Browse Topic: Electric vehicles

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Prognostic Thermal Modeling and Control of a Carbon–Ceramic Brake Disc for Electric Sports Cars under Racetrack Conditions14-15-03-002010/1/2026
Carbon–ceramic brake discs in high-performance electric sports cars are vulnerable to heat fade under racetrack conditions, where repeated high-speed braking can raise disc temperature above the material’s safe limit of 1200°C. Three-dimensional finite-volume analysis is accurate but inefficient for long transient track events. To improve efficiency, a one-dimensional lumped capacitance method (LCM) is proposed to predict brake disc temperature evolution. A speed-dependent cooling coefficient links disc thermal response to vehicle operating conditions. The model is validated against wheel-end temperature measurements of sports cars on the Zhuzhou International Circuit and Nürburgring Nordschleife Circuit. It is then used to assess three thermal control measures: an external air director, increased disc thermal mass, and higher regenerative braking contribution. The model reproduces the measured trend with acceptable error and predicts that the baseline disc temperature can peak at 1445°C in a four-lap Zhuzhou scenario and 1540°C in a Nürburgring scenario. The air director provides substantial cooling but is insufficient on its own. A system-level safe temperature of 1050°C is achieved only when the disc size is increased to 410 mm × 40 mm and regenerative braking deceleration is raised to at least 0.1 g in combination with the air director scheme. The proposed LCM provides a practical and computationally efficient tool for early-stage brake thermal design of sports cars.
Fan, Yang, Huang, Longsheng, Shao, Xingyang, Huang, Taishuo, Liao, Yinsheng
Integrating MBSE with High-Fidelity Crash Simulation2026-01-75429/22/2026
Modern electrified ground vehicles introduce complex, multi-domain safety requirements, such as post-crash thermal runaway prevention, that expose the traceability limitations of Document-Based Systems Engineering (DBSE). This paper proposes a four-layer, bidirectional digital thread architecture that integrates Model-Based Systems Engineering (MBSE) with high-fidelity, non-linear Computer-Aided Engineering (CAE) crash simulations. Leveraging SysML, System-Theoretic Process Analysis (STPA), and Python-based orchestration middleware, the framework automates the translation of descriptive safety requirements into explicit finite element boundary conditions. The architecture programmatically extracts key performance indicators from massive binary solver outputs and injects them back into the SysML environment for automated compliance verification. Demonstrated through a simplified electric vehicle side-pole impact case study utilizing LS-DYNA and a 1D thermal model, the framework successfully eliminates manual data handoffs, accelerates multidisciplinary design optimization, and ensures robust, risk-driven requirement traceability across the engineering lifecycle.
Rye, Patrick J.
Electrical and Power Quality Performance Evaluation of a SiC Based 500kW High Temperature and High Power-Density Inverter2026-01-75689/22/2026
In pursuit of future high-power capabilities for U.S. military ground vehicles, the transition towards vehicle electrification has been heavily adopted. High power-density and high temperature inverters play a key role in progressing vehicle electrification adoption across the U.S. military. This paper presents experimental results to evaluate the power quality performance of the developed high power-density and high temperature inverter, Enercycle™ DC-1000 Inverter based on silicon carbide (SiC). The DC-1000 inverter is a bi-directional inverter with a power density of 11.4 kW/L, which is capable of operating at 600 Vdc and delivering 500kW continuous output power and transient output power up to 640 kW, enable ground vehicle electrification. The experimental results to evaluate the power quality aspects such as distortion factor, ac voltage ripple, and voltage transient due to step load are presented in this paper. Moreover, challenges and next steps for further improvement of design have been discussed. Citation: A. Sadigh, Iris Shiroma “Electrical and Power Quality Performance Evaluation of a SiC Based 500kW High Temperature and High Power-Density Inverter,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Sadigh, Arash, Shiroma, Iris
SAE TOMORROW TODAY - Lessons From a 7,000-Mile EV Road Trip135859/22/2026
What does it take to drive an EV across America and back again? A 7,000-mile road trip is a great way to find out. Listen in as we sit down with science-comedian Sophie Shrand, Founder, Cool Jacket Productions and award-winning TV Host of Science with Sophie, to discuss The Great American ELECTRIC Road Trip, her exciting journey through 25 states in a Rivian R1S that puts EV infrastructure to the test and leads to unexpected discoveries along the way. From navigating EV charging networks to uncovering surprising economic opportunities, Shrand proves that curiosity and a sense of adventure can create meaningful connections with people from across the country. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Research on Electro-Hydraulic Composite Braking Control Strategy for Four-Axle Electric-Drive Special Vehicles02-20-01-00059/22/2026
Four-axle electric-drive special vehicles are often deployed to execute complex tasks under extreme operating conditions. Their harsh working environments, stringent dynamic-response requirements, and high energy demand for mobility impose higher requirements on both the energy-regeneration efficiency and braking safety of the braking system. To address these issues, this article proposes an electro-hydraulic composite braking control strategy for four-axle electric-drive special vehicles. First, a braking axle-load transfer model for the four-axle electric-drive special vehicle is established, enabling axle-to-axle braking force distribution based on axle loads. Second, considering the motor torque characteristics and battery charging characteristics and by establishing an anti-lock braking prediction model, an electro-hydraulic composite braking torque allocation strategy with safety-range constraints on motor braking torque is designed. Then, to enhance vehicle safety during emergency braking, an adaptive super-twisting sliding-mode variable-structure anti-lock braking system (ABS) controller is proposed, and based on this ABS controller, an emergency braking control strategy is developed in which the motor braking torque is pre-reduced to within a safe range, with motor-dominant and hydraulic auxiliary modulation. Finally, simulation and real-vehicle tests demonstrate that the proposed electro-hydraulic composite braking control strategy improves the motor braking energy recovery rate under service braking conditions, reduces the braking distance, and enhances braking safety under emergency braking conditions.
Jin, Liqiang, Peng, Jinxin, Ke, Yuan, Peng, Silun, Li, Jianhua, Xiao, Feng
Data-Driven System Dynamics and Chauvenet-Based Outlier Detection for Assessing Global CO₂ Emissions under Fossil Energy and Electric Vehicle Transition Scenarios2026-24-00209/21/2026
Understanding the structural drivers of global CO₂ emissions requires integrated analysis of fossil fuel production, total energy consumption, and electric vehicle (EV) deployment trends. This study presents a data-driven modeling framework combining system-dynamics formulation with statistical outlier detection implemented in Python to evaluate emission trajectories over a ten-year historical period. The methodology incorporates historical datasets of global CO₂ emissions, primary energy consumption, fossil fuel production, and EV manufacturing volumes. A computational routine developed in Python applies the criterion proposed by William Chauvenet to identify statistically inconsistent observations within the dataset, ensuring robustness prior to regression and correlation analyses. Carbon intensity (CO₂ per unit of energy) is calculated to assess decoupling behavior, while correlation matrices and elasticity indicators quantify the relative influence of fossil production and EV penetration on emissions. The dynamic structure expresses CO₂ emissions as a function of fossil energy share, total energy demand growth, and electrification rate. Sensitivity analysis evaluates the responsiveness of emissions to variations in these parameters. Results indicate that emission reductions are strongly dependent on carbon intensity evolution rather than EV growth alone. Outlier detection enhances model reliability by preventing anomalous years from biasing trend interpretation. The proposed framework provides a transparent and computationally efficient tool for emission diagnostics, transition scenario evaluation, and policy-oriented forecasting within the context of sustainable mobility and global energy transformation.
Gutierrez, Marcos, Taco, Diana
A Computational Study of High Efficiency Wankel Rotary Engines using a Passive Pre-Chamber2026-24-00019/21/2026
Wankel rotary engines are renowned as compact machines with high power-to-weight ratios, which make them suitable for use as range extenders for battery electric vehicles or as propulsion systems for unmanned aerial vehicles. However, their overall efficiency and emissions still need significant improvement to meet to the stringent regulations comparable with classical reciprocating 4-stroke engines. With the aim of improving these shortcomings, this work focuses on the application of a passive pre-chamber in order to enhance the combustion phase and the overall efficiency and emissions of such engines. Computational fluid dynamics (CFD) simulations were conducted for the commercial AIE 225CS rotary engine, configured with port fuel injection and fully-premixed gasoline combustion. The engine was extensively tested in a previous project while different CFD models were validated against experimental data in previous studies by the same authors. In particular, the present work examines the engine performance with two pre-chamber configurations with different volumes. The volume and nozzle specifications were determined to have geometrical characteristics similar to those of the theory of Gussak, with volumes directly comparable with that of the two spark park plug recesses of the original engine, leading to significantly large nozzle diameters in the pre-chambers. In addition, the effect of spark advance was investigated to capture the development of the flame and jets and the resulting effects on the indicated pressure cycle. Consistent with previous findings, heat losses were found to be a critical aspect for the different configurations of engine. Nevertheless, the application of pre-chamber shows some potential to improve efficiency by accelerating combustion phase, leading to a relative increase of 7.4% on the indicated efficiency. This suggests an important new path in the development of Wankel engines as a viable solution to efficient utilisation of decarbonised and innovative future fuels in compact systems.
Vorraro, Giovanni, Im, Hong G., Turner, James
EKF-Based Machine Learning Approach for Battery SOH Prediction in Commercial Vehicles Fleet2026-24-00029/21/2026
Estimating battery state of health (SOH) from field data is essential to ensure successful operation and increase the uptime of battery electric vehicles (BEVs). Most studies in the literature propose methods relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, were collected over approximately 20 months of operation from 10 electric commercial vehicles. During this period, a maximum battery degradation of 4% is observed within the fleet. Firstly, a model-based framework was introduced, in which a second-order battery equivalent circuit model (ECM) was coupled with an extended Kalman filter (EKF) to estimate the battery SOH. Results confirmed that the EKF is able to accurately capture the battery's physical behavior and degradation trend, yielding a maximum root mean square error (RMSE) of 1.23% when compared with the SOH signal provided by the onboard BMS. However, a Kalman filter requires accurate model parameter identification and high-frequency measurement data, leading to increased computational costs. To bridge these gaps, this paper utilizes the SOH estimates obtained from the EKF to train and validate a feedforward neural network (FNN) model, specifically designed to operate on aggregated metrics. The FNN model can provide accurate SOH estimates, with a RMSE as low as 0.26% during the testing phase. The approach proposed in this work combines the interpretability of model-based methods with the scalability and reduced data dimensionality of machine learning (ML) ones, making it more suitable for monitoring battery SOH in large fleets of BEVs.
D'Agostino, Valerio, Pulvirenti, Luca, Shanker, Anirudh, Cardone, Massimo, Rizzoni, Giorgio, Vitale, Francesco
Comparative Analysis of BEV Performance from Simulated Environment and Physical Hardware Platform2026-24-00059/21/2026
The proliferation of simulation environments has accelerated technological progress across various scientific domains by offering a cost-effective and time-efficient framework for data acquisition and analysis. In the automotive sector, high-fidelity modelling of vehicle components and driving scenarios bypasses the logistical constraints associated with hardware procurement and the intensive requirements of large-scale testing infrastructures. However, pre-calibrated or native software models often imply simplified hypotheses, missing relevant aspects of the entire powertrain-to-wheel energy chain. This study presents a comparative analysis of battery performance within a battery electric vehicle (BEV) by synchronizing virtual simulations with experimental hardware at the test bench. The methodology involves the concurrent modelling of the driving environment, the vehicle chassis, and the propulsion system, followed by the execution of identical driving cycles on a physical platform. The experimental setup comprises a fully instrumented BEV featuring an integrated electric motor and battery pack, specifically configured for high-precision signal acquisition. The virtual section starts with the development of a digital twin within a commercial simulation suite, parameterized according to the vehicle specific dynamic and energy requirements. This is followed by the integration of the electric propulsion system and a battery pack model based on the equivalent circuit model method. To ensure high fidelity, the battery model is experimentally calibrated via multi-step pulse discharge tests performed on the physical hardware. Subsequently, various driving scenarios from the simulated environment are translated into speed-time profiles and are replicated on the real vehicle using a PID-controlled actuator on the accelerator pedal. The battery pack that serves the vehicle is monitored during the cycle to collect information on the electrical performance. Finally, a comparison between the simulated and real battery behaviour is performed. This dual approach used in the present work, which compares the simulation accuracy against real-world performance, provides critical insights into the inherent advantages and technical boundaries of digital modelling in electromobility applications.
Sequino, Luigi, Sementa, Paolo, Altieri, Nunzio, Vaglieco, Bianca Maria, Sorrentino, Chiara
Computational Electro-Thermal and Degradation Modeling Framework for Lithium-Ion Batteries in Electric Vehicles2026-24-00119/21/2026
This paper presents an integrated computational framework that couples electro-thermal and degradation dynamics for lithium-ion batteries used in electric vehicles (EVs). The model is implemented using Python. At the cell level, the model describes charge and discharge behavior, state of charge (SOC), terminal voltage, internal resistance losses, and heat generation. An energy balance equation is used to estimate temperature variation during operation. Temperature-dependent resistance and capacity are included to represent nonlinear battery behavior under different load conditions. At the system level, feedback relationships between SOC, temperature, state of health (SOH), and degradation rate are modeled using system dynamics. Battery aging is represented through mathematical functions that relate capacity loss to temperature and usage cycles. This allows simulation of long-term performance under different driving scenarios. The model enables parametric and sensitivity analyses to evaluate the effects of discharge rate, ambient temperature, and degradation parameters. Results show the strong interaction between thermal behavior and battery aging. Model validation against experimental data is not performed in this study and remains an important direction for future research. The current work focuses on the derivation and parametric analysis of the modelling framework. The proposed framework provides a clear, low-cost, and scalable computational approach for EV battery analysis, design studies, and engineering education.
Gutierrez, Marcos, Taco, Diana
System Dynamics Modeling of Global Electric Vehicle Adoption: Market Transition Time Toward a Single-Dominant Transport Technology2026-24-00179/21/2026
This study presents a system dynamics framework to estimate the global transition time toward electric vehicle (EV) dominance. The model, adapted from the 'Growth of a Field' archetype, captures the mutual reinforcement between EV adoption, charging infrastructure deployment, and cost reductions via learning curves. By solving a system of differential equations in Python, we simulate the nonlinear feedbacks that drive technological diffusion within a finite market. The model explicitly represents the dynamics of the EV fleet, charging infrastructure stock, and cumulative production, where adoption is influenced by infrastructure availability and declining battery costs. Sensitivity analysis reveals how variations in the base adoption rate—representing early policy and behavioral factors—affect tipping points. For instance, doubling the initial adoption propensity reduces the time to 50% market penetration from 30 to 20 years. Monte Carlo simulations are incorporated to assess probabilistic forecasts and the robustness of transition timelines under uncertainty. The results highlight infrastructure deployment as a critical bottleneck and quantify the leverage of early incentives. This framework provides a transparent, extensible tool for strategic planning in the automotive and energy sectors.
Gutierrez, Marcos, Taco, Diana
Life Cycle Assessment of Greenhouse Gas Emissions from a Plug-in Hybrid Electric Vehicle with Different Powertrain Configurations in Europe2026-24-00199/21/2026
Plug in hybrid electric vehicles play an important role in transportation decarbonization. Compared with battery electric vehicles, plug in hybrid electric vehicles generally have a lower production carbon footprint due to their smaller batteries, which require far less raw material. Despite their smaller capacity, these batteries are typically sufficient to cover most daily travel distances in pure electric mode. The hybrid powertrain can be configured in multiple ways depending on the number and position of electric machines within the driveline. These configuration differences significantly influence both the total carbon footprint and the use phase greenhouse gas emissions. In this study, we evaluate the life cycle greenhouse gas emissions of a plug-in hybrid electric vehicle with various powertrain configurations in the European context. All configurations share the same premium mid-size sport utility vehicle glider. Battery capacity ranges from 20 kWh to 45 kWh, enabling an electric range of over 200 km under the Worldwide Harmonized Light Vehicles Test Cycle. The number of electric machines varies from one, as in the P2 configuration, to three, as in the P1+P3+P4 configuration. Use phase emissions for each configuration were estimated in accordance with the latest European Union emission legislation. The P2 powertrain exhibited the lowest weighted fuel and electricity consumption, whereas the P1+P3+P4 layout demonstrated the highest overall electric and fuel consumption. A sensitivity analysis of use phase emissions was performed, followed by projections for scenarios with increased renewable energy shares in both electricity generation and liquid fuel production. Finally, an extreme scenario assuming 100 % renewable electricity and fuel was analyzed.
Nguyen, Duc-Khanh, Andersson, Simon, Kristoffersson, Annika
Cost and Emission Aware Strategic Optimization: A Fleet-Level Tool to Support Scenario-Oriented Design2026-24-00349/21/2026
The transition toward low-emission transport systems requires not only technologically optimized Battery Electric Vehicles (BEVs) but also integrated methodologies capable of supporting industrial stakeholders throughout the deployment phase. In particular, for logistics operators, fleet sizing and charging infrastructure planning are tightly coupled with vehicle configuration and mission scheduling. Therefore, decision-support tools are required to minimize total operational costs and environmental impact while ensuring service continuity. Building upon a previously developed two-level BEV design framework, this work introduces a higher-level optimization tool aimed at extending powertrain design outcomes toward fleet-level decision-making, providing an integrated methodology capable of determining not only the optimal vehicle configuration but also the optimal number of vehicles and charging stations required to satisfy operational scheduling constraints. The proposed tool performs fleet charging management optimization under customizable objective functions. Two BEV configurations, equipped respectively with 7 and 10 battery packs, are selected as candidate solutions from the upstream two-level design framework. Starting from these configurations, the tool simultaneously optimizes fleet size, charging infrastructure dimensioning, and charging scheduling strategy. In the first case study, the objective is the minimization of fleet operational costs, primarily associated with charging energy, while introducing a tunable penalty factor on mission time-shifting for schedule flexibility. In the second case study, a CO2-based term is incorporated into the objective function through an equivalent emission cost. By varying its weighting factor, the analysis quantifies how environmental prioritization influences the optimal fleet and infrastructure configuration. Across all the examined scenarios, the optimal fleet size consistently converges to 3 vehicles with a single 50 kW DC charging station. The key difference between cost-driven and environmentally-oriented optimization lies in the battery configuration: in the cost-driven scenario, the 10-packs configuration achieves the lowest Total Cost of Ownership (1134 EUR/week), as its larger energy buffer reduces weekly grid energy demand and thus charging costs. Conversely, under CO₂-prioritized optimization, the optimal configuration shifts to the 7-packs, yielding a lower TCO of 1008 EUR/week and a 15% reduction in CO₂ emissions (127 vs 149 kgCO2/week). The proposed fleet-level optimization framework represents a scalable extension of the vehicle design methodology, enabling logistics companies to support electrification strategies through data-driven, application-specific, and sustainability-oriented decision-making.
Bartolucci, Lorenzo, Cennamo, Edoardo, Cordiner, Stefano, Donnini, Marco, Grattarola, Federico, Lombardi, Simone, Mulone, Vincenzo, Tribioli, Laura
Business Approach to Cope with Automotive Market Uncertainty2026-24-00249/21/2026
The global automotive industry is facing an unprecedented convergence of uncertainties driven by geopolitical tensions, evolving trade policies, emissions related regulations, and increasingly volatile consumer demand. Shifting emissions legislation, including the EU’s tightened CO2 targets and long-term plans to phase out internal combustion engines, is imposing strategic and financial pressures on automakers and suppliers as they navigate divergent regional regulatory trajectories. Demand side volatility further complicates the landscape. Consumer preferences are fluctuating due to economic pressures, infrastructure constraints, and uneven EV adoption patterns. While some markets show stagnation in battery electric vehicle uptake, hybrids are rising as consumers seek cost efficient alternatives amid uncertain energy and regulatory environments. Within this unstable context, the transition toward Software Defined Vehicles (SDVs) is emerging as a critical strategic response. SDVs, characterized by centralized computing, updatable software architectures, and over the air feature deployment, offer automakers greater adaptability in addressing regulatory shifts and market dynamics. By decoupling hardware from software cycles, SDVs enable faster innovation, reduced development risk, and new digital revenue models, while virtualization and AI driven analytics enhance development efficiency and lifecycle value.
Cavanna, Filippo, Potenza, Luca
Short-Term Load Forecasting of Sustainable Power Systems Based on Newton–Raphson-Based Optimizer–Extreme Gradient Boosting under Extreme Weather Events13-07-02-00089/17/2026
With continuous advancements in load-side resources such as distributed photovoltaic systems, electric vehicles, and virtual power plants, the low-carbon and sustainable development attributes of power systems have been significantly enhanced. Meanwhile, the coupling intensity between sustainable power systems and meteorological conditions has been further consolidated. Considerable impacts are exerted by weather variations, particularly extreme weather events, on the dispatching and operation of sustainable power systems. Accurate load forecasting is critical for enabling sustainable power systems operators to optimize power generation strategy, ensuring supply stability and resilience against extreme weather-induced disruptions. However, the intrinsic non-stationarity and volatility of extreme weather events present significant challenges to conventional forecasting approaches. Herein, we introduce a hybrid algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme gradient boosting (XGBoost) to enhance short-term load predictions under such conditions. The model uses optimally selected meteorological and load features as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to maximize performance. Evaluated on an Irish dataset, the proposed framework is quantitatively compared against five baseline models, including traditional decision trees and neural networks. The case studies show that the mean absolute percentage error (MAPE) of the proposed model is 2.57%, which is the lowest among these decision tree and neural network algorithms.
Wang, Yi, Zhou, Jian, Wu, Gang, Ma, Tiannan, Ma, Ruiguang, He, Chuan, Zhu, Huixian
Use-Phase Emissions Comparison of Battery Electric and Internal Combustion Engine Vehicles in Canada2026-01-50749/17/2026
The objective of this study was to evaluate the in-use emissions and energy consumption of similar model internal combustion engine (ICE) and battery electric vehicles (BEVs) in Canada. For the ICE vehicles (ICEVs), carbon dioxide (CO2) emissions were measured at the tailpipe. For the BEVs, the carbon intensity of different energy sources was used along with vehicle energy consumption to estimate the in-use CO2 equivalent (CO2e) emissions. Three ICEVs, the Ford Transit, Ford F-150, and Nissan Versa, and three BEVs, the Ford E-Transit, Ford F-150 Lightning, and Nissan LEAF, were tested over standard test cycles on a chassis dynamometer. The Nissan Versa, Nissan LEAF, Ford F-150, and Ford F-150 Lightning were tested at two temperatures, 25°C and −7°C, to investigate the effect of colder temperatures on emissions and energy consumption. The Ford Transit 150 and E-Transit were tested at two test weights, 2722 kg (6000 lb) and 3629 kg (8000 lb), to study the effects of cargo loading on emissions and energy consumption. In most conditions, the BEV use-phase CO2e emissions were found to be lower than those of the ICEVs. Results showed a significant increase in both emissions in ICEVs (up to 20%) and energy consumption in BEVs (up to 78.5%) at −7°C when compared to 25°C. Results also showed the significant effect of the carbon intensity of electricity on the CO2e emissions of BEVs, where more carbon-intensive electricity grids resulted in higher BEV CO2e emissions, even surpassing ICEV CO2 emissions in certain cold-temperature conditions.
Araji, Fadi, Humphries, Kieran, Hornung, Jeremy, Shantz, Emory
Development and Production of Weight-Reduced Steel Hat Brake Discs: A Contribution to Automotive Lightweighting2026-01-08219/14/2026
The automotive industry's transition towards electrification, particularly in the passenger car (PC) and light commercial vehicle (LCV) segments, has intensified the focus on vehicle lightweighting to maximize battery range and efficiency. Conventional brake systems in electric vehicles (EVs) are subject to minimal mechanical wear due to regenerative braking, making corrosion the primary cause of component failure and replacement. This paper details the development and production of an innovative lightweight brake, which addresses these challenges. The "Cast-In" brake disc combines a traditional gray cast iron friction ring with a pre-finished, deep-drawn steel hat through a specialized composite casting process. This design achieves a significant reduction in unsprung mass—1.6 kg per disc in a 390mm x 36mm example—directly contributing to improved vehicle dynamics and energy efficiency. Key manufacturing challenges, including ensuring a robust material bond, preventing casting defects, and sealing the steel hat during casting, have been overcome through advanced process controls, simulation, and a patented sealing system. Furthermore, a novel, enhanced corrosion protection system has been developed and validated to meet the required service life of over 10 years, addressing the specific demands of e-mobility. With production scheduled to begin in April 2026, this technology is a milestone for modern braking solutions in the era of electrification.
von Reth, Thomas
A Study on the Development of Brake Moan Analysis for CTBA Vehicles Using Simulation2026-01-08149/14/2026
Moan noise is a low-frequency noise occurring in the 170–500 Hz frequency ranges. While it frequently appears in vehicles equipped with a rear Coupled Torsion Beam Axle (CTBA), the exact cause, generation mechanism and clear solutions remain unidentified. For those reasons, we have developed a moan noise analysis method capable of representing the moan noise phenomenon in vehicles with rear CTBA along with an automation tool. From these results, we can use moan analysis models to reduce real moan noise problems. Consequently, this not only enhances customer satisfaction and vehicle quality but also significantly increases the work efficiency of vehicle designers through design modification in the preliminary stages of vehicle development
Kim, Sungho, Kim, Jeongkyu, Hwang, Jaekeun, Kang, Donghoon
Predicting Temporal Friction Torque of Drum Brake Systems: A Mixed-Effects Random Forest Approach2026-01-08099/14/2026
Drum brake systems are becoming increasingly important in electric vehicles (EV) and purpose-built vehicles due to cost competitiveness and EURO-7 particulate emission regulations. Despite this trend, drum brake friction behavior remains incompletely characterized due to its dependence on multiple coupled variables: temperature history, braking conditions, and component interactions. To address this gap, this study presents a method for developing a time-series friction torque prediction model using the Mixed-effects Random Forest (MERF) machine learning framework. Time-series data collected from sensors during drum brake dynamometer tests were analyzed to identify the key variables that govern the friction torque. Significant inputs were selected through Exploratory Data Analysis (EDA), considering test-to-test variability and potential mixed effects, and were then used to train and tune the MERF model. Model performance was evaluated by comparing predicted friction torque with measured torque, and prediction error was quantified by using Mean Absolute Error (MAE) to check whether predicted model is reliable. The proposed prediction model demonstrates a high level of agreement with experimental measurements, confirming that the MERF approach can effectively capture the non-linear and transient characteristics of drum brake friction torque from time-series sensor signals. These results indicate that friction torque estimation is feasible using only sensor signals already available from conventional test instrumentation, without additional dedicated sensors. This capability is expected to support broader applications, including brake performance prediction for vehicles equipped with drum brakes and enhanced simulation of drum brake thermal performance across operating conditions.
Yoon, Jungro, Cho, Sunghyun, Kim, Wonjoon
A Study on TSC (Technical Safety Concept) Design and Verification in EMB (Brake by Wire) Systems2026-01-08019/14/2026
The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Kim, Dokun
Design Considerations for Optimizing the Wheel Bearing Outer Ring Bolt-Mounting Pattern for Reduced Drag2026-01-08309/14/2026
Electric vehicles (EVs) impose more demanding operating conditions on wheel bearing systems due to increased vehicle mass, higher drive torque, and the need to maximize energy efficiency and driving range. These factors elevate the loads transmitted through the bearing to knuckle joint and often require higher clamp loads to ensure joint integrity. However, higher clamp loads amplify distortion of the wheel bearing outer ring, increasing rotational drag and reducing bearing durability. Controlling outer ring distortion is therefore critical for EV wheel bearing design, as well as for high performance vehicles that experience severe lateral loads at the hub to knuckle interface. This paper investigates key design considerations for optimizing the wheel bearing outer ring and its mounting interface to minimize distortion under elevated clamp loads. A comprehensive CAE-based Design of Experiments (DOE) is used to evaluate the influence of multiple bolt-mounting patterns including rectangular, square, and trapezoidal configurations and the relative alignment of the bolt pattern between the outer ring and knuckle. The study also compares the performance of M12 and M14 fastener variants across loading conditions representative of EV and high-performance applications. The results identify geometric and interface design parameters that significantly reduce outer ring out of roundness, thereby lowering drag torque and improving long-term bearing life.
Mandhadi, Chaitanya Reddy, Lee, Seungpyo, Bovee, Benjamin, Callaghan, Kevin
Study on Temperature Rise and Heat Dissipation of Micro Electric Vehicle Drive Motor Based on Multi-Physics Coupling2026-99-11319/11/2026
This paper is mainly about heat dissipation and improvement technology, aiming at solving the problem that the driving motor of 4 low-speed electric vehicles has too high a rising speed when running under complicated working conditions. Core losses and conductor losses are calculated by using the finite element method as well as known empirical formulas, and magnetic eddy loss at three operating conditions (normal condition, maximum speed, and peak torque. The computed heat loads are then used to establish the complete 3- dimensional model of thermal analysis. As shown in Figure 3, without active cooling, the hottest conductors would be at about 141°C (Class-B Insulation limit). Therefore, three different liquid cooling designs (helical channel, serpent channel, and annular flow) have been designed and analyzed. A coupled thermal- fluid simulation has been performed on the helix structure, which showed better results, so we selected it to be optimized. The optimization is performed using central composite design, genetic algorithms for response surfaces, and multi-objective evolutionary optimization (NSGA-II). The objective function is to simultaneously achieve a low maximum working temperature as well as good heat transfer properties by changing the geometry of cooling channels. The cooling of the motor as well as the suction pressure was optimized, after which a decrease by up to 31.1°C (up 8.6% on the maximum temperature) was observed. Also, pressure drop reduction as much as 23% along with substantial increases in both the heat transfer performance and reliability.
Xi, Heyuan, Bai, Enjun, Ma, Wenyu, Tang, Fuyu, Liu, Zhiyi
Linkages and Propagation Mechanisms of Risk Sources during Electric Vehicles Transportation2026-99-15289/11/2026
This study addresses safety issues in three representative logistics scenarios for electric vehicles (EVs) as cargo-car carriers, roll-on/roll-off (Ro-Ro) vessels, and containers. To address the heterogeneity across these modes, we develop an integrated “process–spatiotemporal load–risk factor” framework that embeds operational steps and confinement conditions into the indicator system, overcoming the limitations of single-scenario or single-factor studies in explaining chain-type propagation. Building on process mapping and spatiotemporal load characteristics, we develop a risk indicator system spanning “person-equipment-transported object-operation & environment-system management.” Expert judgments are then analyzed using an integrated DEMATEL-ISM approach to quantify inter-factor linkages and transmission pathways. The results indicate that regulatory oversight and carrier-side emergency equipment constitute the deep root causes of thermal runaway. The most hazardous transmission route is “regulatory oversight, procedural compliance and skill-experience match”, while “battery type, road/sea conditions and hoisting impacts” forms the shortest path. These findings reveal weaknesses in management and equipment that are amplified by operational execution and limited personnel capability, ultimately precipitating severe transportation incidents.
Yuan, Libo, Jiang, Huifu, Qin, Xiao
3D Dynamic Object Detection with Multi-Modal Temporal Fusion2026-99-11179/11/2026
This research aims to address the critical challenge of accurately detecting and estimating the state of dynamic objects in autonomous driving. Traditional 3D object detection methods often struggle with motion perception, particularly in velocity estimation, due to the lack of information in single frame perception. We propose a novel framework that enhances the BEV representation with temporal modeling. The core of our method is a two-stage temporal fusion process. First, we align historical BEV features to the current coordinate frame to eliminate the interference of ego-motion. Subsequently, a dedicated temporal fusion encoder, architected with residual connections and a Feature Pyramid Network, refines the aligned multi-frame BEV features to capture complex motion patterns and improve multi-scale object representation. This approach directly tackles the problem of motion decoupling. By aligning features, we disentangle object motion from ego-motion. The temporal fusion encoder then mitigates the positional ambiguity of moving objects in the fused BEV space, a common issue in simple feature concatenation, leading to more robust detection. We built a dataset following the structure of the nuScenes dataset, using data collected from an autonomous driving simulation platform. The evaluation results on our simulation dataset demonstrate that the proposed temporal module achieves a 13.0% improvement in NDS score and a substantial 29.7% reduction in velocity error (mAVE). These results demonstrate that our temporal fusion strategy effectively enhances 3D detection accuracy in dynamic scenarios.
Shao, Mengjia, Li, Wei, Bai, Jie, Zhu, Shaoxiong, Xu, Chenjie
Conceptual Design Method Study of a New Frame Structure for Electric Heavy-Duty Trucks2026-99-11099/11/2026
To solve the multiple conflicts between user requirements and structural constraints in the new frame structure design of electric heavy-duty trucks, this research proposes a conceptual design methodology that integrates modular design theory, quality function deployment, and TRIZ theory. Firstly, the quality function deployment approach is employed to construct the requirement- technology priority matrix, translating the system of user requirements for electric heavy-duty trucks into specific engineering characteristics. Supported by modular design theory, foundation reusable modules and specialized modules requiring optimization in traditional heavy truck chassis are identified. Then, a three- dimensional requirements-space-technology heat map accurately reveals key design collisions. Furthermore, TRIZ theory is applied to extract relevant engineering parameters and leverage the contradiction matrix for rapid acquisition of innovative solutions, thereby standardizing the conceptual design process, optimizing its implementation, and improving design efficiency during product development.
Sun, Lei, Huang, Wei, Zhu, Baoli, Jin, Zhenye, Guo, Shouwu, Feng, Yu, Chen, Xianlong
Stability Control Strategy for Vehicles under Single-Wheel Brake-by-Wire Failure Conditions2026-99-11269/11/2026
The electromechanical brake-by-wire (EMB) system offers advantages such as high braking accuracy, fast response, and compact structure, and has become a major development direction for electric vehicles. However, the lack of necessary redundancy limits its large-scale application. Therefore, a stability control strategy is proposed, which is implemented at the algorithm level. According to braking intensity, the brake failure scenarios are classified into three levels: mild, moderate, and severe. For mild braking, a brake-force reconstruction strategy is adopted to compensate for the failed wheel. For moderate braking, a combined brake-force reconstruction and fuzzy sliding-mode steering control strategy is employed for active front-wheel steering. For severe braking, a brake-force reconstruction and model predictive control (MPC)-based active steering strategy is applied to achieve precise control of vehicle stability. The results show that the control strategy effectively compensates for single-wheel brake failure and ensures vehicle safety and stability across different braking intensities.
Zhang, Yi-long, Li, Shicheng, Xu, Lin
Recommended Practice of a Common Set of Tests for Sodium-Ion Battery Cells for the Propulsion of Battery Electric Road VehiclesJ3335_202609 (Current)9/9/2026
This document provides a recommended test guideline for secondary sodium-ion cells used for propulsion of electric vehicles including battery electric vehicles (BEV), hybrid electric vehicles (HEV), and other similar propulsion applications (e.g., forklift trucks). The objective of this document is to define common test procedures covering electrical performance, mechanical safety performance, thermal safety performance, and electrical safety performance. The results of these procedures can be used for comparative purposes. Requirements for pass/fail criteria are not defined in this document but are to be defined by the users of the document.
Battery Standards Testing Committee
SAE TOMORROW TODAY - A Roadmap for Sustainable and Affordable Mobility135849/8/2026
The future of mobility may not be about building bigger, more feature-packed vehicles. Listen in as we sit down with Dr. Christopher Borroni-Bird, Founder, Afreecar Consulting, to discuss his vision for rethinking transportation through right-sized EVs, solar-powered mobility, autonomy, and locally tailored solutions. From fleet electrification and last-mile autonomy to new manufacturing models and the changing economics of vehicle ownership, this conversation explores what it takes make mobility more affordable, sustainable, and accessible around the world. Learn more in Dr. Borroni-Bird's award-winning SAE International book, Sustainable and Affordable Mobility for All, which expands on these ideas and offers a compelling roadmap for the future of transportation. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Design, Simulation, and Experimental Evaluation of an R290-Based Dual Secondary Loop Thermal Management System for Electric Vehicles14-15-03-00188/28/2026
The transition toward low global warming potential (GWP) refrigerants, driven by increasingly stringent environmental regulations and carbon reduction targets, has imposed new requirements on thermal management systems (TMSs) for electric vehicles (EVs). These systems must ensure efficient operation across a wide range of ambient conditions while maintaining high energy efficiency and environmental compatibility. Among potential alternatives, R290 (propane) has emerged as a promising natural refrigerant due to its favorable thermophysical properties and low environmental impact. In this study, an R290-based dual secondary loop TMS is proposed and evaluated for wide-temperature-range EV applications. A one-dimensional system model was developed using Dymola and validated through experimental testing on a dedicated performance test bench. TMS performance was investigated under multiple steady-state operating conditions, including high-load cooling, battery fast charging, and low-temperature heating, and benchmarked against a conventional R1234yf-based direct TMS. The results demonstrate that the R290-based dual secondary loop system achieves improved performance compared to a conventional R1234yf direct system, with a coefficient of performance (COP) increase of 4.29% under high-load cooling conditions at 43°C and up to 27.27% under high-load heating conditions at −10°C. Furthermore, under extreme low-temperature conditions (−18°C), the system delivers a heating capacity of 7 kW with a COP of 1.8, demonstrating strong low-temperature adaptability without the need for auxiliary heating. The results confirm that the proposed R290-based dual secondary loop system provides significant advantages in energy efficiency and wide-temperature adaptability, offering a promising solution for next-generation EVTMSs.
Zhang, Yunpeng, Mohammed, Mustafa Mudassir, Gu, Yiliang, Zhou, Guoliang
Development of Remote Cab Controller for Construction Machinery14-15-03-00178/27/2026
This article focuses on the research and development of a remote cab controller for pure electric loaders, aiming to address the threats posed by traditional loaders operating in harsh and hazardous environments to drivers’ health and safety. First, the functional requirements of the controller were analyzed, based on which the hardware design with a multicore microprocessor as the core was completed, featuring functions such as signal acquisition, controller area network (CAN) communication, and H-bridge driving. On this basis, a control algorithm framework for remote driving was developed, including modules for signal input, analysis and processing, and signal output. Detailed control strategies were formulated for key components: For the pedal sensor, algorithms for opening degree calculation, automatic zero-position calibration, and dual-signal redundant fault diagnosis were proposed; for the steering module, precise angle calculation and force feedback feel simulation were achieved; and for the electric control handle, a hysteresis control algorithm was developed to suppress shocks caused by overly fast operations. In addition, a hierarchical fault diagnosis mechanism was established to ensure system safety. To verify the controller performance, a complete remote driving system was built. Field test results show that the system exhibits good signal following and control responsiveness in terms of traveling and working functions. Efficiency tests indicate that the remote driving efficiency can reach 80% of that of in-person operation under short-term test conditions, demonstrating the technical feasibility and control effectiveness of the developed controller. While the prototype exhibits promising performance for pilot deployment, long-term reliability metrics such as mean time between failures (MTBF) remain to be validated through extended field operation.
Lu, Yueqi, Ji, Shaobo, Yu, Qiuye, Li, Meng, Xu, Haozhi, An, Meng
SAE TOMORROW TODAY - Is Megawatt Charging the Missing Link to EV Scalability?135828/27/2026
As electrification expands beyond passenger vehicles to commercial trucks, mining equipment, marine vessels, and even aircraft, the challenge is no longer whether megawatt charging is possible, it's how to scale it safely and efficiently. Fortunately, industry standards are making that future possible. Listen in as we sit down with Ted Bohn, Principal Electrical Engineer at Argonne National Laboratory and Chair of the SAE J3271 Committee, to discuss the Megawatt Charging System (MCS) and how collaboration across industries is laying the foundation for high-power charging that works across multiple transportation sectors. This conversation offers a behind-the-scenes look at how standards are developed, tested, and validated, and why scalable charging depends on much more than the connector itself. Whether you're designing commercial EVs, deploying charging infrastructure, or following the future of heavy-duty Class 8 electrification, this episode provides valuable insight into the technologies and standards that will shape the next generation of mobility. Have your own thoughts on this topic? We'd love to hear from you! Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Security for Plug-In Electric Vehicle CommunicationsJ2931/7_202608 (Current)8/24/2026
This SAE Information Report establishes the security requirements for digital communication between Plug-In Electric Vehicles (PEV), the Electric Vehicle Supply Equipment (EVSE), and the utility, ESI, Advanced Metering Infrastructure (AMI), Home Energy Management Systems (HEMS), Battery Energy Management Systems (BEMS), and Distributed Energy Resource (DER) Aggregators.
Hybrid - EV Committee
Carbon Footprint Methodologies for Light-Duty VehiclesJ3341_202608 (Current)8/24/2026
This Disclosure Addendum will provide recommended disclosures and a standardized scenario for life cycle assessment (LCA) studies which: Include the midpoint impact category, climate change, via the impact indicator, global warming potential over a 100-year time frame (GWP100), hereby referred to as greenhouse gas (GHG) emissions Adhere to ISO 14040 and ISO14044 standards Follow the cut-off approach for allocation per ISO14067:2018 Are conducted for light-duty vehicles (LDVs) with the following powertrains: Internal Combustion Engine Hybrid Electric Plug-in Hybrid Electric and Extended-Range Electric Battery Electric These types of studies may also be referred to as a carbon footprint study or automotive LCA (A-LCA).
Hybrid - EV Committee
Study on the Impact of Vehicle Fixation Methods on the Range Testing of Electric Vehicles2026-99-03268/14/2026
The range energy consumption testing of electric vehicles is usually completed in an environment where the environmental chamber and chassis dynamometer are built. The vehicle is bound to the chassis dynamometer to simulate the range performance on a real road, and the vehicle's fixing method is particularly important, as it even affects the test results. In order to investigate the impact of vehicle fixation as a key testing factor on the range test results of electric vehicles, this study conducted comparative experiments using rigid fixation test vehicles at different positions. By relying on a chassis dynamometer to simulate road resistance and following the Chinese Light Vehicle Test Code (CLTC-P), a range test is conducted on the same electric vehicle under strictly controlled environmental conditions. The experiment collected data on endurance mileage, total energy consumption, and segmented energy consumption. By comparing the differences in simulated resistance and electric energy change trends of chassis dynamometer under different binding methods of test vehicles during the test process, the comprehensive energy consumption results were different. The results showed that the rigid fixation at different positions significantly affected the sliding results of the test vehicle chassis dynamometer, leading to differences in the comprehensive endurance energy consumption results. The comprehensive endurance mileage difference reached 24 kilometers, and the comprehensive energy consumption difference reached 4Wh/km. This study reveals potential sources of system bias in laboratory testing and analyzes the impact of vehicle fixation methods on comprehensive range energy consumption results. The research conclusions can provide a theoretical basis and empirical reference for improving the current standards for energy consumption and range testing of electric vehicles, and enhancing the accuracy and reproducibility of test results.
Zhou, Meng, Jiang, Zhijie, Geng, Peilin
Background for the Plug-In Electrical Vehicle Charge Rate Reporting and Test ProceduresJ2953/4_202608 (Current)8/14/2026
Full, industry standard test procedures for measuring the charging performance of a vehicle can be found in ISO/SAE 12906. This document describes the lessons learned during the development of that document, including false assumptions that are common in vehicle charging tests. The purpose of this document to better explain the need for the specific procedures in ISO/SAE 12906. Furthermore, by communicating the false assumptions and complications of historic charging tests, it is also hoped that others wishing to create test procedures different from ISO/SAE 12906 can do so without making the same mistakes of the past.
Hybrid - EV Committee
Research on Parameter Matching of Electric Vehicle Powertrain System2026-99-09058/7/2026
This paper focuses on the parameter matching of key components and the improvement of overall vehicle performance for a certain front-wheel drive pure electric vehicle. Firstly, based on the target performance of the vehicle, the rated/peak power, speed, and torque of the permanent magnet synchronous drive motor, as well as the capacity, voltage, and series-parallel scheme of the LiFePO4 power battery, are systematically calculated. Meanwhile, the gear ratio of the transmission system is determined based on the dual constraints of the maximum speed and the maximum gradeability. Subsequently, the vehicle model is built using AVL Cruise, and the maximum speed, 0-100 Km/h acceleration time, maximum gradeability, and NEDC range are simulated and verified under steady-state and transient conditions. The results show that the maximum speed of the prototype vehicle reaches 139 Km/h, the 0-100 Km/h acceleration is 7.98 s, the maximum gradeability is 33.2%, the power consumption per 100 Km is 12.12 KWh, and the range is 485 Km, all of which are superior to the design indicators. The research verifies the rationality of the proposed parameter matching scheme and can provide a theoretical basis and engineering reference for the forward development of the power system of pure electric vehicles of the same level.
He, Yuefan, Zhang, Baoping, Tang, Shujian, Chen, Hanbang, Jin, Biao
Automotive Engineering: August 202626AUTP088/6/2026
2026 Toyota RAV4 just the first to benefit from Arlene and Toyota's Woven City A dispatch from the automaker's "let's just try it" city finds hydrogen fuel cells, automated parking robots, and a robust spirit of innovation. Go ahead, lean into AI and smart testing HBK and VI-grade's Smart Prototypes Summit shows again just how fast the auto industry is moving. Fastener specification for EVs: what engineers need to know Breaking down the critical differences in fastener selection for EV platforms. The missing layer of engineering The next generation of materials and automotive breakthroughs will depend on a better understanding and engineering of microstructure. Editorial ITF Summit 2026: Engineering resilience The Navigator Planning a trek across America with dogs in EVs Mercedes AMG EV has three electric motors and the sound of a V8 Glazing creatively at Sekurit Imagine Lab Slate's modem-free pickup brings privacy back to driving 1.5 million km later: How Huber+Suhner connects the gangway GM Energy places bets on sodium batteries Road Ready Four electric SUVs, five states, 16 beagles Product Briefs Spotlight: Noise & vibration tech, machine tools Q&A The changing value of in-car data
Fastener specification for EVs: WHAT ENGINEERS NEED TO KNOW26AUTP08_038/1/2026
Breaking down the critical differences in fastener selection for EV platforms. Specifying fasteners for an electric vehicle is a fundamentally different exercise compared to an ICE vehicle. For ICE vehicles, the main challenges - heat, vibration, and torque repeatability - are well understood, and the industry has decades of established solutions to draw from. EVs introduce four additional constraints that change the equation entirely: high-voltage (isolation, low magnetic permeability, thermal-cycling resilience and gram-level weight targets. A mis-specified bolt at the battery, busbar, or inverter level can reduce efficiency, compromise safety or void certification. To get it right, there is a need to understand each constraint and the materials that can address it.
Faulkner, Patrick
PRODUCT BRIEFS26AUTP08_138/1/2026
Research on the Topology Design of Electric Vehicle Corner Module Suspension Based on POC Theory2026-99-01517/31/2026
The corner module is an innovative design that combines drive, steering, suspension, and other vehicle structures into a single wheel unit. This achieves a high level of integration for chassis functions. A chassis built on this module can perform more complex movements. Suspension is a key part that decides how the vehicle moves. However, current suspension design approaches lack a systematic methodology for configuration synthesis and analytical verification for the multi-degree-of-freedom (multi-DOF) requirements of the corner module. This study introduces a new method for designing the corner module suspension based on the Position and Orientation Characteristic theory (POC theory). First, the type of suspension DOF is derived from chassis functional requirements by treating the required corner module motion as the target suspension DOF. Then, we decide the number of chains, links, and joints in the mechanism and perform configuration synthesis of suspension mechanism. Next, we combine the selected kinematic pairs and select suspension mechanisms that meet the requirements of suspension DOF. There are two steps of calculation in this process. In this study, the goal is to design a suspension with three links, two loops, and two degrees of freedom. Seven suspension mechanisms with specific loops and components were obtained using the proposed process. Finally, the paper presents the process of mechanism verification. Using the steering link and ground excitation as inputs, theoretical calculations and simulation analysis were conducted to verify that the mechanisms obtained meets the suspension design objectives. This proves that the POC theory-based method for creating multi-DOF suspension is effective.
Kong, Wenkai, Zhu, Wenfeng, Zeng, Zhixuan
Study on the Electromechanical Coupling Torsional Vibration Characteristics of Electric Drive Transmission Systems under Motor Harmonic Excitation2026-99-02297/31/2026
Considering the spatial harmonic and time harmonic excitation of the permanent magnet synchronous motor, and the dynamic meshing excitation of gear pairs, this paper constructs one electromechanical coupling torsional vibration model. The torsional vibration characteristics of the electric drive transmission system in pure electric vehicles are investigated. Key electromechanical parameters are obtained by numerical calculations, and the electromechanical coupling system model is solved using the Runge-Kutta method. Finally, the system dynamic response characteristics at rated speed of 3000 r/min are analyzed. The results indicate that the significant bidirectional coupling exists between the electromagnetic excitation of the motor and the mechanical excitation of the gear transmission system. The current spectrum contains fundamental and harmonic components, along with components of gear meshing frequencies and their modulated sideband characteristics relative to the electrical frequency. The electromagnetic torque spectrum exhibits components of gear meshing frequencies, and gear angular velocity fluctuations are influenced by motor harmonic excitation. Under multi-source excitation, this study reveals the frequency modulation mechanism of torsional vibrations in electromechanical coupling drive systems, providing a theoretical basis for vibration and noise suppression.
Luo, Yaou, Zhao, Kaihua, Fu, Shengping
Research on Structural Simulation Design of Lithium-Ion Battery Liquid Cooling Plates Based on Multi-Objective Topology Optimization2026-01-50477/31/2026
As the energy density of electric vehicle power batteries continues to increase, efficient and uniform heat dissipation has become critical to their safety and performance. The liquid cooling plate serves as the core component of the battery thermal management system, with its flow channel structure directly impacting heat dissipation efficiency and system energy consumption. Current liquid cooling plate flow channel designs often rely on empirical methods, making it challenging to simultaneously optimize both heat dissipation uniformity and flow resistance performance. This paper focuses on a single lithium battery as the research subject, employing a topology optimization approach to design the liquid cooling plate flow channel structure. Optimization targets include minimizing pressure drop at the inlet/outlet and minimizing temperature difference across the contact surface between the plate and the battery. Under constant inlet cross-sectional dimensions and flow velocity, numerical simulation of fluid heat transfer processes revealed an 11.17% reduction in temperature difference across the contact surface. This enhances lithium battery heat dissipation uniformity while reducing inlet/outlet pressure drop by approximately 10.98%. This approach reduces the system energy consumption of liquid cooling. It enables multi-objective co-optimization design for power battery liquid cooling plate structures. It provides new technical references for the refined design of cooling systems in automotive power battery packs.
Ma, Honghui, Zheng, Yuqing, Yang, Minghao
Research on Reliability Test Methods for Electric Drive Axle Differentials2026-01-50447/31/2026
With the rapid development of the global economy, issues such as the energy crisis and environmental pollution have become increasingly severe. Owing to their environmental friendliness, structural simplicity, and high energy efficiency, electric vehicles have attracted widespread attention. Electric drive technology serves as the most promising and versatile propulsion solution for battery electric vehicles, hybrid electric vehicles, and fuel cell vehicles. As an advanced mechatronic transmission system, the electric drive axle offers high transmission efficiency, flexible packaging, and ease of digital and active chassis control integration, and has thus been increasingly adopted in modern vehicle architectures. The differential is a key component within the electric drive axle, responsible for regulating the rotational speed difference between the left and right wheels and ensuring balanced torque distribution. It plays a decisive role in vehicle stability and traction performance. This study focuses on the reliability testing methodology for differentials in electric drive axles, primarily including the extraction of reliability test conditions and the feasibility analysis of the proposed testing scheme. Specifically, based on the parameters of a given electric vehicle, a Simulink model of the motor and differential is established, and a complete four-wheel-drive vehicle model is constructed. Through simulation under typical driving conditions, operational data of the rear-drive axle differential are obtained. The collected data are then preprocessed and subjected to dimensionality reduction using Principal Component Analysis. The selected principal components are further analyzed using K-means clustering to construct representative differential reliability test conditions. The limitations of existing testing methods are analyzed based on the simulated results and relevant literature. Finally, a reinforced fatigue testing method for the differential is designed according to the extracted test conditions, and the feasibility of the corresponding test bench is evaluated.
Zheng, Hongyu, Li, Ziyu, Wang, Dajiang, Tian, Kai
Collapse Strength and Crush Space Analysis in Lateral Side Pole Impact of an Electric Vehicle2026-01-50527/31/2026
High-Voltage Battery (HVB) protection in lateral pole impact is very important due to severe nature of the impact. Unlike frontal impacts, vehicles have limited range of space and capacity to absorb kinetic energy in lateral side impacts. Nowadays, computer-aided engineering (CAE) using finite element analysis (FEA) is utilized routinely to simulate high-speed crash events of varied type, including side pole impact. These CAE applications focus on the analysis and design of HVB when the vehicle structure is well-developed. CAE methods are time-consuming and are not suited during the pre-program stage when the structure is only in a concept stage and not even a reasonable CAD is available/developed in any sense to use these methods. There is no analytical tool available to understand how to define the characteristics of the structure that surrounds and protects the HVB. The primary motive of this publication is to help with this aspect of vehicle planning/development. Needless to state that this procedure can also be used in planning/developing of internal combustion engine (ICE) and hybrid vehicles, as well. The objective therefore is to develop a simple method/procedure that can give reasonably accurate estimation of the collapse/crush force required for a specified crush space and hence protect the critical components, such as HVB and fuel tank. This analytical method also gives some insight into the optimal use of the upper body (rocker and floor cross-members) and underbody (ladder frame) parts. It was found, for a problem under consideration, optimum kinetic energy to be absorbed by the upper body is 32.5% to avoid intrusion into HVB.
Alavandi, Bhimaraddi, Midoun, Djamal, Frank, Randy
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-50537/31/2026
The rapid evolution of electric vehicles (EVs) has led to the development of innovative approaches to optimize ride comfort, handling, and the overall suspension performance. EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics, unlike their conventional internal combustion engine (ICE) counterparts. This paper outlines an advanced damping force modeling methodology using machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The analysis is based on data-driven ML algorithms, i.e., Gradient Boosting, Random Forest, and Neural Networks, to simulate the nonlinear and frequency-dependent phenomenon of dampers in different operating conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. Additionally, this research evaluates the impact of machine-learned damping force predictions on critical ride and handling metrics, including ride comfort, road-holding ability, and energy efficiency. The results demonstrate that the ML models can enhance the iterative design process considerably and help to create the adaptive suspension systems that will address the particular requirements of EVs. This paper contributes to advancing the state-of-the-art of the suspension modeling, incorporating the ML-based insights in the development cycle. It highlights the possibility of artificial intelligence to transform suspension design, paving the way for superior ride quality and vehicle performance in electric mobility.
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Development of Electric Vehicle Oils with High Energy Efficiency and Cooling Performance by Applying Novel Friction Modifier2026-01-50647/31/2026
Improving the efficiency of electric vehicle (EV) transmissions can help to extend the driving range of EVs, and the EV oil used in these transmissions plays an important role. In this study, in order to enhance energy efficiency, we examined the effects of lowering viscosity, traction, and friction in EV oil. While friction modifiers (FMs) have been widely used as friction reduction technologies in the field of tribology for many years, we previously developed a new FM that reduces friction in drive units. We found that a combination of lowering viscosity and using the developed FM was effective for better energy efficiency. The oil formulated with the developed FM improved efficiency by approximately +0.8% to +0.9% compared to commercial EV oil. EV oil also requires cooling performance. We assumed that reducing heat generation through friction reduction would improve cooling performance and examined the effect of lowering viscosity, traction, and friction. Consequently, it was found that a combination of lowering traction and applying the developed FM is effective for reduction in parasitic heat losses. We also examined durability, which is an issue when reducing viscosity. The results suggested that the oil formulated with the developed FM had good durability for gears and bearings. Thus, we succeeded in developing an ultra-low-viscosity EV oil that has excellent energy efficiency and high cooling performance.
Nakamura, Toshitaka, Furuse, Takashi, Hasegawa, Shinji, Akahori, Shinya, Itou, Kimikazu, Sakurada, Soichiro, Akiguchi, Junnosuke
Microstructure and Properties of 316L Stainless Steels Modified with C and Cr Elements2026-99-02177/31/2026
SiC-based power devices are favored for high-voltage and high-power applications due to their superior material properties. However, the demand for higher breakdown voltages and improved channel mobility presents significant challenges to the etching process, especially the micro-trenching effect. In this study, etching results from inductively coupled plasma (ICP) have been presents, which focused on using various SF6/O2/Ar gas ratios to eliminate micro-trenching effect. The profile analysis of micro-trench was taken by cross-sectional scanning electron microscopy (SEM). The results demonstrate that micro-trenches primarily originate from the coupling effect between ion multi-reflection from sidewalls and redeposition of etch byproducts. Based on this mechanism, we propose a quasi-Bosch process: a combined polymerization and etching step in oxygen-fluorine-rich plasma deposits polymer on exposed SiC and the mask, while removing it from the structure bottom via ion bombardment to enable etching and passivation; then alternates with a short fluorine-plasma step, which consumes sidewall polymer through ion incidence and prevents SiFxOy charge accumulation, cycle etching gradually deepens the structure without micro-trenches. Different gas ratios and etching time not only change the plasma energy distribution but also affect the temporal synchronization between etching and passivation steps. This approach reduces the special demands on ICP equipment capabilities while achieving superior trench profiles. The optimal etching conditions produced a micro-trench-free SiC structure with a vertical sidewall angle and a surface roughness of less than 1 nm. This methodology and resulting structures significantly advance the manufacturability of high-performance SiC power devices, enabling next-generation applications in electric vehicles and grid infrastructure where device yield and reliability are paramount.
Zhao, Yingfan, Dong, Shuang, Sun, Xiaoxu, Chang, Xiangpeng, Liang, Yiwei, Tong, Weiping
A Quasi-Bosch Process for Micro-trench Free SiC Etching using Inductively-Coupled Plasma (ICP)2026-99-01557/31/2026
SiC-based power devices are favored for high-voltage and high-power applications due to their superior material properties. However, the demand for higher breakdown voltages and improved channel mobility presents significant challenges to the etching process, especially the micro-trenching effect. In this study, etching results from inductively coupled plasma (ICP) have been presents, which focused on using various SF6/O2/Ar gas ratios to eliminate micro-trenching effect. The profile analysis of micro-trench was taken by cross-sectional scanning electron microscopy (SEM). The results demonstrate that micro-trenches primarily originate from the coupling effect between ion multi-reflection from sidewalls and redeposition of etch byproducts. Based on this mechanism, we propose a quasi-Bosch process: a combined polymerization and etching step in oxygen-fluorine-rich plasma deposits polymer on exposed SiC and the mask, while removing it from the structure bottom via ion bombardment to enable etching and passivation; then alternates with a short fluorine-plasma step, which consumes sidewall polymer through ion incidence and prevents SiFxOy charge accumulation, cycle etching gradually deepens the structure without micro-trenches. Different gas ratios and etching time not only change the plasma energy distribution but also affect the temporal synchronization between etching and passivation steps. This approach reduces the special demands on ICP equipment capabilities while achieving superior trench profiles. The optimal etching conditions produced a micro-trench-free SiC structure with a vertical sidewall angle and a surface roughness of less than 1 nm. This methodology and resulting structures significantly advance the manufacturability of high-performance SiC power devices, enabling next-generation applications in electric vehicles and grid infrastructure where device yield and reliability are paramount.
Jiang, Wenjing, Yang, Chengyue, Tang, Yidan, Zhang, Runze, Liu, Yang
Impacts of Automation and Electrification Trends on Pneumatic Braking Systems of Commercial Vehicles2026-36-03417/30/2026
Historically, the demand for advanced technology, efficiency, and safety has been a primary driving force in the evolution of commercial vehicles, particularly with respect to braking systems. More recently, the increasing levels of vehicle autonomy and electrification have emerged as irreversible trends, significantly accelerating the development of new functionalities and innovative electrical/electronic [E/E] architectures. These advancements are essentially focused on performance optimization, risk mitigation, and enhanced system reliability through the application of functional safety and cybersecurity standards, thereby shaping the current landscape of braking system design. From an efficiency standpoint, braking systems with higher levels of electronic content, functional integration – included with regenerative braking systems - and harmonization have been developed to improve energy efficiency and support global scalability. Concurrently, new system configurations are continuously being introduced to enhance vehicle safety and advanced driver assistance capabilities, in alignment with evolving regulatory requirements and market expectations. This paper evaluates the impacts of automation and electrification on commercial vehicle pneumatic braking systems, focusing on Anti-lock Braking Systems [ABS], Electronic Braking Systems [EBS] and air management platforms. It provides a technical overview of both architectures, assessing their capabilities to meet modern requirements such as integration with advanced vehicle architecture, regenerative braking for electrified applications, and Advanced Driver-Assistance Systems [ADAS] support. The study details the evolution of air management systems, with emphasis on electrified vehicles, including key functions such as air compressor charge control, Air Processing Unit [APU] desiccant regeneration, and electronic control strategies. Additionally, it examines key drivers of braking system evolution, braking system selection considering ADAS regulatory developments, Net Zero strategies, and automation trends. The paper further evaluates compliance with functional safety and cybersecurity standards and assesses the readiness of both platforms for emerging mobility concepts. Finally, it highlights the risks of deploying higher levels of autonomy in heavy-duty towing vehicles when operating with non- ABS semi-trailers, identifying this as a critical area for further investigation.
Guarenghi, Vinícius Mendes, Nicora, Fabio, Pizzi, Rafael Fortuna, Resende, Angelo Roberto Rodrigues, Pinto, Gustavo Laranjeira
Maximizing Autonomy, Minimizing Environmental Impact: The Cooperative Regenerative Braking2026-36-03167/30/2026
In conventional braking systems, the kinetic energy of a vehicle is predominantly converted into heat through friction, a thermodynamically inefficient process. This not only causes progressive wear of components but also leads to the release of various materials, including heavy metals and organic compounds. With increasing concern over non-exhaust emissions, the search for innovative solutions becomes imperative. In electrified vehicles (xEVs), regenerative braking emerges as a strategic technology, converting kinetic energy into electrical energy to recharge the battery and extend range. This process not only enhances the vehicle's energy efficiency but also results in reduced frequency and intensity of mechanical brake usage. Consequently, there is a direct reduction in the wear of friction braking components, which translates into a significant mitigation of particulate matter emissions associated with this wear. The optimization of these systems occurs through Cooperative Regenerative Braking (CRB), which intelligently integrates with hydraulic braking. The primary challenge lies in managing the transition between modes to recover maximum energy without compromising safety and driver comfort. This technical paper explores how CRB employs 'torque blending' via advanced ECUs and software to adjust in real-time the proportion of each braking type, aiming for maximum energy recovery in diverse driving scenarios. To verify the effectiveness of this system, practical tests were conducted on a vehicle. The results obtained from these tests were conclusive, demonstrating significant gains in energy efficiency, with an increased battery recharging capacity during decelerations, optimized by the braking system. This improvement in efficiency directly impacts the reduction in the use of the conventional friction brake system and, consequently, a sharp decrease in particulate matter emissions. In this context, the intelligent and cooperative management of regenerative braking is a strategic and fundamental component for building a more sustainable future in vehicular mobility.
Batagini, Emerson, Romão, Bruno
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