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Lithium-ion battery safety under mechanical abuse has become a critical challenge with the widespread adoption of electric vehicles. This study proposes a predictive framework combining multi-physics finite element simulation and machine learning to estimate the temperature rise of lithium-ion cells under impact conditions. An Electro-Thermo-Mechanical (ETM) coupled model was established in LS-DYNA to simulate the effects of impactor radius, velocity, and ambient temperature on internal heat generation. Using a full factorial sampling design, 125 simulation scenarios were generated to extract maximum temperature data. These data were used to train and compare several regression models, including Support Vector Machines (SVM), Decision Trees (DT), Back Propagation Neural Networks (BPNN), and Random Forests (RF). A Stacking ensemble model integrating these base learners achieved the highest prediction accuracy, with an R2 of 0.996 and RMSE below 0.5. Performance remained robust even
Wan, ChengZhan, ZhenfeiChen, Qiuren
Sodium-ion batteries (SIBs) are becoming a strong candidate for large-scale energy storage applications due to their cost-effectiveness and abundant sodium resource reserves. Ether solvents have advantages such as excellent low-temperature performance and good reduction stability. However, poor oxidation stability limits the use of ether-based electrolytes, which need to be addressed urgently. In this study, 1 M sodium tetrafluoroborate (NaBF4) and 0.05 M sodium difluoro(oxalato)borate (NaDFOB) were added in tetraethylene glycol dimethyl ether (G4), which is named “BDG4”. BDG4 electrolyte can promote the formation of cathode electrolyte interface (CEI) layers containing NaF and B─O/B─Na inorganic components on the surface of the cathode. The dense CEI layers can prevent the solvent from undergoing oxidation reactions. Therefore, thanks to the lower highest occupied molecular orbital (HOMO) energy level of G4 and its close coordination structure with Na+, the electrolyte has a high
Bai, ZhengMai, XinyuDou, XinChen, ZixinSong, ZhenChen, LongLi, Chunzhong
Lithium-ion batteries (LIBs) have drawn substantial scientific interest because of their impressive energy storage capabilities and long-term operational stability. In recent years, new battery material systems have emerged, among which LMFP (LiMnxFe1−xPO4) is regarded as a promising candidate for future battery development, combining high energy density with enhanced safety. However, research on the thermal runaway (TR) behavior of LMFP-based batteries remains scarce, leaving their cell-level safety unverified. This study modifies the conventional state of charge (SOC) classification method by measuring the oxidation state of cathode materials at specific voltages. By testing the thermal runaway (TR) temperature and gas release characteristics of LMFP hybrid batteries under different voltage states, it reveals the influence of cathode oxidation state on TR behavior. The results demonstrate that when the NCM (LiNi₀.₅Co₀.₂Mn₀.₃O₂) component remains unoxidized, the battery does not
Guo, ZhenquanWu, SenmingLuan, WeilingChen, YingChen, Haofeng
Accurate SOC and capacity estimation is essential for the safe operation of lithium-ion batteries. However, model parameters drift due to temperature variations and aging. This study proposes a migration-model-based method for joint estimation of SOC and capacity over a wide range of temperatures and degradation levels. The WSPF algorithm identifies migration factors in real time and applies them to estimate SOC and capacity under nonlinear, non-Gaussian conditions. Validation under various test conditions demonstrates clear advantages. Compared to EKF, the migration-model-based algorithm reduces the maximum RMSE of SOC estimation to 0.55%. For capacity estimation, it achieves a maximum RMSE of 1.15%. The estimation accuracy remains high throughout temperature changes and aging, highlighting the robustness and applicability of the proposed method for real-world battery management systems.
Liu, WeiqiangChen, ZhengWei, FuxingShen, Jiangwei
Reliable monitoring of the internal state of lithium-ion batteries (LIBs) is crucial for mitigating potential safety hazards. The incorporation of a reference electrode (RE) within the battery constitutes a vital approach for achieving single-electrode monitoring and understanding changes in electrode state during cycling. Among these, the lithium-copper reference electrode (Li-Cu RE) is particularly cost-effective and straightforward to prepare, being fabricated by depositing lithium onto a copper wire. However, Li-Cu RE exhibits a relatively short effective lifespan during long-term cycling, thereby limiting its practical application. In this work, based on a self-fabricated three-electrode single-layer pouch cell, the microstructural changes before and after failure of the Li-Cu RE were characterized and analyzed, revealing its failure evolution process. Post-failure microstructures observations exhibit marked porosity in the electrode, attributed to substantial depletion of surface
Hu, JiaxingLuan, WeilingChen, HaofengChen, Ying
The rapid integration of intermittent renewable energy sources (RES) poses significant operational challenges for modern power systems. Lithium-ion battery (LIB)–based battery energy storage systems (BESS) have become vital for grid stability and energy management. However, large-scale deployment of BESS has led to increasing incidents such as fires and explosions, raising serious concerns regarding their safety and reliability. To overcome the limitations of traditional reliability assessment methods—such as reliability block diagrams (RBD), fault tree analysis (FTA), and Markov models—this study proposes an integrated fault detection and reliability analysis framework that combines FTA, failure mode and effects analysis (FMEA), and a Bayesian Fault Propagation Network (BFPN). The framework systematically models fault propagation across component, subsystem, and system levels, dynamically updating the prior probabilities of basic failure events using a Gaussian Mixture Model (GMM) and
Yang, ZhanChen, XiaoboZheng, RuixiangLi, Mian
With the vigorous development and technological iteration of the new energy vehicle industry, the strategic position of inspection, certification, R&D and testing in the industrial chain has become increasingly prominent. As the core energy storage component of new energy vehicles, the potential safety risks and environmental hazards in the testing process of power batteries are particularly worthy of vigilance. Based on more than ten years of operational practice in battery laboratories, this paper summarizes experience and lessons in depth, focusing on problems such as smoke, fire, explosion and release of toxic and harmful substances caused by thermal runaway of batteries in lithium-ion battery safety abuse tests. From the dimensions of risk characteristics of safety abuse tests, laboratory security design, and laboratory environmental protection facilities, it systematically expounds the risk prevention and control strategies and environmental protection measures for lithium-ion
Ren, GaohuiLiu, LeiJiang, ChenglongSun, ZhipengChen, Liduo
Lithium-ion batteries suffer from capacity degradation, lifespan attenuation, and power decline at low temperatures. Alternating-pulsed-current (APC) heating method is an effective solution for improving the low-temperature performance of batteries, but it still faces challenges in terms of low heating efficiency and energy consumption. This work proposes a pulsed-charging-current (PCC) heating method to address these issues. The effect of the PCC under various conditions, including frequency and amplitude, is investigated through experiments. According to the experimental results, the battery can be heated from -20 °C to above 7.5 °C within 15 minutes using the proposed PCC method, with a heating rate of 1.83 °C/min. Compared with the traditional APC heating method, the heating rate of the PCC method increases by 7.9%. During the 15-minute heating process, the battery capacity increased by 131.9 mAh on average, and the charging efficiency can be achieved 95% above. The proposed method
Xiao, YuechanHuang, XinrongWu, ZeZhang, YipuMeng, Jinhao
Accurate and rapid remaining useful life (RUL) prediction of batteries under various extreme conditions is crucial for battery management systems. However, existing methods often face challenges such as limited datasets under extreme conditions, high model complexity, and weak interpretability. Therefore, this paper proposes a hybrid framework based on pruning domain-adaptive convolutional neural networks (CNN) and long short-term memory (LSTM) to study RUL prediction under different fast-charging conditions using the MIT dataset. First, four voltage-related feature matrices are extracted. Using maximum mean discrepancy (MMD) constraints, the CNN-LSTM is trained with source domain and limited target domain data to align distributions. Neuron pruning is then applied to the fully connected layer to compress the model. Results demonstrate that under sparse target domain data, the domain adaptation approach achieves significantly lower prediction errors than fine-tuning. The pruned model
Huang, MingyueChen, HongxuLuan, Weiling
With the rapid expansion of the electric vehicle market, the safety of lithium-ion batteries, which serve as the main power source, has become a critical concern. Current mainstream methods for battery fault detection generally face a technical bottleneck of struggling to balance high accuracy with a low false alarm rate. Furthermore, constrained by algorithmic complexity and data processing efficiency, detection speeds often fail to meet the practical demands of real-time monitoring. As a result, developing more efficient and accurate fault detection technologies has emerged as a key challenge urgently needing to be addressed in the industry. This paper proposes a hierarchical fault detection framework for lithium-ion batteries that integrates voltage change characteristics with a Local Outlier Factor (LOF) scoring mechanism. The framework aims to achieve early identification and accurate diagnosis of abnormal battery states through multi-dimensional feature extraction and algorithmic
Gao, ZhengpengGao, PingpingChang, PenghuiLiu, GangWu, Ji
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
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
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
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
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
One primary cause of NEV fires is thermal runaway initiated by internal short circuit in power batteries, leading to subsequent thermal diffusion throughout the battery system. Severe internal short circuit damage can precipitate thermal runaway phenomena in lithium-ion batteries, potentially culminating in fire incidents involving electric vehicles. Although mild internal short circuit may not immediately induce thermal runaway, continuous charge and discharge cycling can exacerbate such conditions, progressively elevating risks associated with thermal runaway and other pertinent safety hazards. Conventional safety testing methodologies, employing techniques such as crushing and nail penetration to simulate internal short circuit, often amplify the extent of these shorts and fail to accurately replicate less severe, deeper internal short circuit. Additionally, methods incorporating foreign objects like nickel pieces for simulating internal short circuit necessitate battery disassembly
Sun, ZhipengMa, TianyiHan, CeWang, FangRen, Gaohui
All-solid-state batteries (ASSBs) based on sulfide electrolytes hold great promise for next-generation energy storage, yet their performance is critically constrained by unstable cathode–electrolyte interfaces. Here, we report a dual-modification strategy utilizing ionic liquids (ILs) in combination with lithium salts to simultaneously improve interfacial wettability, ionic transport, and electrochemical stability in NCM811 composite cathodes. Three ILs (EMIMTFSI, Pyr₁₄FSI, and PP₁₃FSI) and three lithium salts (LiTFSI, LiDFOB, and LiBOB) were systematically evaluated and screened. While neat ILs improved initial capacities by reducing solid–solid contact resistance, they also triggered parasitic reactions with sulfides, resulting in capacity fading. Among the lithium salts, LiBOB was identified as the most chemically compatible additive, forming thin and uniform hybrid interphases enriched with B–O species. This interphase effectively suppressed high-voltage side reactions and reduced
Gu, Yu-YangTian, Shi-YuQi, JiYang, Li-PengZhan, Wen-WeiYang, Xiao-GuangYi, Yong
The State of Charge (SOC) is a key parameter for measuring the remaining capacity of new energy vehicle batteries. It not only directly reflects the driving range of the vehicle but also plays an indispensable role in ensuring operational safety and extending battery lifespan. Accurate estimation of SOC provides strong support for the safe and reliable operation of electric vehicles. During the charging and discharging process of lithium iron phosphate batteries, the intercalation and deintercalation of lithium ions cause deformation of the electrode's lattice structure, leading to the expansion and contraction of the electrode volume. This, in turn, exerts stress on the limited internal space of the battery, which is mainly manifested as changes in battery pressure monitored by sensors. To address the issues of insufficient information and low estimation accuracy associated with the use of electrical signals in traditional data-driven methods, this study introduces pressure
Tian, JieDu, JinqiaoRao, BoLai, TiandeDong, BoyiJiang, Jiuchun
Lithium-ion batteries represent a complex and nonlinear voltage behaviour on various time scales. Battery models are needed to analyze and estimate the battery behaviour and determine their suitability for practical applications. Battery model simulations in previous studies were mainly based on pulse charge and discharge cases. The current amplitude used in the test cases was limited, and the temperature factor of the battery model was neglected. The simulation conditions above were significantly different from those in practical applications. In this paper, an equivalent circuit model considering the temperature factor is developed to simulate the practical applications of lithium-ion batteries. Experimental tests for parameterization are applied to the commercially available 189 Ah lithium iron phosphate battery cells under a wide range of experimental conditions. The parameters are obtained through experimental tests and are used to build the equivalent circuit model of the battery
Chang, AnWang, ShengweiZhou, Kai
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
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
With the growing global demand for sustainable energy and high-performance mobile devices, lithium metal solid-state batteries (LMBs) have emerged as a research hotspot in the field of energy storage due to their exceptional high energy density and significant safety advantages. However, the growth of lithium dendrites and their penetration through the solid electrolyte remain key issues leading to battery short-circuiting and failure. To date, there has been a lack of effective in situ research methods to reveal the failure mechanisms, which has severely restricted the commercialization of LMBs. This study innovatively employs in situ electrochemical impedance spectroscopy (EIS) to investigate lithium plating behavior in symmetric cells during critical current density (CCD) tests under room temperature and elevated temperature conditions. By analyzing characteristic signals at 1 MHz, this study presents the in situ impedance changes at the grain boundaries and interfaces of the
Liu, ZexuanWu, SenmingChen, YingLuan, WeilingChen, Haofeng
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
The health assessment of lithium-ion batteries is crucial for the efficient operation of electric vehicles and energy storage systems. However, in the complex and ever-changing operating environment, accurately assessing the health state of lithium-ion batteries remains a challenge that urgently needs to be overcome. This paper proposes a SOH estimation method based on the relaxation phase curves after battery charging. It extracts health features from the relaxation phase and combines them with Gaussian process regression. The method initially extracts six statistical features from the relaxation voltage and selects those with high correlation as inputs for the model. Next, it constructs new combined kernel functions by randomly pairing 5 commonly used kernel functions. It employs cross-validation to adaptively select the optimal combined kernel function. The proposed method is validated with 55 batteries. Results show that compared with traditional single-kernel models, the mean
Lin, YupengYang, DaWan, FuMu, JingyiLiu, RuiqiYin, WenweiLuo, BingZhong, ZhengChen, Weigen
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