Browse Topic: Electrical, Electronics, and Avionics

Items (54,796)

Impact of Temperature on the Aging of Lithium-Ion Batteries: Insights from Capacity Testing for Enhanced Battery Management in Sustainable Transportation Systems

2025-01-8131To be published on 04/01/2025

This study focuses on a vital element of energy storage systems, particularly lithium-ion batteries, in the effort to cut down greenhouse gases and air pollutants from internal combustion engine vehicles. It investigates the impact of temperature on the aging process of these batteries, offering important insights into their capacity and lifespan. The research involved controlled testing at various temperatures to replicate real-world automotive conditions and evaluate how battery capacity changes over time. Initial characterization of fresh cells was conducted to establish baseline data on their capacity and internal resistance, which helps in understanding how temperature fluctuations affect battery performance. A thorough teardown analysis was then performed using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) on the electrodes, separator, and electrolyte. This analysis provided an in-depth look at the physical and chemical behavior of the battery

Garcia, Antonio, Monsalve-Serrano, Javier, Egea, Juan Manuel H.

Robust Predictive Cruise Control for Class 8 Trucks Considering Traffic Light Timing

2025-01-8033To be published on 04/01/2025

An implementation of a robust predictive cruise control method for class 8 trucks utilizing V2X communication with connected traffic lights is presented in this work. This method accounts for traffic signal phases with the goal of reducing energy consumption while maintaining reasonable travel speed and respecting safety concerns. Tightened constraints are created using a robust model predictive control (RMPC) framework in which constraints are modified so that the safety critical requirements are satisfied even in the presence of disturbances, while requiring only the expected bounds of the disturbances to be provided. In particular, variation in the actuator performance under different conditions presents a unique challenge for this application, which the approach applied in this work is well-suited to handle. The errors resulting from lower-level control and actuator performance are accounted for by treating them as bounded and additive disturbances on the states of the model used

Ellison, Evan, Ward, Jacob, Brown, Lowell, Bevly, David

A Fault Prediction Model for Electric Vehicle Charging Equipment Based on Adaptive Dynamic Thresholds

2025-01-8117To be published on 04/01/2025

The surge in electric vehicle usage has expanded the number of charging stations, intensifying demands on their operation and maintenance. Public charging stations, often exposed to harsh weather and unpredictable human factors, frequently encounter malfunctions requiring prompt attention. Current methods primarily employ data-driven approaches or rely on empirical expertise to establish warning thresholds for fault prediction. While these approaches are generally effective, the artificially fixed thresholds they employ for fault prediction limit adaptability and fall short in sensitivity to special scenarios, timings, locations, and types of faults, as well as in overall intelligence. This paper presents a novel fault prediction model for charging equipment that utilizes adaptive dynamic thresholds to enhance diagnostic accuracy and reliability. By integrating and quantifying Environmental Influence Factors (EF), Scenario Influence Factors (SF), Fault Severity Factors (FF), and

Wang, Hao, Wang, Ning, Li, Yuan, Tang, Xinyue

Verification of the Impact of Coefficient of Drag Variation due to Natural Wind on Fuel Consumption based on Actual Driving Data

2025-01-8778To be published on 04/01/2025

It is often assumed that the actual fuel economy on public roads is affected by natural wind and that this could be contributing to discrepancy between catalog fuel economy and actual fuel economy, owing to the increased yaw angle and drag coefficient (CD). However, the impact of yaw characteristics alone on fuel economy during actual driving has not been verified or proven as it is difficult to obtain actual driving data under uniform conditions. For this reason, shape optimization is normally performed at zero-yaw through the aerodynamic development phases. In this paper, two vehicles with different yaw sensitivity characteristics are driven simultaneously, and fuel economy measurements are performed simultaneously with ambient airflow, environment, and vehicle conditions, and the results where the conditions of the two vehicles match are extracted to clarify the impact of the differences of yaw characteristics on fuel economy. The obtained results matched the values predicted by

Onishi, Yasuyuki, Nichols, Larry, Metka, Matt, masumitsu, Yasutaka, Inoue, Taisuke

Optimization method of phase change energy storage device for electric vehicle batteries based on numerical simulation

2025-01-8603To be published on 04/01/2025

Phase change energy storage devices are extensively utilized in latent heat thermal energy storage and hold significant potential for application in the thermal management of automotive batteries. By harnessing the high-density energy storage capabilities of phase change materials to absorb heat released by the batteries, followed by timely release and utilization, there is a substantial improvement in energy efficiency. This approach aligns with the objective of promoting low-carbon environmental protection and presents an opportunity to enhance the operational performance and energy utilization efficiency of electric vehicle batteries in low-temperature conditions. However, the thermal conductivity of medium and low temperature phase change materials is poor, leading to its inefficient utilization. This paper focuses on optimizing the structure of a phase change heat exchanger in a phase change energy storage device to improve its performance. A basic design of the phase change heat

Zhang, Haonan, Sun, Mingzhe, Zheng, Haoyun, Zhang, Tianming

On-Road Investigation of Energy Saving Opportunity for Autonomous Light-Duty Vehicles through Automated Vehicle-Following in Safe Distance Scenarios

2025-01-8029To be published on 04/01/2025

Reducing aerodynamic drag through vehicle following is one of the energy reduction methods for connected and automated vehicles with advanced perception systems. This paper presents the results of an experimental investigation aimed at assessing energy reduction in manually driven light-duty vehicles through on-road tests of reducing the aerodynamic drag by vehicle following. This study provides insights into the effects of lateral positioning in addition to intervehicle distance and vehicle speed, and the profile of the lead vehicle. A series of tests were employed to analyze the impact of these factors, conducted under realistic driving conditions. The research encompasses various light-duty vehicle models and configurations, with advanced instrumentation and data collection techniques employed to quantify energy-saving potential. The study featured a two set of L4 capable light duty vehicles, including the Stellantis Pacifica PHEV minivan and Stellantis RAM Truck, examined in

Poovalappil, Aman, Robare, Andrew, Schexnaydre, Logan, Santhosh, Pruthwiraj, Bahramgiri, Mojtaba, Bos, Jeremy P., Chen, Bo, Naber, Jeffrey, Robinette, Darrell

Prognostic Techniques in Automated Driving System (ADS) Vehicle Safety

2025-01-8094To be published on 04/01/2025

The recent advancements in fields such as sensors, AI, and IoT are majorly impacting the automotive industry. Automated Driving Systems (ADS) are developing rapidly, meaning that SAE level 3 and above vehicles are quickly becoming a reality. As a result, maintenance of such systems becomes essential to ensure their safe and efficient navigation. Prognostic techniques in particular are crucial to predict the future health state of the system and its components. Many methods have been developed and are being used for prognostic of conventional vehicles as well as other industries. However, ADS is a new technology, and several of these approaches are still being developed and adapted to it. In this paper, we first present a review of the most used prognostic techniques across different safety-critical domains such as aerospace, power, and manufacturing. Then, we summarize the main challenges that must be faced to successfully develop novel approaches for ADS prognostic and provide a set

Merola, Francesco, Hanif, Athar, Lami, Giuseppe, Ahmed, Qadeer, Monohon, Mark

Tire wear life prediction using machine learning technique

2025-01-8757To be published on 04/01/2025

As global warming and environmental problems are becoming more serious, tires are required to accomplish balanced performance such as low rolling resistance, wet braking performance, driving stability, and ride comfort, while minimizing wear, noise, and weight. Furthermore, to respond to the rapid changes in the market environment, there is a strong demand for tire performance prediction and testing methods adapted to MBD. In MBD, Magic Formula and Ftire are used in performance design to predict handling and stability and ride comfort. However, predicting tire wear life, which is affected by both vehicle and tire characteristics, is difficult. And practical prediction method for estimating tire wear life has long been awaited. So, we had proposed an experimental-based tire wear life prediction method that using actual measurement tire characteristics and an abrasive wear volume formula. This method achieves accuracy that can be practically used in the early stages of vehicle

ANDO, TAKASHI

Quantifying the uncertainty in bicycle-computer speed measurements

2025-01-8701To be published on 04/01/2025

The goal of this study is to quantify the uncertainty in bicycle computer measurements of speed. We acquired 100+ hours of data at 1-second intervals simultaneously from three Garmin Edge 530 computers mounted to the same handlebars during road cycling in rural and urban environments. Each computer recorded speed data using a different method: One unit was paired to a Garmin GSC 10 speed-and-cadence sensor (wheel rotation based), a second unit was paired to Garmin Speed Sensor 2 and Cadence Sensor 2 (accelerometer based), and a third unit was not paired to any remote sensors and relied on the device’s GPS position to calculate speed (GPS based). Based on these data, we generated probability distributions that quantify the uncertainty in the accelerometer based and GPS based speed measurements. This study provides foundational information regarding the appropriate methods to quantify the uncertainty in bicycle-computer speed data and to probabilistically interpret these data for

Booth, Gabrielle R., Siegmund, Gunter P.

Collision avoidance system at urban intersections using V2X communication

2025-01-8030To be published on 04/01/2025

Driving safely through urban intersections is quite challenging for self-driving vehicles due to complicated road geometries and the highly dynamic maneuvers of oncoming traffic, which can cause a high risk of collisions. Traditional onboard sensors like cameras, radar, and lidar give limited visibility in this environment. To overcome these limitations, this paper explores implementing a collision avoidance system at urban intersections utilizing cellular vehicle-to-everything (V2X) communication. The system leverages V2X map data to identify warning and collision zones and uses vehicle-to-vehicle (V2V) communication to estimate the maneuver types and paths of approaching vehicles. The system assesses collision probabilities by calculating the intersection points of predicted paths for the ego vehicle and oncoming traffic. Depending on the level of collision probabilities, the system generates a collision warning signal to the driver or activates emergency braking when necessary to

Park, Seo-Wook, Suresh, Raynier, Ailuri, Anusha

An experimental investigation of the liquid jet breakup characteristics in a vaporizer under equivalent operating conditions of a microturbine combustion chamber

2025-01-8458To be published on 04/01/2025

This study experimentally investigates the liquid jet breakup process in a vaporizer of a microturbine combustion chamber under equivalent operating conditions, including temperature and air mass flow rate. A high-speed camera experimental system, coupled with an image processing code, was developed to analyze the jet breakup length. The fuel jet is centrally positioned in a vaporizer with an inner diameter of 8mm. Airflow enters the vaporizer at controlled pressures, while thermal conditions are maintained between 298 K and 373 K using a PID-controlled heating system. The liquid is supplied through a jet with a 0.4 mm inner diameter, with a range of Reynolds numbers (Reliq=2300-3400), and aerodynamic Weber numbers (Weg=4-10), corresponding to the membrane and/or fiber breakup modes of the liquid jet. Based on the results of jet breakup length, a new model has been developed to complement flow regimes by low Weber and Reynolds numbers. The analysis of droplet size distribution

ha, Nguyen, Quan, Nguyen, Manh, Vu, Pham, Phuong Xuan

Understanding Forming Characteristics of 980 MPa and 1180 MPa strength level AHSS with various Microstructures

2025-01-8222To be published on 04/01/2025

For electrical vehicle (EV) automotive body-in-white (BIW) structures, protection of passengers and battery in crash event becomes critically important. In addition to energy absorption, intrusion protection for battery and vehicle becomes extremely important and Advanced high strength steels (AHSS), like dual phase (DP)/multi-phase (MP)/complex phase (CP)/retained austenite bearing (GEN3) steels and Press hardened steels (PHS), have become material of choice for design for these components. Higher yield strength materials especially in 980/1180MPa MP and CP category are chosen for part design over conventional low yield strength DP to meet crash and performance targets. In this study, the forming characteristics including both global and local formability are evaluated and compared among 980 DP/MP/CP grades. Formability tests, such as forming limit curve (FLC), true fraction strain, V bend, half dome, and hole expansion tests are conducted. Microstructure analysis to understand the

Shih, Hua-Chu, Pednekar, Vasant, Shi, Ming, Singh, Jatinder, Tedesco, Sarah, Wu, Wei

Bicycle Pitch-over Reconstruction Analysis

2025-01-8684To be published on 04/01/2025

During a pitch-over event, the forward momentum of the combined bicycle and rider is suddenly impeded at the front tire and front axle causing the rider and bicycle to rotate about the bicycle front structures and also possibly propelling the rider forward. A pitch-over event can result in significant injuries to the rider. This paper presents the examination of the pitch-over of a bicycle by approaching the event using two different methods over what has been published previously. One method uses Newton’s 2nd Law directly and the other method uses the principle of impulse and momentum, the integrated form of Newton’s 2nd Law. The two methods provide useful equations, contributing to current literature on the topic of reconstructing and analyzing bicycle pitch-over incidents. The analysis is supplemented with Madymo simulation models to evaluate the kinematics and kinetics of the bicycle and rider interacting with front wheel obstructions of different heights. The effect of variables

Brach, R. Matthew, Kelley, Mireille, Van Poppel, Jon

Optimal Hybrid Drive Configuration for Synthesized ICE

2025-01-8534To be published on 04/01/2025

In order to reduce the emission in individual traffic electrification and hybridization is needed. The emission reduction depends on the degree of electrification and the specific drive system design and optimized components. This applies especially for hybrids with the highest amount of components, consisting of internal combustion engine, transmissions, electric motors and batteries. With the integration of the EM in the transmission for dedicated hybrid transmissions, the amount possible solutions and variants lies in the billions. Hence for the development of new drive architectures computer aided drive synthesis are needed. In a DFG funded project we developed a drive synthesis, including an ICE synthesis as well as transmission synthesis. The syntheses are controlled via an artificial intelligence based control algorithm including engineering knowledge. In this paper we are focusing on the impact the ICE concept has on the drive system dimensioning, the fuel economy, performance

Sturm, Axel

Disturbance Estimation Approach for minimizing Control Windup in Target Engine Speed Profile on Electrified Powertrains with a Low Voltage Belt Starter Generator and a Disconnect Clutch

2025-01-8584To be published on 04/01/2025

In cost- effective P2 hybrid vehicles with low voltage electric machines connected to the engine, an interesting control problem arises during the transition to a locked driveline state. This occurs when the engine connects to the wheels via a separation clutch. The two primary torque sources, the engine and the clutch, are traditionally imperfect estimators of applied and transferred torques. The Hybrid Supervisor’s feedforward constraints model relies on these imperfect inputs to determine torque and acceleration limits for the engine’s desired acceleration profiles and to specify engine feedforward commands, aiming for synchronization speed. Due to the inaccuracies in the torque estimates of the engine and clutch, the Hybrid Supervisor is susceptible to control windup, increased jerk to the driveline during synchronization, and inaccurate computation of its target acceleration profile, speed, and torque targets for the engine to achieve synchronization speed. This paper presents a

Banuso, Abdulquadri, Sha, Hangxing, Karogal, Indrasen, Madireddy, Krishna Chaitanya, Patel, Nadirsh

European initiatives for User-Centric design of Electric Vehicle

2025-01-8807To be published on 04/01/2025

E-mobility is revolutionizing the automotive industry by improving energy efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under the

Ratz, Florian, Bäuml, Thomas, Kompara, Tomaž, Kospach, Alexander, Simic, Dragan, Jan, Petra, Möller, Sebastian, Fuse, Hiroyuki, Parades Barros, Esteban, Armengaud, Eric, Amati, Nicola, Sorniotti, Aldo, Lukesch, Walter

Lithium battery health status and lifespan prediction based on deep learning

2025-01-8126To be published on 04/01/2025

Rechargeable lithium batteries are widely used in the electric vehicle industry due to their long lifespan and high energy density. However, after long-term repeated charging and discharging, various electrochemical reactions inside lithium batteries can lead to performance degradation and even cause battery fires. Estimating the health status and predicting the remaining life of lithium batteries can provide insights into their future operating conditions, which is crucial for achieving fault warnings and ensuring the safe operation of battery-related equipment. In terms of predicting the health status of lithium batteries, this paper proposes a method based on an improved LSTM for health status estimation. This method first employs nearest neighbor component analysis to eliminate feature redundancy among the multidimensional health factors of the battery. Then, the differential grey wolf optimization algorithm is used to globally optimize the hyperparameters of the LSTM. The

K, meng zi

Recommendation of Adaptive Safe Speed Limit for different driver Profiles using Deep Learning

2025-01-8106To be published on 04/01/2025

Personalization and driver profiling have emerged as popular topics within the Advanced Driver Assistance Systems domain. Several features, like seat position adjustment and route selection deliver a customized experience for everyone. The systems often profile the driver and their preferences using Machine Learning algorithms to anticipate the needs of the driver. Within this field, personalization of speed limits is not explored to the fullest. Currently, speed limits are the same for all drivers, irrespective of their driving style. Even when the speed limits are designed to address adverse weather conditions, they do not consider the driver’s capabilities. Thus, we propose a novel idea to recommend a safe speed limit based on the driving style and the surrounding environmental conditions. The architecture consists of Long Short-Term Memory based neural networks that capture the driver’s traits based on risks of the environment. The system identifies if a driver is uncomfortable in

Perumal, Rathapriya, Chouhan, Madhvendra, Rangarajan, Rishi

Model-Predictive Control for Heat Pump-Based Battery Thermal Management in Off-Road Autonomous Electrified Vehicles During Transient Operation

2025-01-8184To be published on 04/01/2025

The shift towards hybrid and electric powertrains in off-road vehicles aims to enhance mobility, extend range, and improve energy efficiency. However, heat pump-based thermal management systems in these vehicles continue to consume significant energy, impacting overall range and efficiency. Effective thermal management is essential for maintaining battery performance and safety, particularly in extreme conditions. Although high-fidelity models can capture the complex dynamics of heat pumps, real-time control within model-based optimization frameworks often depends on simplified models, which can degrade system performance. To address this, we propose a novel data-driven grey box control-oriented model (COM) that accurately represents the thermal dynamics of a vapor-compression refrigeration-based heat pump system. This COM is integrated into a model-predictive control (MPC) framework, optimizing thermal management during transient and burst-power operations of the battery pack. We

Sundar, Anirudh, Ghate, Atharva, Zhu, Qilun, Prucka, Robert, Ruan, Yeefeng, Figueroa-Santos, Miriam, Barron, Morgan

Application of AI/ML based Image Analytics in Auto Component Fracture Analysis

2025-01-8228To be published on 04/01/2025

This paper introduces an innovative digital solution for the categorization and analysis of fractures in Auto components, leveraging Artificial Intelligence and Machine Learning (AI/ML) technologies. The proposed system automates the fracture analysis process, enhancing speed, reliability, and accessibility for users with varying levels of expertise. The platform enables users to upload images of fractured parts, which are then processed by an AI/ML engine. The engine employs an image classification model to identify the type of fracture and a segmentation model to detect and analyze the direction of the fracture. The segmentation model accurately predicts cracks in the images, providing detailed insights into the direction and progression of the fractures. Additionally, the solution offers an intuitive interface for stakeholders to review past analyses and upload new images for examination. The AI/ML engine further examines the origin of the fracture, its progression pattern, and the

Sahoo, Priyabrata, Rawat, Sudhanshu, Garg, Vipin, Naidu, Garima, Sharma, Amit, Narula, Rahul, Bindra, Ritesh, Khera, Pankaj, Goel, Pooja, MONDAL, ARUP

Effectiveness of Nail Penetration for Thermal Propagation Test in Battery Packs and Vehicles

2025-01-8128To be published on 04/01/2025

The number of electric vehicles (EVs) has increased significantly in recent years. Consequently, incidents of EV fires have been reported. To protect passengers and evaluate battery robustness, thermal propagation tests are conducted. Typically, these tests initiate a cell in a battery pack using either a heater or a nail. While the heater method is commonly used, the nail method faces restrictions when selecting an initiation cell, especially for battery pack and vehicle testing. For battery pack testing, vertical penetration against the vehicle's travel direction does not have cell selection restrictions. However, for horizontal penetration, the initiation cells are limited to the outermost cells in the battery pack. In vehicle testing, horizontal penetration is unfeasible, whereas vertical penetration from bottom to top is possible. To demonstrate the feasibility of the nail method for battery pack and vehicle testing, especially in vehicles, nail tests were conducted on an electric

Maeda, Kiyotaka, Takahashi, Masashi

Driver’s Pedal Control for Detecting Pedal Misapplication

2025-01-8675To be published on 04/01/2025

Drivers sometimes operate the accelerator pedal instead of the brake pedal due to driver error, potentially resulting in serious accidents. Acceleration Control for Pedal Error (ACPE) has been developed, a system that detects such errors and controls vehicle acceleration to prevent these incidents. The United Nations is already considering regulations for this technology. If ACPE can detect errors based on the operations of the accelerator pedal at various driving speeds, the system will be even more effective in terms of safety. The purpose of this study is to investigate the operation characteristics of the accelerator pedal and brake pedal, focusing on the pedal operation characteristics of pedal stroke speed and pedal force speed used as thresholds for the operation of ACPE. We believe that if there is a significant difference in the operations of the accelerator pedal and brake pedals while driving, it can be used as a threshold for judging misstep. In this study, we utilized a

Natsume, Hayato, Shen, Shuncong, Hirose, Toshiya

Analysis of Trapped Gases Within an Aftertreatment System Reacting Over a TWC After Engine is Stopped

2025-01-8499To be published on 04/01/2025

Measurements of Hydrogen emissions from vehicle exhaust have been often substituted for prediction models, partly due to the lack of Hydrogen analyzers targeted for combustion gases. A previous study using a Hydrogen mass spectrometer revealed that the ratio of Hydrocarbons entering a Three-Way Catalyst (TWC) and Hydrogen leaving the catalyst was inconstant throughout a standardized driving cycle. Although Hydrogen by itself is not currently a target of emission regulations, its omission during catalyzer optimization may disrupt the intended performance of the integrated aftertreatment system. The highest emissions of unwanted gases are commonly seen during vehicle cold start. Thus, this study focuses on intermittent operation of an engine, such as that of full hybrid vehicles. In particular, this study measures how the gases trapped in the aftertreatment system continue to react over the TWC as it cools down after the engine stops. Hydrocarbons (HC), NOx, NH3 and H2 are measured

Lamas, Jorge Eduardo, Lacdan, Ma Camille, Hara, Kenji, Otsuki, Yoshinori

Deep Learning-Based Intelligent Tire Wear Detection Method

2025-01-8278To be published on 04/01/2025

In order to obtain real-time tire wear status of vehicles, this paper proposes a deep learning-based tire wear detection method. First, a PVDF piezoelectric film sensor is attached to the center of the tire airtight layer with different wear degrees to collect tire stress data under different working conditions. Secondly, the collected data is filtered, and the time domain feature information is extracted by principal component analysis to construct the feature data set. Finally, a deep regression model is established to train the feature data set to realize the real-time detection of tire damage. The results show that the tire wear detection error based on deep learning is less than 0.3mm, which has a high tire wear detection accuracy. It provides tire information for vehicle safe running and has high industrial application value

Xianyi, Xie, Yang, hao, jin, Lisheng

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

2025-01-8101To be published on 04/01/2025

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

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

Fuzzy Sliding Mode Control of Intelligent Electric Vehicles Based on Phase Plane Stability Domain Boundaries

2025-01-8802To be published on 04/01/2025

In order to effectively improve the chassis handling stability and driving safety of intelligent electric vehicles (IEVs), especially in combing nonlinear observer and chassis control for improving road handling. Simultaneously, uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IEVs become a hot topic in both academia and industry. To issue the above mentioned, a fuzzy sliding mode control method based on phase plane stability domain is proposed to enhance the vehicle’s chassis performance during complex driving scenarios. Firstly, a two-degree-of-freedom vehicle dynamics model, accounting for tire non-linearity, was established. Secondly, combing with phase plane theory, the stability domain boundary of vehicle yaw rate and side-slip phase plane based

Liao, Yinsheng, Wang, Zhenfeng, Guo, Fenghuan, Deng, Weili, Zhang, Zhijie, Zhao, Binggen, Zhao, Gaoming

A framework for modeling mechanically induced thermal runaway in lithium-ion batteries

2025-01-8134To be published on 04/01/2025

Battery safety is a paramount concern in the development of electric vehicles (EVs), as failures can lead to catastrophic consequences, including fires and explosions. Ensuring the reliability and robustness of lithium-ion cells under various conditions is essential for the widespread adoption of EVs. With the rapid global adoption of EVs, understanding how battery cells perform under extreme conditions such as mechanical or thermal abuse is crucial for ensuring safety. This study investigates the dynamic behavior of lithium-ion pouch cells under mechanical abuse conditions, focusing on improving safety measures in EV battery systems. Our research systematically examines the response of these cells at different states of charge (SOC), through controlled dynamic tests. These tests, which involve varying strain rates and punch geometries, offer significant insights into the mechanical thresholds that lead to cell failure. By analyzing the data, we gain a deeper understanding of the

Patanwala, Huzefa, kong, Kevin, Challa, Vidyu, Darvish, Kurosh, Sahraei, Elham

AI-Enhanced CAE Simulations: A Revolutionary Approach to Automotive Design and Engineering

2025-01-8241To be published on 04/01/2025

In the automotive industry, the durability and thermal analysis of components significantly impact vehicle component robustness and customer satisfaction. Traditional computer-aided engineering (CAE) methods, while effective, often involve extensive design iterations and troubleshooting, leading to prolonged development times and increased costs. The integration of artificial intelligence (AI) and machine learning (ML) into the CAE process presents a transformative solution to these challenges. By leveraging AI and ML, the durability simulation time of automobile components is significantly enhanced. Altair’s Physics AI tool utilizes historical CAE data to train ML models, enabling accurate predictions of model performance in terms of durability and stiffness. This reduces the necessity for multiple simulations, thereby decreasing CAE model design and solution completion times by 30%. By predicting potential issues early in the design phase, AI and ML allow engineers to make informed

Patil, Amol, Sonavane, Pravinkumar

A drive anti-slip and lateral stability control technique for distributed three-axis drive vehicle

2025-01-8303To be published on 04/01/2025

The research object of this project is the longitudinal and lateral control of the distributed three-axis drive tractor. Which is different from the traditional four motors power system layout is that the third axel have two motors while the second axel only have one motor. Compared with the traditional design, it can reduce the dependence on battery performance and maintain motor operation in high efficiency range by switching different working model. For example, when driving at high speed, only the two-axis motor works, which can improve motor efficiency. When accelerate or climb, all motors work to provide a large power output. In the research, established the vehicle model in Simulink firstly, and then Co-simulated with Trucksim. The longitudinal control identified the optimal slip rate of the road firstly, then used the adaptive sliding mode control to obtain the driving torque of each wheel, which can achieve well control effect under various road conditions and motion modes

Shen, Ruiteng, Zheng, Hongyu, Kaku, Chuyo, Zong, Changfu

Reducing Temperature-related Aging Inhomogeneities in Battery Modules Using a Switchable Thermal Management System

2025-01-8167To be published on 04/01/2025

The operating temperature of lithium-ion battery (LIB) cells significantly influences their degradation behavior. In indirect liquid cooling systems, temperature variations within a Battery Electric Vehicle (BEV) LIB module are inevitable due to the increasing downstream temperature of the cooling medium as it absorbs heat. This leads to reduced temperature differentials between the cooling medium and the LIB cells. As a result, LIB cells located further along the flow path experience higher average temperatures than those at the front. Typically, a maximum average cell temperature difference of 5 K within LIB modules is considered acceptable. However, results from a conventional cooling system indicate that, when fast charging is exclusively used, this can lead to a 15.5 % difference in the total ampere-hours passed before the End-of-Life (EOL) is reached for the front and back LIB cells. To address this issue, a switchable thermal management system for the traction battery is

Auch, Marcus, Weyershäuser, Konstantin, Kuthada, Timo, Wagner, Andreas

Machine Learning Based Lane Detection and Lateral Offset Estimation Model for Vehicle Following Applications

2025-01-8020To be published on 04/01/2025

Precisely understanding the driving environment and determining the vehicle's exact position are crucial for the safe operation of Autonomous Vehicles. In the vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLO-V8 image segmentation, trained on a diverse real-world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to real-world

Karuppiah Loganathan, Nirmal Raja, Poovalappil, Aman, Naber, Jeffrey, Robinette, Darrell, Bahramgiri, Mojtaba

Aggressive Autonomous Control on Snow and Ice

2025-01-8040To be published on 04/01/2025

In cold and snowy areas, low-friction and non-uniform road surfaces make vehicle control complex. Manually driving a car becomes a labor-intensive process with higher risks. To explore the upper limits of vehicle motion on snow and ice, we use an existing aggressive autonomous algorithm as a testing tool. We built our 1:5 scaled test platform and proposed an RGBA-based cost map generation method to generate cost maps from either recorded GPS waypoints or manually designed waypoints. From the test results, the AutoRally software can be used on our test platform, which has the same wheelbase but different weights and actuators. Due to the different platforms, the maximum speed that the vehicle can reach is reduced by 1.38% and 2.26% at 6.0 m/s and 8.5 m/s target speeds. When tested on snow and ice surfaces, compared to the max speed on dirt (7.51 m/s), the maximum speed decreased by 48% and 53.9%, respectively. In addition to the significant performance degradation on snow and ice, the

Yang, Yiming, Bos, Jeremy P.

Using CAN Electrical Signals for Cybersecurity and Harness Diagnostics

2025-01-8080To be published on 04/01/2025

This paper describes a novel invention which is an Intrusion Detection System based on fingerprints of the CAN bus analogue features. Clusters of CAN message analogue fingerprints can be associated with each ECU on the network. During a learning mode of operation, fingerprints can be learnt with the prior knowledge of which CAN identifier should be transmitted by each ECU. During normal operation, if the fingerprint of analogue features of a particular CAN identifier does not match the one that was learnt then there is a strong possibility that this particular CAN identifier’s message is symptomatic of a problem. It could be that the message has been sent by either an intruder ECU or an existing ECU has been hacked to send the message. In this case an intruder can be defined as a device that has been added to the CAN bus OR a device that has been hacked/manipulated to send CAN messages that it was not designed to (i.e. could be originally transmitted by another device). It could also

Quigley, Christopher, Charles, David

Controllability and Driving Automation

2025-01-8679To be published on 04/01/2025

A key challenge for manufacturers of automotive systems, hardware components and software products with no contribution to driving automation is the stringent requirements imposed on elements while being integrated into vehicles with driving automation. The result is increased development cost and low reusability. For such elements or components with no contribution to driving automation, their functions and failure modes remain unchanged when comparing vehicle integration with and without driving automation. The influence of driving automation is not accounted for in the current approach of classifying risk while conducting a Hazard Analysis and Risk Assessment (HARA). Functional safety standards for on-road vehicles rely on human intervention as a parameter to classify risk. Since current safety standards for on-road vehicles are not inclusive of driving automation concepts, classification of risk, based on existing definitions of parameters such as controllability, leads to

Shah, Mihir, Ibarra PhD, Ireri

Potential for Reduction in NRMM Real-World Emissions

2025-01-8489To be published on 04/01/2025

The pollutant emission regulation for Non-Road Mobile Machinery (NRMM) is currently under consideration, both in the European Union (EU) and the United States (US). In Europe a Stage V review is expected within 2025 and in the US, the California Air Resource Board (CARB) has released their Tier 5 proposal in late 2024. It is expected that there will be further focus on covering a wide variety of operation conditions in actual use cases, including continuous low load scenarios. In addition, CO2-neutral fuels are being investigated to reduce the carbon footprint of NRMM Internal Combustion Engines (ICE), which remains an important powertrain for the sector. The objective of the work presented is to assess the potential for emissions reductions in the future, both NOx and CO2. A simulation study is conducted, modelling a 9l class engine with 8-10 g/kWh engine-out NOx emission level. Three different emission control systems are investigated: an enhanced stage V system with single SCR, a

Demuynck, Joachim, Bosteels, Dirk, Michelitsch, Philipp, Noll, Hannes

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

2025-01-8397To be published on 04/01/2025

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

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

Energy-Efficient Maneuvering of Connected and Automated Vehicles: NEXTCAR Phase II Results

2025-01-8385To be published on 04/01/2025

Onboard sensing and Vehicle-to-Everything (V2X) connectivity enhance a vehicle's situational awareness beyond direct line-of-sight scenarios. A team led by Southwest Research Institute (SwRI) demonstrated 20% energy savings by leveraging these streams on a 2017 Prius Prime as part of the first phase of the ARPA-E-funded NEXTCAR program. When combined with automation, these information streams can not only improve vehicle safety but also enhance energy efficiency further. In the second phase, SwRI demonstrated 30% energy savings over the baseline by leveraging connectivity with higher levels of automation. This paper summarizes the efforts to achieve 30% savings on a 2020 Honda Clarity PHEV. The vehicle was outfitted with the SwRI Ranger automated driving suite for perception and localization. Model-based control schemes with selective interrupt and control (SIC) were used to override stock vehicle controls and actuate the accelerator and brake pedals, and the electric power steering

Bhagdikar, Piyush, Gankov, Stanislav, Sarlashkar, Jayant, Hotz, Scott, Rajakumar Deshpande, Shreshta, Rengarajan, Sankar, Adsule, Kartik, Drallmeier, Joseph, D'Souza, Daniel, Alden, Joshua, Bhattacharjya, Shuvodeep

Comparative Kinematic Performance Testing to SAE J3230 on a Dynamometer and Test Track

2025-01-8792To be published on 04/01/2025

SAE J3230 provides Kinematic Performance Metrics for Powered Standing Scooters. These performance metrics include many tests which require specific conditions including flat pavement with a near zero slope, drivers of specific height and weights, and data acquisition equipment. In order to determine the efficacy of replicating SAE J3230 tests in a laboratory setting, a device called the Micromobility Device Thermo-Electric Dynamometer was used alongside outdoor tests to provide a comparison of scooter performance in these two testing applications. Based on the testing outcomes, it can be determined whether SAE J3230 and similar standards for other micromobility devices can be replicated in a lab-based setting, saving time, operator hazard, and providing more thorough data outputs

Bartholomew, Meredith, Andreatta, Dale, Zagorski, Scott, Heydinger, Gary

Vehicle Motion Management - A model predictive control approach to realize holistic redundancy to enable actuator fail operational autonomous driving

2025-01-8812To be published on 04/01/2025

Since the series introduction of ESC (1995), the number of vehicle dynamic control functions and ADAS has increased continuously. Today, there are many functions that control the vehicle motion in cooperation, so that the vehicle motion is often implemented suboptimally. Current megatrends (electrification, AD, new SW architectures) and new key technologies (X-by-Wire) enable the development of Integrated Chassis Control (ICC), which controls all motion-relevant components to optimize vehicle motion. This function, which is currently being developed by many OEMs, not only improves the driving safety and comfort but is also furthermore essential for AD. Vehicle Motion Management (VMM), the ICC solution by IAV GmbH, uses Model Predictive Control (MPC) to predict and optimize vehicle motion in real time. The VMM architecture already developed (Observer, Supervisor, MPC, Coordination) can be adapted to the powertrain topology. This article reports on the results of a simulative potential

Wielitzka, Mark, Ahrenhold, Tim, Vocht, Moritz, Rawitzer, Jonas, Schrader, Jonas

Model-Driven Systems Engineering Automation with Artificial Intelligence for Robustly Writing Automotive System Requirements

2025-01-8658To be published on 04/01/2025

The intensive use of software applications in modern vehicles has highlighted the critical role of Systems Engineering (SE) in the automotive industry. These “computers on wheels” are thoroughly interconnected, by their own connections and with the cloud, due to the advancement of Electronic Control Units (ECU) technologies and the widespread use of sensors transmitting real-time data. This interconnectedness and the level of software abstraction that are known today, significantly escalates the complexity of these systems. This has made it necessary to adopt an approach that is flexible to change, structured, agile, and traceable. The modern approach to SE, now model-based, offers numerous advantages over the previous paradigm, which was predominantly document-based. MBSE (Model-Based Systems Engineering) emerges as a contemporary approach, providing the scalability needed for engineering teams to develop robust products. Its “model-based” essence ensures that the model acts as the

Mendes de Oliveira, Arthur Hendricks, Reis, Pedro Almeida, Anunciação, Gabriel, Vinícius Carlos de Lima, Jonathan, Sarracini Júnior, Fernando, Garcia, Matias Ezequiel

Research on the Motor Mode Control based on the Shut off Test in Dual Motor Hybrid System

2025-01-8576To be published on 04/01/2025

In order to improve the safety and reliability of the inverter used in hybrid vehicles and reduce the risk of inverter failure, based on the functional safety ISO26262 development process and software architecture, a safe shutdown path scheme is designed in this paper. After entering the initialization mode, on the basis of adding the inverter control signal feedback mechanism on the inverter control system, this scheme designs the control methods and specific processes of the shutdown path test and insulation detection. The shutdown path test and insulation detection designed in this scheme are implemented during the control initialization process, including designing the hardware diagnostic safety mechanism and the unique output shutdown path test method. If the shutdown path test or insulation detection fails, the risk of IGBT out of control can be avoided; the detection mechanism of this system can effectively reduce the failure rate and potential failure rate of faults. The CAN

Jing, Junchao, Liu, Yiqiang, Zuo, Botao, Huang, Weishan, Dai, Zhengxing

LLM-Powered Fuzz Testing of Automotive Diagnostic Protocols

2025-01-8091To be published on 04/01/2025

Modern vehicles contain tens of different Electronic Control Units (ECUs) from several vendors. These small computers are connected through several networking busses and protocols, potentially through gateways and converters. Moreover, vehicle-to-vehicle and internet connectivity are now considered to be requirements, adding an addi- tional layer of complexity to an already complex electronic system. Due to this complexity and the safety-critical nature of vehicles, au- tomotive cyber-security a difficult undertaking. One critical aspect of cyber-security is the robust testing of software for potential bugs and vulnerabilities. Fuzz testing is an automated software testing method that injects a large set of inputs to a system. It is an invaluable tech- nique across many industries, and has been increasingly gaining popu- larity since its conception. Its success highly relies on the ”quality” of inputs injected. One shortcoming associated with fuzz testing is the expertise required in

McShane, John, Celik, Levent, Brooks, Richard, Pesé, Mert D., Aideyan, Iwinosa

Highly accurate Machine Learning models for Automotive Crash applications using CAE centric AI/ML Platform

2025-01-8719To be published on 04/01/2025

In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid virtual simulation and decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of not knowing if the trained model is sound enough to drive the design. In addition, the complexity of porting simulation data back and forth to a ML software can make the process cumbersome. We would like to put forth a highly accurate and comprehensive one-stop ML workflow that has been implemented within a commercially available CAE driven platform called DEP MeshWorks. MeshWorks is used for rapid concept CAE and CAD model generation and parametrization. The AI platform within Meshworks leverages the inherent best-in-class parameterization capability to help rapidly generate data for training purposes. It is relatively quick to set up a fully parameterized CAE model and vary design

Krishnan, Radha

YRSRA: Yaw and Roll Stable Region Analytics for Ground Vehicle System

2025-01-8799To be published on 04/01/2025

This article presents a software tool YRSRA for calculating both the yaw and roll stable region for ground vehicle system. Firstly, the iterative model of yaw rate and slip angle is constructed through deducing the two-degree-of-freedom vehicle dynamics. Secondly, the load transfer ratio (LTR) is coded with given yaw rate and slip angle. Thirdly, several Illustrative examples are depicted, such as variation of steer angle, road adhesion coefficient and vehicle speed. The software features an easy to generate yaw and roll stability region by on-demand configuring vehicle parameters, but can also be scripted and used as a library

Tu, Lihong, Zeng, Dequan, Zhang, Zhouping, He, Qixiao, Zhao, Shuqi, Sun, Jing, Wang, Aichun, Yu, Qin, Ming, Jinghong, Wang, Xiaoliang, Hu, Yiming

A Control Strategy for Height Adjustment in Closed-Loop Air Spring Suspension System

2025-01-8798To be published on 04/01/2025

This study investigates the closed-loop air spring suspension system by developing comprehensive models for both the air spring and its associated pipeline. To address issues such as "overcharging," "over-discharging," and significant overshoot during vehicle height adjustment, a threshold control strategy was developed. This strategy manages the trigger conditions for height adjustment to mitigate overshoot and precision problems. Experiments demonstrated that while this control strategy reduced overshoot and improved accuracy, it introduced the risk of excessive control due to varying threshold settings across different modules. To resolve this issue, a fuzzy PID controller was designed, utilizing fuzzy rules to adapt PID parameters in real-time, thereby enhancing height adjustment control. Testing confirmed that the fuzzy PID controller effectively resolved the issues associated with threshold control and also improved the rate of height adjustment. Furthermore, to prevent excessive

Zheng, Guoqing, Yin, Zhihong, Chen, Shiwen, Shangguan, Wen-Bin

Development of a Test Course Selection Method for Statistically Correct Wind Modeling on US Public Roads

2025-01-8780To be published on 04/01/2025

Traditional automotive aerodynamic development relies on wind tunnel testing and Computation Fluid Dynamics (CFD), where the former provides reliable values to be used for fuel economy calculations, and the latter enables the investigation of the flow features responsible for improvement/degradation of the averaged large-scale performances in terms of aerodynamic coefficients. The abovementioned procedure overlooks a crucial factor: natural wind. The speed of the wind encountered while driving alters the vehicle’s effective yaw angle. Such condition implies that the minimization of the drag coefficient at zero-yaw, commonly performed through wind tunnel testing and CFD simulations, may not yield real-world optimal shapes. While it is possible to employ a wind signal as an input in a wind tunnel, the signal itself must be available beforehand, and such key element is the focus of the present research effort. In this paper, we explain a methodology behind the selection of a statistically

Nucera, Fortunato, Onishi, Yasuyuki, Metka, Matt

Advanced Engine Cooling System for a Gas-Engine Vehicle, Part III: State Feedback Control System Design

2025-01-8161To be published on 04/01/2025

This paper presents an advanced control system design for an engine cooling system in an internal combustion engine (ICE) vehicle. Building upon our previous work, we have derived models for crucial temperatures within the engine, including combustion wall temperature, coolant-out temperature, block temperature, as well as temperatures in external components such as heat exchangers and radiator. To accurately predict these temperatures in a rapid manner, we have utilized a lumped parameter concept with a mean-value approach. This approach allows for precise temperature estimation while maintaining computational efficiency. Given the complexity of the cooling system, we have proposed a linear time-varying (LTV) model predictive control (MPC) system to regulate the temperatures. This control system linearizes the model at each time step and applies linear MPC over the control and prediction horizons. By doing so, we effectively control the highly nonlinear and time-delayed system

Chang, Insu, Sun, Min, Edwards, David

Applications of Advanced High-Strength Steels for Automotive Structures – Haulage Load body for EV

2025-01-8219To be published on 04/01/2025

To develop a Light weight Load body structure for Electric haulage vehicle application without affecting strength & durability of the product. Conventional haulage Load body given for diesel vehicle application weight is approximately 20% of vehicle Kerb weight. Payload carried by customer does not get affected much. Electric vehicle Kerb weight is comparatively higher than diesel vehicle, due to battery packs & its mounting structures. So the payload carried by customer is getting affected. Hence Load body weight must be reduced to 10-15% of Kerb weight. By considering past field failure data, customer loading pattern, durability trials & CAE static structural analysis, light weight Load body is designed with optimized sections & HSS material. To delight the customers, Number of Floor cross members & sideboard pillars not reduced which is their first impression before buying any haulage vehicle. Multiple iterations & design failure mode effective analysis was done before finalizing

Lakshminarayanan, Karthik

Tire Thermal Model with State Observer Integration for Enhanced Real-Time Temperature Prediction

2025-01-8756To be published on 04/01/2025

This paper focuses on the development of a tire thermal model for automotive applications, addressing the challenge of accurately predicting tire temperatures on different layers of the tire, under various driving conditions. The primary goal is to enhance the understanding of tire temperature behavior to improve safety, performance, and durability. The research utilizes a physics 1-D model for the tire, from which a system of differential equations, describing the interaction between different layers of the tire, is derived. Furthermore, a state observer is used to estimate tire temperatures, using Tire Pressure Monitoring System (TPMS) measurements to correct model predictions. In particular, the TPMS measurements are assumed to be sufficient to exclude the additional thermal contributions coming from the rims and disk brakes, which simplifies the model, making it more suitable for real-time applications. A calibration procedure is defined for deriving the model parameters, based on

Longobardi, Armando, Balaga, Sanjay, labella, Mario, Gorine, Mohamed El Amine

Aerodynamic Effect on Vehicle Handling

2025-01-8754To be published on 04/01/2025

Vehicle handling is significantly influenced by aerodynamic forces, which alter the normal load distribution across all four wheels, affecting vehicle stability. These forces, including lift, drag, and side forces, cause complex weight transfers and vary nonlinearly with the vehicle's apparent velocity and orientation relative to wind direction. In this study, we simulate the vehicle traveling on a circular path with constant steering input, calculate the normal load on each tire using a weight transfer formula, calculate the effect of lift force on the vehicle on the front and rear, and calculate the vehicle dynamic relation at steady state because the frequency of change due to aero load is significantly less than that of yaw rate response. The wind velocity vector is constant while the vehicle drives in a circle, so the apparent wind velocity relative to the car is cyclical. Our approach focuses on the interaction between two fundamental nonlinearities: the nonlinear aerodynamic

Patil, Harshvardhan, Williams, Daniel

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