Browse Topic: Electric vehicles

Items (5,032)
The organizers of the most prominent Formula Student competitions have recently initiated a preliminary feasibility study on the application of hydrogen-based propulsion technologies in future single-seater race vehicles. These include electric powertrains with electrochemically converted hydrogen in fuel cell–powered vehicles, competing within the electric championship league. Based on the initial set of regulations, this study presents a model-based comparison between battery-powered (BEVs) and fuel cell–powered electric vehicles (FCVs) for Formula Student. The analysis is conducted using energy, power, and efficiency metrics from four candidate models of propulsion systems, implemented in an open and publicly available MATLAB script: two BEVs with varying battery capacities, and two FCVs employing different hybridization strategies. The aim of this study is to pinpoint and quantify the advantages and disadvantages of each technology for the Formula Student use case, and to identify
Martoccia, LorenzoBreda, SebastianoFontanesi, Stefanod’Adamo, Alessandro
In a traditional electric vehicle, managing its battery thermal performance is of prime importance. A well-designed battery thermal management system helps in extending its life and avoids safety-related issues like thermal runaways. A critical part of this thermal management is the battery cooling system (BCS), which can be air- or liquid-cooled. Based on the vehicle battery pack size, location, and its design complexity, the original equipment manufacturer can opt for either of the previous two methods. An air-cooled type of BCS system usually involves an active ventilation fan to dissipate the battery heat in the surroundings, which brings symbiotic noise into the picture. In an air-cooled BCS system, the primary source of noise is the cooling airflow over the heat exchanger caused by the fan. The airflow and noise performance characteristics of this fan are typically measured by the supplier in a standalone condition. These performance parameters deviate greatly when the fan is
Nomani, MustafaDupatti, DarshanNikam, KrishnaSasikumar, R.Kajagar, SureshPanchare, DattajiAgalawe, Kiran
TOC
Tobolski, Sue
This SAE Recommended Practice establishes uniform procedures for testing BEVs that are capable of being operated on public and private roads. The procedure applies only to vehicles using batteries as their sole source of power. It is the intent of this document to provide standard tests that will allow for the determination of energy consumption and range for light-duty vehicles (LDVs) based on the federal test procedure (FTP) using the urban dynamometer driving cycle (UDDS) and the highway fuel economy driving schedule (HFEDS) and provide a flexible testing methodology that is capable of accommodating additional test cycles as needed. Additionally, this SAE Recommended Practice provides five-cycle testing guidelines for vehicles performing supplementary testing on the US06, SC03, and cold FTP procedures. Realistic alternatives should be allowed for new technology. Evaluations are based on the total vehicle system’s performance and not on subsystems apart from the vehicle.
Light Duty Vehicle Performance and Economy Measure Committee
This study investigates the gradeability performance of an L7e-class electric micro truck from both vehicle dynamics and thermal perspectives. A 1D simulation model (Amesim) was developed and validated with multiple test results. Using inputs such as motor characteristics, drivetrain configuration, and vehicle mass, the model analyzed vehicle performance on a 20% gradient, calculating the required torque, achievable motor speed, and corresponding vehicle speed. Furthermore, gradeability limits were evaluated, and the effects of gear ratio and airflow rate around the air-cooled motor on both gradeability and thermal behavior were examined. The findings provide practical insights for improving the powertrain and cooling system design of lightweight electric vehicles. The results showed that selecting an appropriate gear ratio can enable the motor to operate more efficiently under demanding driving conditions. A 20% increase in the gear ratio was found to delay motor heating by up to 10
Turan, AzimKantaroğlu, Hasan HüseyinAkbaba, MahirKasım, Recep FarukYarar, Göktuğ
Tailor Welded Blanks are critical for automotive lightweighting yet prone to premature failure due to differential thickness and strength across the weld. This study utilized digital image correlation (DIC) to analyze the maximum in-plane principal Hencky strain (E₁max) and axial strain (εₐₓₐₗ) of TWBs under complex loading conditions, including biaxial and plane-strain states. Twelve distinct material stack-ups were tested to evaluate the impact of material difference on formability. Results indicated that differential properties significantly altered strain distribution, often forcing localization onto the thinner or softer sheet. While UHSS welds provided high load capacity with limited ductility, combinations using HSLA or IF substrates were susceptible to early localization and unstable fracture. Comparative heatmaps illustrate strain evolution across all samples, providing spatial insights beyond conventional force–displacement analysis. Metallurgical characterization confirmed a
Aminzadeh, AhmadSheng, ZiQiangHuang, LuMcCarty, EricBiro, Elliot
Hydrogen fuel cell powered vehicles for heavy duty trucking are a promising path for reducing future vehicle emissions due to their reduced mass for storage and faster refueling compared to battery electric trucks. These benefits come at the cost of increased system complexity stemming from the fact that fuel cells generate electricity through a chemical reaction which must be tightly controlled. The air handling system delivers the proper amount of air (oxygen) to react with fuel (hydrogen) in the fuel cell to produce power. Air delivery requires significant power and is the largest parasitic loss for a 300 kW fuel cell. Today’s systems use an electric motor driving an air compressor to supply pressurized air to the fuel cell stack. By operating at elevated pressure levels, fuel cells can achieve higher power density, which is important for vehicle powertrains. In addition to parasitic power loss, hydrogen fuel cell systems often have reliability issues associated with the air
Reich, EvanSwartzlander, MatthewWine, JonathanMcCarthy, Jr., JamesMiller, EricAkhtar, SaadReddy, SharanLawy, TJ
This study presents an effective predictive methodology for determining the mechanical properties of glue-laminated motor cores, with explicit consideration of glue disposition, including bonding pattern, configuration, location, and coverage. In laminated stator cores, glue bonding and stacking processes jointly govern the mechanical integrity of the lamination stack. Practical production bonding schemes are typically nonuniform and localized, leading to spatial variations in stiffness and to locally anisotropic, orthotropic material behavior. These effects influence both the in-plane and through-thickness stiffness of the stator core. They can significantly affect the accuracy of structural simulations, such as NVH responses of high-speed traction motors and e-drive systems. Given the constituent material properties of the electrical steel laminations and the glue, this work distinguishes the governing mechanisms underlying the equivalent core properties. The in-plane stiffness is
Nie, Zifeng
With the rapid proliferation of electrified vehicles (xEVs), maximizing regenerative energy recovery has become a crucial challenge in realizing zero-emission mobility. In front-wheel-drive (FWD) vehicles, regenerative braking acts only on the front axle, resulting in a braking-force distribution biased toward the front. When uniform hydraulic pressure is applied to both axles, excessive braking force on the front wheels may cause premature wheel lock and hinder the intended regenerative braking effect. To address this issue, it is essential to implement an independent pressure control strategy (two-channel pressure control) that appropriately reduces front pressure according to regenerative force while independently maintaining adequate rear pressure. This study proposes a new two-channel pressure control architecture utilizing a simple and reasonable actuator set consisting of one electric cylinder and one solenoid valve. The electric cylinder generates hydraulic pressure by
Kaneko, ShosukeDeno, YoshitomoKobayashi, TatsushiKawamura, Hikaru
Ambient and initial temperatures significantly impact the energy consumption rate (ECR) of battery electric vehicles (BEVs) due to auxiliary loads and the temperature dependence of battery efficiency. This study introduces a streamlined, physics-based thermal modeling approach within the FASTSim tool that bridges the gap between oversimplified constant-load models and computationally expensive high-fidelity simulations. By employing a lumped thermal mass framework, the model captures fundamental energy balances and critical non-linear energy penalties while maintaining the computational efficiency required for expansive sensitivity studies. The simulations evaluated a compact BEV hatchback with a resistive heater over city (UDDS) and highway (HWFET) test cycles. Compared to a 22°C initial and ambient temperature baseline, a -7°C initial/ambient temperature resulted in a 221% increase in the ECR for the city cycle and a 100% increase for the highway cycle. Conversely, a 45°C initial
Baker, ChadSteuteville, RobinHolden, JakeGonder, JeffreyCarow, Kyle
The increasing demand for electrified transportation is leading to accelerated development of highly efficient hybrid and battery electric vehicles. A major concern for customers adapting to battery electric vehicles (BEV) is range anxiety due to low charging speeds, charging infrastructure not matching expectations and unreliable range estimations shown to the customers by their vehicles. Estimating the range more accurately has been difficult due to the sensitivity of vehicle’s energy consumption to real-world environmental and driving conditions. This paper aims to find out the effect of true wind in the road load experienced by BEVs in the real-world driving scenarios and how using a highly accurate wind speed measurement improves the energy consumption estimation better. On-road tests were conducted on public roads and in controlled test-track environments to collect reliable wind speed measurements using a dynamic multi-hole pressure probe. Additional coastdown tests were also
Raghupathy, Vishnu PrasaadKim, ShinhoonEvans, NicNiimi, KeisukeMochihara, Takahiro
Demand for cost-effective automotive traction inverters requires improved power module packaging. This paper presents a packaging method using an epoxy composite insulator applied directly to the cold plate surface, replacing Direct Bonded Copper (DBC) and Active Metal Brazed (AMB) substrates. This integration removes the substrate-to-cold plate solder interface and eliminates two material layers from the thermal path. The epoxy composite demonstrates a dielectric strength greater than 60 kV/mm. Thermal resistance (junction-to-coolant) measured approximately 0.17 K∙cm2/W. Electrical characterization showed a relative permittivity of 3.9, which is lower than standard ceramics and results in reduced parasitic capacitance. Initial thermal cycling tests indicated no significant degradation in thermal or electrical performance. These results suggest the epoxy composite insulator could be a promising alternative for traction power modules.
Chen, YuMena-Garcia, JavierChen, HaoXiao, KeweiGupta, Man PrakashDegner, Michael
Aerodynamic wind noise is a critical challenge in modern automotive development, particularly with the rise of vehicle electrification and intelligent mobility, where cabin acoustic comfort is a key quality metric. While reliable, traditional methods like wind tunnel experiments and computational fluid dynamics (CFD) simulations are both costly and time-consuming. To address these challenges, we propose a novel Transformer-based framework for rapid and accurate wind noise prediction. Several model improvements, including the physical attention, geometry wave number embedding, hybrid FPS-random downsampling method and frequency separation output heads are properly employed to reduce the GPU memory cost and improve the prediction accuracy. This framework is pre-trained on a large-scale acoustic dataset of nearly 1,000 diverse vehicles generated using Improved Delayed Detached Eddy Simulation (IDDES). From a vehicle's point cloud coordinates, the model directly predicts the surface
Tang, WeishaoLiu, MengxinQin, LingDuan, MenghuaWang, ChengjunZhang, YufeiWang, Qingyang
Global geopolitical volatility is recognized as a critical threat to the resilience of the electric vehicle battery supply chain. Static, manually updated databases are inadequate for capturing the sector’s rapid dynamics, resulting in significant information gaps for strategic planning. To address this, an Artificial Intelligence-driven methodology is proposed for constructing a comprehensive and dynamic database. An automated pipeline was implemented. First, real-time textual data are collected from curated news and industry sources using specialized web crawlers. Then, the unstructured data obtained undergo preprocessing, including deduplication and cleansing, to ensure quality. A core innovation involves the application of Large Language Models (LLMs) for deep semantic parsing and extraction of structured information. These models are utilized to accurately identify key entities—such as corporations, facilities, and production capacities—and to delineate complex multi-tier
Zhu, JuntongLuo, WeiZhang, XiangYang, ZhifengOu, Shiqi(Shawn)He, Xin
Achieving the stringent EPA CAFE 2032 standards for light-duty full-size trucks and sport-utility vehicles (SUVs) in North American poses significant challenges. While Battery Electric Vehicles (BEVs) offer a clear path to zero tailpipe emissions, their widespread adoption in this segment faces hurdles including range anxiety, payload/towing capabilities, and traditional truck/SUV use cases. This paper investigates a balanced approach, focusing on optimizing propulsion system design with appropriate hardware content, can effectively meet future fuel economy and emissions standards. This investigation examines advanced BEVs and hybrid electric vehicle architectures, including full hybrids (HEVs), and plug-in hybrids (PHEVs) tailored for full-size trucks and SUVs. Considerations include the optimal sizing of internal combustion engines, electric motors, and battery packs to deliver robust performance while maximizing energy efficiency. This paper analyzes the integration of technologies
Babcock, DillonRobinette, Darrell
General Motors (GM) continues to advance its electrification strategy through the development of scalable Battery Electric Vehicle (BEV) and Battery Electric Truck (BET) platforms. This paper highlights GM’s latest BEV and BET products that leverage shared Drive Unit (DU), Rechargeable Energy Storage System (RESS), and integrated power electronic (IPE) components across multiple vehicle programs. By adopting a modular and commonized propulsion architecture, GM achieves significant benefits in manufacturing efficiency, cost optimization, speed to market, and product flexibility. The shared DU, RESS, and IPE components are engineered to meet diverse performance requirements while maintaining high standards of energy efficiency, thermal management, and durability. This approach enables rapid deployment of electrified solutions across various segments, from passenger vehicles to full-size trucks, without compromising on capability or customer experience. The paper outlines the technical
Liu, JinmingSevel, KrisAnwar, MohammadOury, AndrewWelchko, BrianGagas, Brent
This study presents the vehicle control optimization of a Formula SAE (FSAE) electric vehicle developed by National Taiwan University Racing Team (NTU Racing), utilizing a dual-axle dynamometer and a real-time Hardware-in-the-Loop platform from Chroma. The novelty of this work lies in the comprehensive system-level validation of independent torque control strategies, namely Torque Vectoring (TV) and Traction Control (TC), implemented directly within the vehicle control unit (VCU), and the high-fidelity simulation of dynamic driving scenarios based on the FSAE circuit. The vehicle features an independently controlled rear-axle, two-wheel drive (2WD) configuration, consisting of two in-wheel motors, self-developed inverters, and planetary gearboxes. During testing, a pre-built CarSim driver model provides throttle, brake, and steering inputs to the VCU via Controller Area Network (CAN) interface. The VCU, in turn, computes the independent torque commands according to the TV and TC
Hsiao, Tsung-YuChen, Zhi-RenJian, Rong-WeiChen, Tai-HsiangWang, Tai-JieHu, Wei-ZheHo, Hui-TingWu, Ting-YuLin, Ting-HeChiu, Joseph
Electric vehicles (EVs) face unique safety challenges under pole side impact conditions, largely due to the presence of floor-mounted battery packs. Existing regulatory test procedures, such as FMVSS 214, primarily address occupant injury using full-height cylindrical obstacles. These procedures were originally developed for internal combustion vehicles (ICVs). However, real-world roadside crashes frequently involve obstacles of varying heights, such as guardrails, curbs, and median bases. While these obstacles pose limited risk to the passenger compartment, they can intrude into the battery pack and trigger thermal runaway. This study investigates the influence of obstacle height on EV pole side impacts. Finite element simulations of a commercially available sedan were conducted against rigid obstacles of different heights. Results reveal a non-monotonic trend of battery intrusion governed by the interplay between rollover dynamics and structural stiffness. Theoretical analyses were
Ma, ChenghaoXing, BobinZhou, QingXia, Yong
In vehicle development, noise reduction is critical for ensuring passenger comfort. As electric vehicles become prevalent and engine noise is minimized, wind noise becomes more noticeable. Modulated wind noise, which causes a sense of fluctuation due to atmospheric turbulence, wind gusts, and preceding vehicle wakes, can cause significant discomfort. This noise is characterized as a high frequency sound above 1 kHz, modulated at low frequencies owing to the wind velocity and direction fluctuating at several Hz. The mechanisms behind wind noise modulation are not fully understood, and no established countermeasures have been developed. This is because wind noise perceived through the side window is primarily caused by the A-pillar vortex and door mirror wake, which coexist as complex turbulent flows around the vehicle. Therefore, identifying the source of modulated wind noise around vehicles under fluctuating wind conditions is difficult. This study aims to identify the source of the
Tajima, AtsushiHirata, TakumiIkeda, JunKamiwaki, TakahiroWakamatsu, JunichiTsubokura, Makoto
Electric vehicle (EV) battery packs have undergone substantial advancements in recent years, driven by engineering design improvements, material innovations, and increasingly stringent regulatory enforcement. These developments have enabled battery packs to become more energy-dense, which is essential for extending driving range and improving overall vehicle performance. However, with increased energy density comes a higher severity of thermal events, such as thermal runaway, which continues to raise concerns regarding vehicle safety, reliability, and long-term durability. This review highlights the critical role that thermal insulation materials play in mitigating the impact of such thermal events within EV battery systems. It presents an overview of commonly used thermal insulation materials, emphasizing their chemical composition, thermal resistance, and mechanical integrity under extreme conditions such as high temperatures and physical stress. The ability of these materials to
Ng, Sze-SzeDhyani, AbhishekGorin, CraigJeon, JunhoNuguri, SravyaRepollet Pedrosa, MiltonRylski, AdrianShete, AbhishekSteinbrecher, JacobThomas, Ryan
In the stringent market of BEV, the development of integrated Drive Modules (iDM) fitting environmental and customer needs is mandatory. It is important to extract the best from the less. To achieve those goals, a deep insight into complex multiphysics phenomena occurring in an iDM has been achieved by accurate and validated models. This engineering methodology is applied through the development of BorgWarner products, comprising non-exhaustively iDM 180-HF, Externally Excited Synchronous Machine and Multi-Level Inverter. The paper will review the methodology development for deeper understanding involving in-house technical excellence and complemented by strategic partnerships with academic institutions and start-ups. It will present the approach of integrating advanced multiphysics models with high-quality experimental validations, specifically on loss evaluation on electrical machines and inverters. Complex models involving multiphysics such as thermal/fluid coupling or electric
Leblay, ArnaudBourniche, EricBossi, AdrienDavid, PascalNanjundaswamy, Harsha
As the utilization of lithium-ion batteries in electric vehicles expands, monitoring the usable cell capacity (UCC) is essential for ensuring accurate state-of-health (SOH) estimation. Battery performance degradation is influenced by temperature and constraints. Capacity tests in laboratory settings are typically conducted at low C-rates to approximate equilibrium conditions, whereas in real vehicle applications, charging currents are often much higher. This discrepancy in rates frequently results in deviations between laboratory characterization and on-board Battery Management Systems (BMS) capacity estimation. To investigate how C-rate of diagnostic Reference Performance Test (RPT) modulates aging effects under temperature and mechanical loading, we conducted long-term cycling tests on lithium iron phosphate/graphite pouch cells at 25°C and 45°C under different constrained conditions. The cycling protocol is a tiered multi-rate protocol. Cells were aged at Block1 under 1C, and UCC
Zhang, ShanNiu, ZhiceXia, Yong
Lithium-ion batteries are critical to Electric Vehicles (EV) and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires characterization of electrical, thermal, and mechanical properties at the full cell and cell-component levels. In this study, a commercial cell from an EV was disassembled, and tested to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell’s power capability. Full cell compression tests were conducted to characterize mechanical behavior under deformation and used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell integrity under mechanical deformation. At the
Challa, VidyuRostami-Angas, Masoudkong, KevinWang, LeyuReichert, RudolfKan, Cing-Dao
Battery fires pose a significant risk across a wide range of applications, including electric vehicles, consumer electronics, and grid-scale energy storage systems. Early detection of fire and smoke is critical to preventing catastrophic failures and ensuring human safety. In this study, we developed a synthetic dataset of battery fire and smoke images in the context of a simple battery pack. The primary application of this dataset is to support the development of a machine learning–based visual classification system capable of accurately detecting battery fires and smoke in real time at an early stage. The intended outcome is a deployable classification system that enhances battery safety through rapid visual identification of hazardous conditions.
Govilesh, VidarshanaGunasekaran, AswinChalla, KarthikeyaMaxim, BruceShen, Jie
The design and development of EVs and HEVs has become a growing issue recently due to concerns about pollution and dependence on non-renewable fossil fuels. Accordingly, General Motors (GM) has an evolving vehicle electrification plan over the past several decades and into the future to deliver low-cost and efficient EVs and HEVs. Propulsion system requirements for the applications of EV and HEVs are quite different and therefore, the design principles and directions are also distinct between these cases. From micro-hybrid and full plug-in hybrid applications to full EV applications, design requirements, strategies and outcomes can widely vary. Continuous and peak duty are substantially different depending on the application of the vehicle. Motor operational duty is significantly higher for EV compared to the electric motor of a hybrid electric vehicle. Motor torque, power and efficiency requirements are also higher for EV motors, which greatly influences the choice of motor type and
Momen, FaizulJensen, WilliamDas, ShuvajitChowdhury, MazharulAlam, KhorshedAnwar, MohammadReinhart, Timothy
This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of
Petersen, Nicholas ConnerRobinette, Darrell
The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables
Hofstetter, MartinLechleitner, Dominik
Vehicle pull under acceleration is a phenomenon commonly observed in high-performance vehicles and electric vehicles (EVs), primarily arising asymmetric driveshaft angles, drivetrain architecture, and suspension geometry. In addition to these mechanical factors, tire characteristics, particularly the tire lateral force generated at the contact patch, significantly influence this effect. The lateral force is intricately tied to the dynamics of the contact patch and the geometric design of the tire tread pattern. This study investigates the relationship between tread pattern geometry and vehicle pull under acceleration, emphasizing the role of tire lateral force variations. By employing finite element (FE) simulation, lateral force response variations (dfy/dfx) resulting from tread block deformation were analyzed. Based on these simulation, a robust analytical methodology for tread pattern evaluation and optimization was established. The developed tread pattern characteristic parameter
Yoon, YoungsamJang, DongjinKim, HyungjooLee, Jaekil
The anticipated PFAS ban in the US by 2029 has created a need to evaluate alternative refrigerant solutions for automotive thermal management systems. This work compares three candidates—Propane (R290), Carbon Dioxide (R744), and R1234yf—through system-level testing and demonstration projects. R1234yf remains the current industry baseline. Test results show that Propane (R290) delivers comparable efficiency while offering a significantly lower global warming potential. However, its flammability presents integration challenges, not present with R1234yf or R744. CO₂ (R744) demonstrated promising performance as well. To address safety concerns with Propane, AVL developed mitigation measures including rapid leak detection, robust containment strategies, and optimized circuit layouts designed to reduce ignition risks. These countermeasures were validated in practice through the European Commission’s QUIET project. Within this program, a Honda B-segment electric vehicle was equipped with a
bires, MichaelPossegger, Jonathan
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, ChristophHirz, MarioMartin, MichaelKreis, AlexanderStadler, Severin
The final assembly of electric vehicle (EV) drive units includes an essential End-of-Line (EOL) test to ensure both component integrity and Noise, Vibration, and Harshness (NVH) quality. This screening process, which uses dynamometers to measure vibration signals, is critical for identifying defects before a drive unit is installed in a vehicle. A significant source of failure during this test is gear defects, which can arise from manufacturing or handling issues. Traditional EOL testing methods rely on time-domain analysis and the impulsiveness of vibration signatures to detect these defects, a technique with inherent limitations in accuracy. This paper introduces and evaluates a novel approach using Machine Learning (ML) to analyze vibration signals for improved gear defect detection. We discuss the methodologies of both the traditional time-domain and the proposed ML-based techniques. Finally, we provide a comprehensive comparison of their respective efficiency and accuracy
Arvanitis, AnastasiosMichaloliakos, Anargyros
Thermal runaway in high-voltage lithium-ion battery modules should focus on critical safety and design challenges in electric vehicle applications, which need predictive methods that enhance passenger safety and support regulatory compliance. The primary purpose of a lithium-ion battery in an electric vehicle is to provide reliable energy storage while maintaining safe operation under different operating conditions. This study proposes a Design for Six Sigma (DFSS) methodology to virtually predict and correlate thermal runaway and its propagation in an 800V high-power lithium-ion battery pack module. Conventional propagation analysis relies heavily on physical testing, whereas the DFSS-based virtual framework enables cost-effective evaluation at early design stages. Input factors included are heat transfer pathways, which are sensitive to the temperature changes, as well as thermal propagation time. Control factors are the design or process parameters that engineers use to establish
Dixit, ManishRaja, VinayakGudiyella, Soumya
To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle’s lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to
Zhao, KunZhao, ZhiguoWang, YutaoXia, XueChen, XiHu, Yingjia
This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions
Taylor, Dwayne
The advancement of electric vehicles necessitates a rigorous focus on passenger cabin safety, particularly concerning the severe thermal hazard of a lithium-ion battery thermal runaway. Unlike internal combustion engine vehicles, electric vehicles require interior materials that provide superior thermal resistance to slow heat propagation, delay autoignition, and minimize smoke and toxic gas emissions, thereby securing a survivable evacuation window. This paper examines the application of the lumped-capacitance thermal model and the derived thermal time constant (τ) as a foundational framework for evaluating and selecting cabin materials. This approach enables a quantitative, physics-based ranking of materials—including seat composites, sound-deadening layers, electrical insulation, and carpet assemblies—based on their intrinsic ability to delay their own temperature rise under transient heat flux. By integrating materials with a high τ and elevated critical failure temperatures, this
El-Sharkawy, AlaaTaha, NahlaAsar, MonaSheta, Mai
Flow simulation with conjugate heat transfer, which involves fluid flow, conduction, and radiation within solid components, is a vital capability that enables engineers to design and assess cooling systems for heat-producing parts such as brakes, powertrains, batteries, and power electronics in both gasoline and electric vehicles. In this study, we employ PowerFLOW®, which features a thermal solver capable of simultaneously modeling both fluid and solid domains within a unified framework. The fluid flow is simulated using the Lattice Boltzmann Method (LBM) with VLES turbulence modeling based on the RNG k–ε approach. The solid domain is solved using a finite volume method with second-order accuracy for thermal conduction, combined with surface-to-surface radiation modeling for thermal exchange between surfaces. This integrated approach streamlines the simulation workflow while enabling accurate representation of both conduction and radiation phenomena. We assess the accuracy of the
Mukutmoni, DevadattaShock, RichardLi, HanWanderer, JohnGopalaswamy, NathMiao, Ling
Sparse Stream DETR 3D object detection has become pivotal in autonomous driving, and previous methods achieve remarkable performance by aggregating temporal information, which also face a balance problem of precision and efficiency. Knowledge distillation offers a promising solution to enhance the efficiency of a small model without incurring computational overhead; however, previous methods lack the exploration of the Temporal Distillation knowledge for the DETR detector. This paper designs a novel Temporal DETR Query Guidance paradigm to impart temporal relation knowledge from a powerful teacher model to enable the student to associate object states across time, leverage historical context. The teacher’s queries grasp the temporal knowledge through self-attention, and the backbone uses the EVA-02 large-scale image model. The student utilizes the teacher's self-attention layer and its own learnable queries to compute the attention as its guidance and mimics the feature interaction
Yan, Yixiong
The performance of a full battery pack with its effective thermal management system (BTMS) depends on coolant flow and heat transfer characteristics inside the pack. To develop a full BTMS using model-based design (MBD), the model must capture the coolant pressure drop ∆?? and heat-exchange performance from the cell to ambient air via the coolant, cooling flow channels, air gaps, and pack cases. Predicting battery pack responses (i.e., voltage, SOC, temperature) under all weather conditions is a challenge, as a complete pack contains several hundred to thousands of cells, coolant lines, coolant line bends, and coolant channels. This work presents a detailed approach to identifying heat transfer and ∆P correlations that can capture the real-time thermal-electrical performance of a mass-produced LIB pack under constant speed (in winter) and transient driving (in summer). A vehicle test is conducted using a Tesla Model Y, 2-motor model equipped with a 75-kWh LIB pack. The LIB pack's
Sok, RatnakKusaka, Jin
To effectively improve the performance of chassis control of distributed drive intelligent electric vehicles (EVs) under difference road conditions, especially in combing road information and chassis control for improving road handling and ride comfort, is a challenging task for the distributed drive intelligent EVs. Simultaneously, inaccurate chassis control and uncertainty with system input, are always existing, e.g., 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 control strategy of distributed drive intelligent EVs become a hot topic in both academia and industry. To issue the above mentioned, an adaptive torque vector hierarchical controller based on road level and adhesion is proposed, which optimizes the comprehensive. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and the rotor of the in-wheel motor, a
Wang, ZhenfengZhao, GaomingZhang, ZhijieZhou, ZitaoHuang, TaishuoMa, Changye
Object detection and distance prediction have advanced significantly in recent years. The YOLO toolbox has released its 11th version, along with numerous variants that have been applied across various fields. Meanwhile, the Detection Transformer (DETRs) has repeatedly set new state-of-the-art (SOTA) records in the field of object detection. Depth Anything also released its second version last year, further pushing the boundaries of distance detection. Although these models achieve impressive performance, they often require substantial computational resources. However, for the algorithms intended for real-world applications and deployment on onboard devices, computational efficiency are extremely critical. Inference time per frame is a critical factor in ensuring an algorithm’s reliability and feasibility. Designing a model that operates in real time without sacrificing accuracy remains an extremely challenging problem, and extensive research is ongoing in this area. To address this
Li, TaozheWang, HanchenHajnorouzali, YasamanXu, Bin
As the demand for electrical power has surged over recent years due to the increasing popularity of data centers for Artificial Intelligence (AI) and Electric Vehicles (EVs), it is becoming evident that the aging electrical grid infrastructure is struggling to keep up. Some of the problems this aging infrastructure has resulted in include frequent blackouts due to weather related events, reduced efficiency resulting in higher maintenance costs and outdated communication systems causing poor monitoring and response times. Modernization of the grid in conjunction with integration of the transportation sector with the grid is essential to ensure the reliability and resiliency of the grid. Electric vehicles have dramatically increased in popularity, with most vehicle manufacturers offering at least one electric option in their lineups. Looking at recent developments in vehicle-to-grid (V2G) technology, a new possibility becomes evident; instead of straining the power grid, the electric
Dahlmann, Alexander DrakeLele, Sneha
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