Your Destination for Mobility Engineering Resources

Announcements for SAE Mobilus

Browse All

Recent SAE Edge™ Research Reports

Browse All 177

Recent Books

Browse All 721

Recently Published

Browse All
This specification covers piston rings fabricated from cast iron.
AMS E Carbon and Low Alloy Steels Committee
This specification covers a beryllium aluminum alloy in the form of investment castings.
AMS G Titanium and Refractory Metals Committee
This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in the SAE International (SAE) J2945/1 standard. For the purposes of this recommended practice, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured dedicated short range communications (DSRC) vehicle-to-vehicle (V2V) device as defined in SAE J2945/1 and is installed on a light vehicle. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle-level so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J2945/1
V2X Core Technical Committee
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
This SAE Recommended Practice provides a system for marking thermoset rubber parts to designate the general type of material from which the part was fabricated.
Committee on Automotive Rubber Specs
This specification covers crimp-style aluminum lug terminals and conductor splices for aluminum aircraft wire. Lug terminals and conductor splices are hereafter called “terminals.”
AE-8C2 Terminating Devices and Tooling Committee
In recent times, energy conservation and environmental protection have attracted more and more attention. This research presents a comparative study on the quantitative analysis and comprehensive ranking of the cradle-to-grave environmental benefits of a multi-material body shell across 18 countries. For quantitative analysis of the cradle-to-grave environmental impact of the body shell, life cycle assessment (LCA) was adopted to assess the process of interactions between the environment and human activity. For a comprehensive ranking of the environmental impacts across 18 nations, two modified techniques were used for order preferences by similarity to the ideal solution (TOPSIS) methods, which are improved by the fuzzy analytic hierarchy process (FAHP) and entropy method (EM). The outcomes from these three methodologies; FAHP&EM-TOPSIS, FAHP-TOPSIS, and conventional TOPSIS revealed that the comprehensive environmental benefit rankings of TOPSIS are highly different from the two
Li, ShuhuaWu, ZongyangJi, XiaoyuanTang, ZhengWu, BofuRokhsun, Hossain Rahman
Multimodal sensors, capable of simultaneously acquiring multiple physical or chemical signals, have shown broad application potential in fields such as health monitoring, soft robotics, and energy systems. However, current multimodal sensors often suffer from complex fabrication processes and signal decoupling challenges, which limit their practical deployment. To address these issues, this work presents a thin-film temperature–strain multimodal sensor (FTSMS) fabricated via laser processing. The temperature-sensing unit, based on the Seebeck effect, achieves a sensitivity of 9.08 μV/°C, while the strain-sensing unit, utilizing BaTiO₃/AlN@PDMS as the sensitive layer, exhibits a gauge factor (GF) of 43.2. By integrating distinct sensing mechanisms (thermovoltage for temperature and capacitance change for strain), the FTSMS enables self-decoupled measurements over 20–90 °C. Applied in LIB monitoring, it successfully captures real-time temperature and strain variations during charge
Wang, ZiweiLi, ZhenglinGao, YangXuan, Fuzhen
This study systematically investigates methods to enhance the fast-charging capability of lithium-ion batteries through advanced simulation. The electrochemical reaction mechanism, heat generation mechanism, and lithium plating mechanism are analyzed in detail, and an electrochemical–thermal coupled model incorporating a lithium plating sub-model is established. A hybrid parameter identification strategy, combining random search, grid search, and manual adjustment, is employed to calibrate the model across different operating conditions, thereby improving its accuracy in reproducing real battery behavior. Lithium plating is selected as the primary indicator to evaluate fast-charging performance. Based on simulation results, the effects of both operational parameters and structural parameters on lithium plating are thoroughly analyzed. The results indicate that lower charging rates, elevated charging temperatures, higher electrode porosity, and reduced tortuosity are favorable for
Zhao, PeiqiangZhan, WenweiQi, JiYi, Yong
With high energy density and long cycle life, lithium-ion batteries (LIBs) are currently the most promising electrochemical devices for electric vehicles and energy storage. However, the safety and reliability of LIBs can be significantly compromised in low-temperature cyclic due to anode lithium plating and other factors which are still unclear. Therefore, it is essential to reveal the thermal-gas stability of LIBs under low-temperature cyclic. This study investigates the thermal runaway (TR) characteristics and gas production characteristics after TR of 18650-type NCA LIBs across four states of health (SOH), from 100% to 70%. Using Glove box, Electrochemical impedance spectroscopy, Scanning electron microscope, X-ray photoelectron spectroscopy, Accelerating rate calorimetry, and Gas chromatography, the research identifies critical trends in temperature rate, gas composition and explosion risk. After around 150 cycles, there is a significant and rapid decline in capacity. The internal
Wang, HailongWu, SenmingLuan, WeilingChen, Haofeng
This paper focus on the direct cooling plate with serpentine flow channels, the effects of heat load power, compressor speed, fan speed, and types of heating plates on the temperature field of the cold plate were investigated respectively based on the direct cooling thermal management system.The experimental results show that as the heating power decreases, both the overall temperature and temperature difference of the cold plate decrease synchronously. The temperature distribution along the flow channel is non-monotonic, with the highest temperature at the first elbow (T2/T3) and the lowest temperature at the outlet (T12), which is lower than the inlet temperature.A study on the T4-T11 region reveals that when the fan speed is low, with the increase of compressor speed, both Tmax and Tmin first decrease and then increase, while ΔT decreases. When the fan speed is constant at medium or high levels, as the compressor speed increases from low to medium, Tmax and Tmin decrease and ΔT also
Chen, SijianHuo, GuojunChen, JiyongWei, ShaoliangZhang, GuihaoZhang, JinglongJu, XinzeYang, Xiaoxia
Due to limitations in available battery samples and testing costs, lithium-ion battery thermal runaway experiments are not practical to repeat multiple times, and the reliability of experimental results is frequently questioned. To systematically evaluate the repeatability of the heating wire-triggered method in thermal runaway tests, this study investigates two types of commercial 18650 cylindrical batteries with NCM/graphite chemistry under different heating power levels and health conditions. The results indicate that under the same heating power, batteries of the same type exhibit good repeatability in thermal runaway onset time and onset temperature, with the consistency of onset time outperforming that of onset temperature. As the heating power increases, the onset time of thermal runaway decreases significantly, while the variation in onset temperature remains relatively small. Compared to fresh batteries, aged batteries show reduced variability in thermal runaway
Wang, JiaYan, HongtaoZhang, YuemengLin, ChunjingLao, Li
As an important energy storage device and the power source for key equipment such as automobiles and drones at present, lithium-ion batteries generate a substantial amount of heat during their operation. Without an effective cooling system, the temperature of the battery module can rise, significantly impacting the battery's service life and safety performance. Therefore, automotive battery modules require an efficient battery thermal management system to regulate heat dissipation and extend battery life. We note that many existing vehicle battery thermal management systems focus solely on the surface temperature of the battery. However, uneven heat distribution within the battery can also lead to issues such as unbalanced aging and thermal runaway safety hazards. Thus, we specifically emphasize the internal temperature distribution of the battery, focusing on internal temperature optimization design and simulation. Taking the battery module equipped with the third-generation NCM 9
Wu, JiayiZheng, BowenKang, MengranZhan, WenweiQi, JiYi, Yong
Appropriate thermal management system is important for the lifespan and safety of proton exchange membrane fuel cells (PEMFCs). A comprehensive thermal management system for PEMFC was proposed through finite element model (FEM), control optimization and nanofluid cooling. An 0D-3D coupled thermal model for energy balance and local temperature field analysis was established. By coupling internal heat transfer dynamics with Proportional-Integral-Derivative (PID) control logic, the optimal parameter combination was determined as Kp=-1 m/(s⋅K), Ki=-0.1 m/(s2⋅K) and Kd=0 (m/K). Additionally, the nanofluid coolant revealed a concentration-dependent trade-off between enhanced thermal performance and decreased flow performance. In the range of 0-15% of the nanofluid concentration, the Reynolds number and pressure drop increase with the increase of the concentration of the nanofluid, while in the range of 16-20%, the Reynolds number decreases with the increase of the concentration of the
Zhang, XiaoliangDeng, YikangZhao, YanliWang, QiLuo, Shengfeng
Heat sinks are essential cooling components in the battery thermal management systems (BTMS). Porous fin microchannel heat sinks can achieve high heat transfer rates in confined spaces, offering significant potential for practical applications. In this study, a modified-porous fin microchannel heat sink for BTMS is numerically simulated to examine its fluid dynamics and thermal exchange properties. By partially and uniformly filling metal foam in solid fins, the temperature is reduced, the Nusselt number is increased, and the comprehensive performance is enhanced. Compared with solid fins, the modified design is shown to yield a maximum Nusselt number improvement of 153.6%, accompanied by a peak performance evaluation coefficient reaching 1.92. Thermal analysis is conducted by considering both structural optimization and coolant flow behavior. Effects of metal foam filling width and height are investigated. The fluid dynamics and thermal exchange properties of the modified structure
Zhang, LiyuanLai, Huanxin
Current studies about battery pack bottom strike usually focus on one test condition individually. To study the relation between quasi-static and dynamic crush in battery pack bottom strike, the paper combined quasi-static crush result and dynamic strike preset kinetic energy value with the same displacement damage on the battery pack bottom plate and cell. First, based on the finite element model of the battery pack, the quasi-static crush is applied. Several dynamic crush tests with different initial kinetic energy sets are also introduced. Then based on the same displacement damage, the pressure in quasi-static and kinetic energy in dynamic conditions are summarized. Fitting methods including polynomial regression, support vector regression (SVR), extreme learning machine (ELM), multilayer perceptron (MLP), Gaussian process regression (GPR), and K-nearest neighbor (KNN) regression are used to study the relation between the two different test load. The result shows that they have a
Tang, HongxiWang, ShengweiZhou, KaiLiu, Jinyu
To enhance the accuracy and robustness of State of Charge (SOC) estimation for lithium iron phosphate (LiFePO₄) batteries and to overcome the limitations of traditional electrical signal-based methods—such as cumulative errors in Coulomb counting and the need for rest periods in open-circuit voltage (OCV) methods—this study proposes a novel SOC fusion estimation algorithm based on mechanical expansion force signals. Addressing the challenge of feature extraction, a model framework integrating the Sparrow Search Algorithm (SSA), Least Squares Support Vector Machine (LSSVM), and Adaptive Extended Kalman Filter (AEKF) is developed. The state equation is constructed via Coulomb counting, while SSA optimizes the LSSVM to establish an observation model centered on expansion force as the input. The AEKF is employed to achieve real-time, precise SOC prediction. Experimental validation under varying temperatures (25°C, 35°C) and dynamic driving cycles (FUDS, UDDS) demonstrate that this fusion
Du, JinqiaoRao, BoTian, JieWu, YizengXu, HaomingJiang, Jiuchun
Currently, electric propulsion is playing an increasingly important role in marine propulsion systems.Lithium metal batteries are new-generation high-performance energy storage system with development prospect. Traditional flammable and volatile organic liquid electrolytes pose a risk of thermal runaway, while solid-state lithium metal batteries using solid electrolytes have significant advantages in energy density and safety, and are considered the most promising mobile power sources. Among numerous solid electrolyte systems, polymer solid electrolytes have excellent flexibility, good interface compatibility, and good processing characteristics, which have attracted the attention of researchers. Polyurethane (PU) is a common polymer with high mechanical strength and a flexible and adjustable molecular structure, making it one of the best choices for polymer electrolyte matrices. Based on the structural design of polyurethane polymers, this paper explores polycaprolactone type
Yuan, MengTang, QingYu, Gongye
To address the challenges of recognizing abnormal states, detecting subtle early warning signs, and quantifying fault severity in scenarios involving simultaneous multiple faults in lithium-ion batteries, this study proposes a dual-layer fault diagnosis framework that integrates One-Class Support Vector Machine (OCSVM) and Robust Local Mahalanobis Distance Quantile (RLMQD) algorithm. First, a three-dimensional multi-scale feature space, incorporating voltage, kurtosis, and voltage change rate, is constructed to detect abnormal battery states via OCSVM and dynamically filter abnormal time periods with improved adaptability. Second, a computationally efficient RLMQD-based quantization algorithm is developed, which employs a small-scale sliding window and adaptively selects healthy cells to construct reference distributions. By incorporating low-quantile thresholds, the algorithm enhances early abnormality detection and significantly reduces false positives. Subsequently, fault severity
Wei, FuxingYang, LibingWang, ZongleiXia, XueleiShen, JiangweiChen, Zheng
For the safe and reliable deployment of lithium-ion batteries, accurate state of health (SOH) estimation is paramount. However, most existing data-driven methodologies depend exclusively on single-modal data, such as voltage-capacity or incremental capacity (IC) curves. Such limited data frequently fails to offer a holistic understanding of the complex battery degradation process. To address this limitation, this paper proposes a novel multi-modal feature fusion network. This network can effectively combine three different but complementary data modalities: historical point features, voltage-capacity and IC sequence features, as well as degraded image features. To this end, the framework incorporates a one-dimensional convolutional neural network (1D-CNN) for analyzing point features, leverages a Transformer encoder to process sequence features, and employs ResNet for identifying spatio-temporal patterns in degraded images. These heterogeneous features are then collaboratively
Li, XiaobinHe, NingYang, Fangfang
With the rapid expansion of global electric vehicles (EVs) deployment, the echelon utilization of retired lithium-ion batteries (LIBs) has emerged as a critical issue. Although these batteries typically retain over 70% of their initial capacity and remain suitable for stationary energy storage systems, the substantial variability in aging states poses safety risks. Conventional capacity estimation methods are often time-intensive and costly, while data-driven approaches face challenges from complex degradation mechanisms and limited historical usage data. This study uses the electrochemical impedance spectroscopy (EIS) method to create a model that estimates the capacity of retired batteries. EIS offers fast measurement, requires no historical cycling data, and provides rich state-of-health (SOH) information. An EIS dataset was acquired from 18650-type LFP and NCM cells aged under multiple cycling conditions. The real part and magnitude of the impedance spectra were extracted as input
Hou, ZhengyuLuan, WeilingSun, ChangzhengChen, Ying
Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and performance optimization of electric vehicles. In practical operating environments, however, data quality is often compromised by noise interference, frequent fluctuations in load conditions, and the inherently non-stationary nature of battery degradation features. These challenges reduce the effectiveness of conventional modeling approaches, which often struggle to maintain both high prediction accuracy and strong generalization capability. To address these issues, this study develops a comprehensive SOH estimation approach encompassing data quality enhancement, degradation feature extraction, and hybrid deep learning-based modeling. In the first stage, multi-stage anomaly detection techniques are applied to remove noisy or inconsistent measurements. A week-based indexing strategy is introduced to generate temporally coherent labels, ensuring that time
Wang, SijingJiao, MeiyuanHuang, WeixuanLin, YitingLiu, HonglaiLian, Cheng
Ensuring safety and consistent quality in lithium-ion battery manufacturing is essential for the reliable operation of electric vehicles and energy storage systems. Strict quality control measures during production not only enhance product safety but also reduce the number of defective units entering post-market recycling streams. However, variations in battery quality remain inevitable, making efficient downstream sorting an important complement to upstream manufacturing control. Efficient sorting of retired lithium-ion batteries is critical for battery second-life utilization and circular economy development. Based on 750 commercially recycled retired batteries, this study proposes a 1D CNN-Transformer hybrid deep learning framework for automatic screening of retired batteries. The framework first employs a 1D convolutional neural network to extract local features from time–voltage sequences and compress sequence length, followed by a Transformer encoder to capture global
Xiao, HualongLuo, GangWang, LiLin, MingqiangWu, Ji