Browse Topic: Electric vehicles

Items (5,033)
Find
Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches2026-01-0628To be published on 04/07/2026
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, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie, Zhou, Zitao, Huang, Taishuo, Ma, Changye
Techno-economic assessment of battery recycling and cascade use for legal and illegal recyclers in China2026-01-0456To be published on 04/07/2026
With the strong momentum of electric vehicles (EVs), the battery recycling industry is undergoing rapid growth. While the Chinese government has implemented a white-list mechanism under which only approved recyclers are allowed to process retired batteries, small-scale illegal battery recycling vendors have posed a serious challenge. This study compares the techno-economic performance of battery recycling between legal and illegal recyclers in China, and makes recommendations to eliminate illegal operations. Our research covers two battery chemistries: lithium nickel-manganese-cobalt oxide (NMC) and lithium iron phosphate (LFP), as well as two technological pathways: resource recycling and cascade utilization. For the general case, the costs of illegal vendors are 35-46% lower than that of legal companies. Although legal companies achieve high resource utilization, their overall economic performance lags behind due to their high costs associated with equipment, environmental protection
Du, Shilong, Li, Haoyang, Dou, Hao, Hao, Han
Enabling Sustainable E-Mobility: An Edge–Cloud Collaborative Framework for Battery Lifecycle Health Management in Electric Vehicles2026-01-0460To be published on 04/07/2026
The rapid adoption of electric vehicles (EVs) is a cornerstone of the transition to sustainable transportation. However, uncertainty regarding battery degradation remains a significant obstacle, hindering vehicle energy efficiency, operational safety, and the recovery of end-of-life value. Accurate estimation of the battery state of health (SOH) and prediction of the remaining useful life (RUL) are therefore critical for sustainable vehicle lifecycle management. This study proposes an edge–cloud collaborative intelligent framework for in-vehicle deployment that leverages a Transformer-based architecture to jointly model SOH and RUL. The cloud-side model retains the full configuration to capture long-term degradation trajectories for high-accuracy RUL prediction. A lightweight edge-side model, engineered via pruning and knowledge distillation, delivers millisecond-level inference for real-time SOH estimation onboard the vehicle. To ensure efficiency, only four core health indicators are
Gao, Weimin, Lv, Zhilong, Ou, Shiqi(Shawn)
Collaborative Development Framework for electric-based Software-Defined Vehicles – The European Research project CODE4EV2026-01-0113To be published on 04/07/2026
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, CCAM, C-ITS). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of SW development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. In the context of the Industrial Action Plan for the European automotive sector , different funding mechanisms have been designed to accelerate development and uptake of SDV. It is the target of this paper to introduce different European programs on SDV - focusing on their main objectives, expected benefits for the R&D&I community, and complementarity of these initiatives. Especially, this paper will introduce Chips-JU programs targeting the creation of a vivid ecosystem for European Software Defined Vehicle of the Future (SDVoF) Initiative
Armengaud, Eric, Permann, Robert, Joergler, Sabrina, Barcelona, Miguel Angel, García, Laura, Rodriguez, José Manuel, Ivanov, Valentin, Li, Zhenqian, Nguyen Quoc, Trieu, Rodrigues, Sandy, Kowalczyk, Bogdan, Avdić Čaušević, Amra
ENERGY AND OPERATIONAL EFFICIENCY OF SHARED AUTONOMOUS FLEET2026-01-0464To be published on 04/07/2026
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The test aims to verify the
Tang, Kang, Abdulsattar, Harith, Yang, Hao, Wang, Jinghui
EV Propulsion Drive Converter SiC Power Device Over-Temperature Protection under Low Motor Speed Operation2026-01-0436To be published on 04/07/2026
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with widely used Si-IGBT. However, SiC MOSFET is a more expensive device, so the present technology trend is trying to reduce its chip size. Installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET
Thongam, Jogendra Singh, Ge, BAOMING, Bradford, Steven, Kulkarni, Milind
Advancing Sustainable Electric Vehicles: A Simulation-Driven Study on Rare Earth-Free Motors and Battery Technologies2026-01-0242To be published on 04/07/2026
As electric vehicles (EVs) become more popular, the growing dependence on rare earth materials in their motors and batteries has raised concerns about supply risks, environmental impact, and rising costs. Finding alternatives to these materials is essential to making EVs more sustainable and affordable for everyone. This study aims to explore new motor designs and battery chemistries that reduce or eliminate the use of rare earth elements. We will focus on promising options like induction and wound rotor motors, as well as ferrite and iron-nitride magnets that can replace traditional rare earth magnets in EVs. On the battery side, we will look at lithium-iron-phosphate (LFP) and sodium-ion batteries, which offer a safer and more cost-effective alternative while maintaining good performance. A big part of this work involves using advanced simulations to predict how these new materials and designs will perform. This lets us optimize the components before building physical prototypes
Saraswat, Shubham, Vishe, Prashant
Development of the Honda S+ Shift Utilizing e:HEV System2026-01-0418To be published on 04/07/2026
Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver
Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, GUNJI, HIROKI, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa
Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry2026-01-0161To be published on 04/07/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. The core of the approach is an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
Proactive Faulty Detection in EV Batteries Via Hybrid method: Cell-to-Cell Variation Pattern And Physics-Based Model insights.2026-01-0391To be published on 04/07/2026
This study presents a distinct methodology for the early detection of faulty cells in electric vehicle (EV) battery systems, leveraging temporal voltage deviation patterns under real-world charging scenarios alongside outputs from a physics-based model. A comparative longitudinal analysis was conducted on a fleet of ten EVs—five exhibiting stable performance and five demonstrating early-stage anomalies characterized by intermittent transitions from drive to neutral mode. These behavioral cues were investigated as precursors to deeper battery degradation. The analysis focused on cell-level voltage dispersion during the mid-to-high state-of-charge (SoC) range (approx. 20–30% to full charge). Vehicles in healthy condition consistently displayed minimal voltage differentials among cells, whereas those with latent faults showed markedly higher variance, particularly between the highest and model-expected voltages. Notably, this voltage divergence was often accompanied by a modest yet
Jawle, Bharat Sanjay, Selvakumar, Ashwin, Puttoji Rao, Nagaraj Kumar
Low-Cost Packaging of SiC Power Modules for EV Traction Inverters2026-01-0119To be published on 04/07/2026
The increasing demand for efficient and cost-effective power electronics in electric vehicle (EV) traction inverters necessitates innovative power module designs. This paper presents a novel SiC power module packaging focused on low-cost fabrication and assembly.. The proposed design features a simplified sandwich structure consisting of a lead frame, the innovative epoxy composite deposited on a high-performance cold plate. The novel epoxy composite eliminates the critical solder connection typically found between the DBC/AMB and the cold plate. This significantly streamlines the thermal path by reducing the number of material layers and interfaces between the SiC chip and the coolant. As a result, the integrated stack-up achieves significantly low junction-to-coolant thermal resistance (Rth) while delivering substantial cost savings related to power module module packaging. This work provides the details on the power module prototype fabrication steps and functionality including the
Chen, Yu, Mena-Garcia, Javier, Chen, Hao, Xiao, Kewei, Gupta, Man Prakash, Degner, Michael
BEV incremental CO2 emissions in a ranked order electric grid2026-01-0424To be published on 04/07/2026
Battery Electric Vehicles (BEV) have been promoted and required by many governments towards the need to reduce transportations CO2 emissions. BEV are not “Zero Emissions Vehicles” because they run on grid electricity, but could be ‘effectively ZEV’ if the incremental CO2 is ‘very small’. To answer this question at the national level, three metrics are needed and estimated: (1) ICEV fuel consumption, approximated by the total US gasoline consumption divided by the total fleet miles driven, 25 mpg or 350 g CO2/mi. For strong HEV it is about one third less, 240 g CO2/mi. (2) BEV energy consumption, using data from systematic on-road testing of a wide range of vehicles, estimated at 40 kWh/100 mi for a US sales mix. (3) Electric grid marginal CO2, with ranked order sourcing: zero-CO2 sources are prioritized and supplemented by fossil sources. IEA hourly data show that the US 48 contiguous states are nearly self-contained, with zero-CO2 sources providing about a third of total demand. The
Phlips, Patrick
Beyond PFAS: Unlocking the Potential of R290 and R744 for EV Efficiency2026-01-0127To be published on 04/07/2026
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. In AVL’s Refrigerant Cooling Project, direct CO₂ cooling battery plate simplified the thermal system architecture and provided adequate cooling capacity during high charging loads. 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
bires, Michael, Possegger, Jonathan
Electrochemical-Thermal Simulation for Evaluating Indirect and Immersion Cooling in High-Power EV Charging2026-01-0403To be published on 04/07/2026
With the rapid advancement of electric vehicle (EV) fast-charging technology, battery thermal management faces increasingly critical challenges due to elevated heat generation and stringent safety requirements. Immersion cooling has emerged as a promising solution because of its superior heat dissipation and uniform temperature distribution compared to conventional indirect cooling methods. This study develops an electro-thermal coupled simulation framework for evaluating indirect and immersion cooling performance under high-power charging conditions. A pseudo-two-dimensional (P2D) electrochemical battery model of an electric vehicle was developed in GT-Suite and validated against vehicle on-board charging data. In parallel, an immersion cooling system model was developed and validated using battery bench test results. This model was subsequently integrated into the vehicle model to allow comparisons with a conventional indirect cooling system under high-power DC fast-charging scenario
Guo, Yuyang, Rockstroh, Toby, Haenel, Patrick, Oezdag, Erdal, Bodemann, Basil, Toghyani PhD, Somayeh
Optimizing EV Battery Models for Full Vehicle Simulations2026-01-0389To be published on 04/07/2026
The increasing adoption of electric vehicles (EVs) demands accurate yet computationally efficient battery models that can be integrated into full vehicle simulations. At the cell level, mechanical battery models often employ fine-scale elements to capture localized deformation and failure phenomena. While such detailed discretization enables high-fidelity predictions, it also imposes significant computational costs that become prohibitive when scaling up to pack-level or full-vehicle crash and durability simulations. This research addresses the challenge by systematically simplifying cell-level mechanical models to reduce computational burden while preserving predictive accuracy. We propose an approach in which larger elements and reduced complexity representations are introduced without compromising the model’s ability to replicate experimentally observed behaviors. The methodology emphasizes model validation against targeted loading conditions, ensuring that the essential mechanics
Sahraei, Elham, Brar, Bavneet, Parmar, Dhruv, Muralidharan, Umachandran
Propulsion System Design Considerations for HEV versus EV Applications2026-01-0641To be published on 04/07/2026
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, Faizul, Jensen, William, Das, Shuvajit, Chowdhury, Mazharul, Alam, Khorshed, Anwar, Mohammad, Reinhart, Timothy
Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe2026-01-0455To be published on 04/07/2026
Accurate projection of Plug-in Electric Vehicle (PEV) market sales share is vital for evidence-based policymaking, yet existing studies employ diverse and often fragmented methodologies, creating a need for a systematic review to clarify their analytical foundations and comparative strengths. This study classifies mainstream approaches to market projections into theory-driven and data-driven categories and reviews the merits, limitations, and future directions of five representative models. Analysis reveals that leading approaches increasingly employ cross-scale model coupling, theory-data fusion, and modular design to harness complementary strengths, improving model robustness and predictive accuracy. Furthermore, the study compares PEV policies and market outlooks in China, the United States, and Europe—the world's three largest automotive markets. The findings indicate a strong linkage between projection convergence and policy stability. China demonstrates the highest policy
Luo, Wei, Ou, Shiqi(Shawn), Zhou, Pan, Wang, Tianpeng, Qian, Xiaodong
Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations2026-01-0458To be published on 04/07/2026
Electric vehicles (EVs) are central to sustainable transport, yet battery service life remains a limiting factor for cost and adoption. Distinct from traditional laboratory-based simulations that often fail to capture the complexity of field conditions, this study investigates how EV user behavior—including driving style and charging demands—influences capacity using large-scale, real-world operational data from daily EV usage. A data-driven framework is developed to quantify driving and charging behaviors through multidimensional feature extraction at the vehicle level and estimate battery State-of-Health (SOH) trajectories, enabling direct linkage between individual behavior patterns and degradation outcomes. Results reveal substantial heterogeneity in aging rates explicitly driven by diverse user behaviors: under identical urban conditions, vehicles with a radical driving style exhibit approximately 81% faster SOH decline per 20,000 km than those with a moderate style ; regarding
Liu, Tianyi, Jing, Hao, Zhu, Jiankuan, Chen, Yongjian, Ou, Shiqi, Qian, Xiaodong
A Large Language Model-Based Database for Analyzing the Electric Vehicle Battery Supply Chain2026-01-0471To be published on 04/07/2026
Global geopolitical volatility poses a critical threat to the resilience of the electric vehicle battery supply chain. Static, manually updated databases fail to capture the sector's rapid dynamics, creating significant information gaps for strategic planning. This study proposes an Artificial Intelligence-driven methodology to construct a comprehensive and dynamic knowledge base. An automated pipeline is implemented, beginning with specialized web crawlers that harvest real-time textual data from curated news and industry sources. This unstructured data is preprocessed through deduplication and cleansing to ensure quality. The core innovation involves applying fine-tuned Large Language Models (LLMs) to perform deep semantic parsing and extract structured information. These models precisely identify key entities—corporations, facilities, production capacities—and delineate the complex multi-tier relationships from raw material extraction to final distribution. The output is an
Zhu, Juntong, Luo, Wei, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi(Shawn), He, Xin
Cradle-to-grave assessment of Austrian passenger car traffic based on actual vehicle movements and derivation of future forecasts for minimising greenhouse gas emissions2026-01-0457To be published on 04/07/2026
As part of the decarbonisation process for passenger car fleet in Austria, battery electric cars in particular have been subsidised in recent years, as these vehicles are considered to be largely emission-free during use and are expected to reduce emissions in future. However, in order to sustainably reduce the global greenhouse gas emissions of Austrian passenger car traffic, taking into account all types of propulsion systems, it is necessary to apply a cradle-to-gate approach, as is commonly done in comparable analyses in the literature, which evaluates the emissions of the entire vehicle life cycle. The most important phase in the life cycle assessment remains the well-to-wheel phase, which includes emissions from energy supply and vehicle use. Due to the large number of influencing factors, highly simplified models are usually used for this phase in the literature. As part of this work, a methodology was developed that, allows an in-depth analysis of entire vehicle fleets by
Lischka, Gregor, Tober, Werner
New Inverter Switching Control Method to Enhance Sound Quality for Electric Vehicles2026-01-0437To be published on 04/07/2026
Conventional inverter control uses a fixed switching frequency, which leads to a high-pitched switching noise in electric vehicles (EVs) that does not vary with vehicle speed. Although EVs are generally much quieter than traditional internal combustion engine (ICE) vehicles, some EV owners complain about the lack of dynamic driving sound feedback. A new patented technology has been developed to enhance EV sound quality by dynamically controlling the inverter switching frequencies. This technology generates dynamic propulsion sound with new "switching order" features at multiple harmonics, with the pitch proportional to vehicle speed. A fixed pulse ratio between the switching frequency and the electric motor RPM is implemented to control the switching order. This reduces switching losses during low-speed operation and provides boosted acoustic feedback to the driver during acceleration, which enhances driving experience during sports driving. Furthermore, a special "EV shifting" sound
He, Song, Gagas, Brent, Welchko, Brian, Ball, Kerrie, Gong, Cheng
In-Use Life Digital Twin and Swappable Use Case for Battery Durability2026-01-0397To be published on 04/07/2026
In-Use Life Digital Twin and Swappable Use Case for Battery Durability By: Patricia Laskowsky, Josue Albarran, Justin Bunnell, and Andrew Zettel Abstract The UN GTR 22 is required as part of the Environmental Protection Agency’s legislative rulemaking for multi-pollutants emission standards for model years 2027 and later light-duty and medium duty vehicles. The EPA claims it has the right to govern requirements on ZEVs and BEVs. ZEVs, in their viewpoint, are “emission related” because it enables the vehicle to produce zero emissions. With the adoption by the EPA under its Tier 4 regulations of the UN GTR 22, comes several new tracking DIDs, Data Identifiers. These DIDs are tied battery durability and energy tracking They track conditions and usages of electric vehicles that may accelerate the loss of electric range and/or loss of efficiency of the propulsion energy storage and delivery. More specifically, tying these trackers to EPA emission related discussion, the loss of electric
Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue
Climate Heat Recovery Energy Study in Electric Vehicles2026-01-0129To be published on 04/07/2026
Climate control systems in Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Extended Range Electric Vehicles (EREVs) rely on electrical energy to provide cabin heating. In winter conditions, the absence of waste heat from internal combustion engines necessitates increased energy consumption for thermal comfort, which directly impacts vehicle range. Conventional HVAC systems typically operate with a mixture of cold ambient air and recirculated cabin air. However, the proportion of recirculated air is limited due to windshield fogging risks, constraining energy-saving potential. To address this, MAHLE has developed modular heat recovery technologies that utilize the thermal energy of exhaust cabin air, that would normally leave the passenger compartment through vehicle body vents, to precondition incoming fresh air, thereby reducing the heating load. These solutions are engineered for scalable integration into existing HVAC architectures, allowing
Wolfe, Edward, Wochele, Kerstin, Reid, Bailey
Data-driven feedforward compensation-based trajectory tracking control2026-01-0028To be published on 04/07/2026
Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and
Yang, Yijin, Yuan, Yin, Wang, Zhenfeng, Su, Ailin, Zhang, Zhijie, Lu, Yukun
Modeling Non-Uniform Temperature Distribution in Lithium-Ion Batteries2026-01-0120To be published on 04/07/2026
Effective thermal management is critical to the performance, lifespan, and safety of lithium-ion batteries in electric vehicles (EV). Transient and non-uniform temperature distributions within cells accelerate degradation, cause pack imbalance, and reduce efficiency. Localized hotspots, in particular, promote lithium plating, solid electrolyte interface (SEI) growth, and other failure mechanisms. Persistent non-uniformities increase the likelihood of thermal runaway under high-stress operation. To address these challenges, this work develops a one-dimensional thermal model that captures heat generation and dissipation under dynamic operating conditions, incorporating both conduction within the cell and active convective cooling. Using a DFSS-based approach, we evaluate design and cooling strategies that minimize temperature differentials while maintaining acceptable operating ranges. The findings provide practical guidance for battery thermal management system design, emphasizing
El-Sharkawy, Alaa, Asar, Mona, Serpento, Stan, Sheta, Mai
Low-voltage electric vehicle with increased electrical safety and distributed charging system2026-01-0396To be published on 04/07/2026
Abstract - Reducing the high-voltage BEV to a household level of 120-240 volts is considered in the article as an effective means of solving the problems of electrical safety, maintenance and minor repairs of an electric vehicle in household conditions, distributed power supply of BEV with walking distance for the driver. The analysis of the low-voltage electric drive is performed under the assumption that the battery has a nominal voltage of 200 volts. The issues of transforming a high-voltage machine (400 volts) into a low-voltage one (200 volts) by switching the stator phase sections from serial to parallel connection without changing the overall and energy characteristics are considered. It is shown that a two-motor unit with induction machines with a capacity of 50 kilowatts can provide 100 kilowatts in long-term and up to 200 kilowatts in peak modes. The article considers the issues of implementing a low-voltage inverter and modern trends in distributed power supply for BEVs
Smolin PhD, Victor, Sobolevskiy, Anatoliy, Volovich, Georgy
Advancing Safe Battery Design through Experimental Cell Characterization of Thermal, Electrical and Mechanical Properties2026-01-0408To be published on 04/07/2026
Lithium-ion batteries are critical to electric vehicles and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of battery thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires the characterization of electrical, thermal, and mechanical properties of the battery at the full cell and cell-component levels. In this study, a commercial cell from an electric vehicle was disassembled, and testing was conducted 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 power capability. Cell punch tests were conducted to characterize mechanical behavior under deformation. These tests were 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
Challa, Vidyu, Rostami-Angas, Masoud, kong, Kevin, Wang, Leyu, Reichert, Rudolf, Kan, Cing-Dao
Transformer-Based Deep Learning Framework for Efficient Prediction of Automotive Wind Noise2026-01-0222To be published on 04/07/2026
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 experiment and CFD simulations are both costly and time-consuming. To address this, 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 down sampling method and frequency separation output heads are properly employed to reduce the GPU mermory cost and improve the prediction accuracy. This framework is pre-trained on a large-scale transient flow field dataset of 1,000 diverse vehicles generated using Delayed Detached Eddy Simulation (DES). From a vehicle's point-cloud coordinates, the model directly predicts the surface pressure spectrum on the driver-side window and the
TANG, Weishao, Liu, Mengxin, QIN, Ling, Duan, Menghua, wang, chengjun
Novel Steel Tubular Side Sill Reinforcement Design and Validation for Enhanced Crash Performance in EVs2026-01-0478To be published on 04/07/2026
Increased use of Electrical vehicles (EVs) brings in unique structural design challenges particularly in Side Impact scenarios when the battery pack is near the sill region therefore elevating the possibility of Electrical Module penetration and triggering thermal runway event resulting from the crash. Thus, Electric Vehicles present unique structural design challenges. This study intends to evaluate a novel tubular structure design for side sill reinforcement and its performance benefits over a traditional aluminum side sill reinforcement currently commonly utilized for battery protection in EV vehicles body components. The proposed Steel side sill design mainly focuses on structural rigidity and improved load transfer during side pole impact crash cases and address manufacturing complexity and cost reduction opportunity. Multiple iterations for Steel side sill reinforcement design were carried out using finite element analysis with software like LSDYNA Hyper works to propose optimal
Kusnoorkar, Harsha, Khutorsky, Alex, Penumetsa, Vivek, Koraddi, Basavaraj
Development and Application of CAE Methods for Predicting Snow Accumulation on Floor Undercovers During Snowy Road Driving2026-01-0482To be published on 04/07/2026
The automotive industry is increasingly challenged to address environmental concerns, particularly the goal of achieving carbon neutrality (CN). Japan has committed to CN by 2050 and aims for 100% electric vehicle (EV) sales by 2035. A key factor influencing EV adoption is driving range, which is significantly affected by driving resistance—especially aerodynamic drag. Underbody covers (undercovers) are widely used to improve aerodynamic performance, but they are exposed to various environmental stresses, including water intrusion, stone chipping, and snow accumulation during winter driving. While previous studies have effectively simulated water and chipping stresses, research specifically targeting snow stress has been limited. This study proposes a novel simulation approach to evaluate snow stress on undercovers, focusing on slushy road conditions commonly found in Japan’s snowy regions. A particle-based method was employed to model the interaction between slush and underbody
Matsuura, Tadashi, Annen, Teruyuki, Harada, Takeyuki, Ueno, Shigeki, Asai, Mikio, Watanabe, Haruyuki
Elucidation of Flow Mechanisms Modulating Vehicle Wind Noise by using Large-Scale CFD Simulation2026-01-0614To be published on 04/07/2026
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, causes 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. However, 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
Tajima, Atsushi, Hirata, Takumi, Ikeda, Jun, Kamiwaki, Takahiro, Wakamatsu, Junichi, Tsubokura, Makoto
A Cooperative AFS/DYC Control for FWDIEV Based on Dissipative Energy Method2026-01-0619To be published on 04/07/2026
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, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia
An Adaptive Brake Force Allocation Strategy for Rear-Wheel Driven EVs2026-01-0636To be published on 04/07/2026
Due to changed requirements compared to conventional propulsion concepts, electromobility demands new and innovative strategies for energy-efficient vehicle motion control. For example, the challenge in purely rear-wheel drive (RWD) electric vehicles (EVs) is to achieve a maximum of regenerative braking power in order to increase energy recovery and to ensure, that this does not impair the braking stability. Within this conflict between energy efficiency and braking dynamics, it is necessary to design an intelligent strategy to optimise recuperation. This paper presents such a strategy, which improves an existing approach formerly presented by the authors, but specifically optimises it to overcome weaknesses. The previous approach had two major limitations: First, the efficiency map of the in-wheel machines (IWMs) was not considered. Second, there was no possibility of switching flexibly between different brake force distributions to guarantee both, maximized recovery potential and
Mitsching, Thomas, Heydrich, Marius, Ivanov, Valentin
Machine Learning-Based Gear Defect Detection in Electric Vehicle Drive Units Using End-of-Line Vibration Analysis2026-01-0637To be published on 04/07/2026
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, Anastasios, Michaloliakos, Anargyros
Backlash Evasion Strategy for Enhancing Vehicle Cornering Performance2026-01-0640To be published on 04/07/2026
This study presents a torque distribution control strategy for EVs with e4WD powertrain to overcome the trade-off between ensuring vehicle acceleration and deceleration responsiveness and mitigating backlash shock in the driving system. The deterioration of the drivability which occurs from the intrinsic hardware characteristics of the drivetrain is prevented by designing a response-priority drive mode in which neither front or rear motor torque is allowed to change its sign. Instead, in such drive mode, the front motor torque is only allowed to perform regenerative braking while the rear motor torque is only allowed to produce positive acceleration torque. In order to avoid sacrificing the maximum acceleration by applying such strategy, the mode transition function is implemented as well. In addition, in order to prevent backlash impact due to drivetrain compliance, variable offset torque based on drivetrain compliance model is evaluated in real time and applied to each motor command
Oh, JIWON, Lee, Ho Wook
Integrated Effects of Suspension Geometry and Tire Pattern Design on Vehicle Pull Reduction under Acceleration in High-Performance and Electric Vehicles2026-01-0593To be published on 04/07/2026
Torque steer is a phenomenon that is more prominently observed in high-performance vehicles and electric vehicles (EVs). This phenomenon occurs primarily due to asymmetric driveshaft angles, drivetrain structure, and suspension geometry. Additionally, tire characteristics, particularly lateral force (Tire Lateral Force), also play a significant role in affecting torque steer. Tire lateral force is strongly influenced by the contact patch dynamics and the geometric design of the tire tread pattern. This study focuses on analyzing the impact of tire tread pattern geometry on the relationship between torque steer and tire lateral force in such vehicles. Specifically, lateral force response variations (dfy/dfx) resulting from tread pattern block deformation were derived through finite element (FE) simulations. These simulations enabled the establishment of an analytical method for tread pattern evaluation and optimization. The developed characteristic parameter was validated by comparative
Yoon, Youngsam, Jang, Dongjin, Kim, Hyungjoo, Lee, Jaekil
Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications2026-01-0643To be published on 04/07/2026
Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy
Heydrich, Marius, Mitsching, Thomas, Ivanov, Valentin
Research on electro-hydraulic compound braking coordinated control strategy based on sliding mode control2026-01-0631To be published on 04/07/2026
Hydraulic braking torque and motor braking torque are the main sources of braking torque of new energy vehicles. Hydraulic braking converts vehicle kinetic energy into heat dissipation, and motor braking converts vehicle kinetic energy into electric energy to achieve energy recovery. In the process of vehicle braking, when the wheels tend to lock, it is easy to cause vehicle instability, which seriously threatens the safety of driving.Therefore, how to coordinated the braking torque of the two braking systems to ensure the vehicle braking safety and energy recovery efficiency is still an urgent problem to be solved. In this paper, the electric vehicle equipped with electro-hydraulic compound braking system is taken as the research object, and the electro-hydraulic compound braking coordinated control strategy considering the general braking state and emergency braking state is proposed. Firstly, a 3-DOF vehicle longitudinal dynamic model is established according to the vehicle dynamic
Zhao, Gaoming, Zhao, Bingquan, Zhao, Binggen, He, Chengkun, Zhang, Xiaoyang
Study on performance parameters of retarder in mountain condition of hybrid electric vehicle2026-01-0617To be published on 04/07/2026
In mountain driving scenarios, the continuous braking demands of vehicles pose significant challenges to the performance and reliability of braking systems. Traditional fuel-powered vehicles primarily rely on friction brakes for deceleration, which face issues such as high thermal fade risks and severe brake wear. While pure electric vehicles can utilize motor energy recovery to provide braking force and recover energy, their braking capacity is limited by the maximum regenerative torque of the motor. Moreover, when the battery State of Charge (SOC) is high, the energy recovery function may be restricted or disabled to protect the battery, leading to a sharp reduction or complete loss of braking torque—a safety hazard. Range-extended hybrid vehicles combine the advantages of both engine and electric drive systems. During long downhill operations, their motors can also serve as priority decelerators for efficient energy recovery. However, similar to pure electric vehicles, their motor
duan, xianqi, Tan, Gangfeng, Li, LiuYuanHeng, Tian, Tian, Yang, Yong
Look-ahead Information based Predictive Cruise Controller for Energy Efficient Powertrain Operation of Heavy-Duty Electric Trucks2026-01-0039To be published on 04/07/2026
Heavy-duty electric trucks represent a growing innovation in the transport and logistics sector, aiming to reduce emissions and reliance on fossil fuels. A major challenge with battery electric trucks is the long recharging time which takes significantly longer than refueling conventional diesel trucks. This limitation highlights the importance of optimizing powertrain operations to reduce energy losses and maximize efficiency. One effective approach is implementing optimal speed control through a predictive cruise controller. By anticipating road conditions, traffic, and elevation changes, the predictive cruise controller can adjust the truck’s speed in real time to minimize energy consumption, enhancing the range and reducing the need for frequent charging. Many problem formulations for electric trucks focus primarily on minimizing the energy required at the wheels, often overlooking the impact of powertrain efficiencies. This simplification neglects critical factors such as the
Safder, Ahmad Hussain, Villani, Manfredi, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Accelerating Electrification Through Modular Propulsion: GM’s Strategy for BEV and BET Platforms2026-01-0410To be published on 04/07/2026
General Motors 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, 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 rationale behind
Liu, Jinming, Sevel, Kris, Anwar, Mohammad, Oury, Andrew, Welchko, Brian, Gagas, Brent
A Dataset for Visual Classification of Battery Fire and Smoke2026-01-0569To be published on 04/07/2026
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, Vidarshana, Gunasekaran, Aswin, Challa, Karthikeya, Maxim, Bruce, Shen, Jie
Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture2026-01-0639To be published on 04/07/2026
The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded SmartCorners project addresses this challenge by developing integrated smart corner systems (SCS) that combine in-wheel motor propulsion, brake-by-wire, active suspension, and steer-by-wire functionality in a single module. These SCS are embedded within a highly adaptable skateboard chassis architecture, enabling scalable deployment across diverse vehicle platforms. The central approach of this paper is the utilization of artificial intelligence and machine learning to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and a user-centric vehicle architecture. The SmartCorners project targets to demonstrate significant advancements, including improvement in real-world driving range, reduction of component and system costs, and development time drop of the EV systems enabled by digital
Ratz, Florian, Armengaud, Eric, Formento, Cecilia, Moscone, Giulia, Sorrentino, Gennaro, Bisciaio, Giorgio, Sorniotti, Aldo, Amati, Nicola, Braun, Daniel, Deibler, Bernd, Boxberger, Valerius, Sottile, Salvatore, Ivanov, Valentin, Fuse, Hiroyuki, Kompara, Tomaž
Mitigation of Sensor-Level Cyber-Physical Attacks on Electric Vehicle Powertrains: A Simulation-Based Study2026-01-0091To be published on 04/07/2026
Electric vehicles (EVs) rely extensively on sensor feedback for safe and efficient powertrain operation. However, this dependency introduces cyber-physical vulnerabilities, especially when sensor signals are maliciously manipulated. This paper presents a simulation-based investigation into sensor-level cyberattacks on a mid-sized EV powertrain model developed in MATLAB/Simulink. The study quantifies mechanical consequences and evaluates mitigation strategies to enhance system resilience. Four representative attack scenarios were simulated. Speed sensor spoofing led the controller to misinterpret vehicle velocity, causing a 41% overshoot beyond the 50 km/h setpoint. False data injection into torque/current sensors triggered an unintended torque surge of approximately 20%, resulting in inverter current saturation within 2 seconds. Battery temperature spoofing delayed thermal protection, allowing a deviation of 1.5 °C/min beyond safe operating limits. A hybrid attack combining frozen
Tariq, Usama, Sahandabadi, Sahereh, Dianat, Ali
CFD Modelling of Cell Formation Gas and Venting During the EV’s Battery Manufacturing Process2026-01-0384To be published on 04/07/2026
The electric battery is a critical component of electric vehicles (EVs), where high power demands pose significant operational challenges. One such challenge is gas generation within the porous anode layer, which can lead to pressure buildup inside the battery. The complex interfacial dynamics at the microscale play a crucial role in determining the effectiveness of gas venting and the resulting pressure evolution. This study addresses this issue through a two-pronged simulation approach. First, gas generation within a representative anode microstructure is investigated using a Volume of Fluid (VOF) framework that resolves tortuous flow passages. The simulations reveal that gas generation in such microstructures can lead to pressure rises of several thousand Pascals, with interfacial behavior primarily governed by surface tension effects. Second, a high-level battery pack simulation is performed using a porous media approach to evaluate system-scale gas venting and localized
Mahyawansi, Pratik J., Schlautman, Jeff, Viswanath, Priyanka, Srinivasan, Chiranth
A Scalable and Accessible Remote Testing Framework for Automotive Development2026-01-0191To be published on 04/07/2026
With the rise of software-defined vehicles and the emergence of cyber threats to vehicular systems, developing teams are compelled to conduct extensive testing on both virtual and physical prototypes at an accelerated pace. This new development landscape necessitates diagnostic tools that are both precise and adaptable. However, proprietary systems dominate this field, often hindering accessibility for students and researchers due to high costs and restrictive licensing. This paper presents the design and implementation of an open-source, low-cost remote testing system tailored for automotive development and diagnostics. The proposed system utilizes Arduino and Raspberry Pi processing units, along with relay-based switching modules, to provide secure remote control of vehicle components through a web-based dashboard equipped with authentication, scheduling, and real-time synchronization capabilities. The tested prototype showcased robust scalability, secure session handling, and
Pries, Andrew, Mohammad, Utayba
Impact of C-rates on Electrical Behavior of Li-ion Batteries under Coupled Temperature-load Conditions2026-01-0383To be published on 04/07/2026
The utilization of lithium-ion batteries in electric vehicles becomes increasingly common. Battery performance degradation is influenced by both temperature and boundary constraints. Monitoring the usable cell capacity (UCC) under real-world operating conditions is essential for ensuring accurate state-of-health estimation. 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 charge/discharge rates frequently results in deviations between laboratory characterization and on-board BMS capacity estimation. To investigate the impact of C-rates under coupled temperature-load conditions, we conducted long-term cycling tests on LFP/graphite pouch cells at 25 °C and 45 °C under different constrained conditions. The UCC was evaluated at different test rates (1/20C, 1/3C, and 1C) as an indicator of aging. The results show that
ZHANG, Shan, Niu, Zhice, Xia, Yong
Innovation Powered by High-Fidelity Multiphysics Modeling: A Holistic System Optimization of BorgWarner’s Next Generation Integrated Drive Modules2026-01-0497To be published on 04/07/2026
In the stringent market of BEV, the development of integrated Drive Modules (iDM) fitting to environmental and customer needs is mandatory. It is important to extract the best from the less. To achieve those goals, a deep insight of 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 PhD, Arnaud, Bossi, Adrien, Bourniche, Eric, DAVID, Pascal, Nanjundaswamy, Harsha
Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation2026-01-0392To be published on 04/07/2026
Electric Vehicles (EV) have become a major focus in the automotive industry. This paper introduces a propulsion system design, which supports the Wide Torque Band (WTB) concept to boost the power density of PM (permanent magnet) motors in EV Trucks resulting in performance, efficiency, and cost benefits. The WTB drive requires an 800V battery for vehicle operation. The chosen solution combines two 400V battery packs to create a selectable 400V/800V DC bus. The electrical hardware and controls to manage the dual battery packs in either series (800V) or parallel (400V) mode are presented in this paper. A prototype battery management system (BMS) along with existing production Voltage, Current, Temperature Monitoring (VITM) hardware are applied to perform mode switching, safety, and cell balancing. The success of this dual pack hardware enables high voltage dynamometer testing of a new 800V DU (Drive Unit) and inverters for EVs. The flexibility of switching between two voltage levels (400
Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra
An Image-Derived Multimodal Framework for Automotive Tire Exterior Noise Prediction2026-01-0220To be published on 04/07/2026
This study presents an image-derived multimodal AI framework for early-stage tire noise evaluation. The proposed model requires only multi-angle photographs captured by a standard smartphone and basic tire specifications. From these images, scaled three-dimensional (3D) meshes and fixed-view depth maps are reconstructed and combined with numerical parameters within a neural network architecture. Three input branches—a point-cloud–gradient branch, a depth-map convolutional neural network (CNN) branch, and a specification multi-layer perceptron (MLP) branch—are jointly trained using a composite loss that integrates frequency-weighted mean squared error (MSE), spectral cosine similarity, FFT-domain consistency, and A-weighted sound-level terms. A dataset of 28 tires, spanning passenger, SUV, and pickup applications for both battery electric vehicles (BEVs) and internal combustion engine (ICE) vehicles, was evaluated using leave-one-out (LOO) cross-validation. The model achieved a mean
Shao, Guangxin, Shopoff, Scott, Franklin, Nicholas
Items per page:
1 – 50 of 5033