Tsushima, Shohji

Optimization of Combustion Efficiency and Conversion Efficiency of Catalytic Converter in Spark-Ignited Engine using Taguchi Methods Robust Optimization Technique for Flex Fuel Application

2024-32-0123

04/18/2025

Flex fuel vehicles (FFV) can operate effectively from E5 (Gasoline 95%, ethanol 5%) fuel to E100 (Gasoline 0%, ethanol 100%) fuel. It is necessary to meet the performance, drivability, emission targets and regulatory requirements irrespective of fuel mixture combination. This research work focuses on optimizing the combustion efficiency and conversion efficiency of catalytic converter of a spark-ignited less than 200 cc engine for FFV using Taguchi methods robust optimization technique. The study employs an eight-step robust optimization approach to simultaneously minimize engine out emissions and maximize catalytic converter efficiency. Six control factors including type of fuel, catalyst heating rpm, lambda (excess-air ratio), injection end angle, lambda controller delay, and ignition timing are optimized. Four noise factors like compression ratio, clearance volume, catalyst noble metal loading, and catalyst aging are also considered. Through approximately 100 physical experiments on

Vaidyanathan, Balaji, Arunkumar, Praveenkumar, Shunmugasundaram, Palani, Murugesan, Manickam, Jayajothijohnson, Vedhanayagam

Numerical Investigation of Electrolyte Feed System Designs at the Stack Level of Vanadium Redox Flow Batteries

2024-32-0068

04/18/2025

The rise of electric vehicles (EVs) highlights the need to transition to a renewable energy society, where power is generated from sustainable sources. This shift is driven by environmental, economic, and energy security concerns. However, renewable energy sources like wind and solar are intermittent, necessitating extensive energy storage systems. Vanadium redox flow batteries (VRFBs) are promising for large-scale energy storage due to their long cycle life, scalability, and safety. In VRFBs, cells are typically connected in series to increase voltage, with electrolytes introduced through parallel flow channels using a single manifold. This design, while simple and low in pressure drop, often leads to imbalanced flow rates among cells, affecting performance. Balancing flow rates is crucial to minimize uneven overpotential and enhance durability, presenting an optimization challenge between achieving uniform flow and minimizing pressure drop. This study developed numerical models to

Suwanpakdee, Nut, Aiemsathit, Poramet, Charoen-amornkitt, Patcharawat, Suzuki, Takahiro, Tsushima, Shohji

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