Browse Topic: Lithium-ion batteries

Items (1,454)
With the strong momentum of electric vehicles (EVs), the battery recycling industry is undergoing rapid growth. While the Chinese government has implemented a white-list mechanism under which only approved recyclers are allowed to process retired batteries, small-scale illegal battery recycling vendors have posed a serious challenge. This study compares the techno-economic performance of battery recycling between legal and illegal recyclers in China, and makes recommendations to eliminate illegal operations. Our research covers two battery chemistries: lithium nickel-manganese-cobalt oxide (NMC) and lithium iron phosphate (LFP), as well as two technological pathways: resource recycling and cascade utilization. For the general case, the costs of illegal vendors are 35-46% lower than that of legal companies. Although legal companies achieve high resource utilization, their overall economic performance lags behind due to their high costs associated with equipment, environmental protection
Du, ShilongLi, HaoyangDou, HaoHao, Han
The rapid advancement of lithium-ion battery technologies, particularly pouch cells, has driven significant growth in electric vehicles, mobile devices, and renewable energy storage. However, pouch cells are especially susceptible to mechanical deformation and failure, including bulging caused by internal gas formation—a common indicator of cell aging or imminent failure. In this study, we developed a visual dataset of bulging pouch battery cells to support real-time diagnostics and safety monitoring in industrial and laboratory environments. The dataset includes 200 high-resolution images (100 bulged, 100 normal) curated through a web-crawling and filtering pipeline. The dataset is benchmarked across several traditional machine learning models to evaluate performance and feasibility for edge AI deployment. The best model achieved strong classification accuracy while maintaining a small computational footprint suitable for embedded applications.
Alkawasmie, MohammadFarooqui, SaadAlgalham, DheyaRahman, MahfilurChalla, KarthikeyaMaxim, BruceShen, Jie
This work evaluates a standardized 30-ton, 16 m railbus platform optimized for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption and component performance under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates detailed sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined cycle- and calendar-aging model using the rainflow algorithm to extract charge cycles, while cost models include capital, fuel, maintenance, track fees, and staffing. Results show that battery-electric configurations achieve 1 kWh/km energy
Ahrling, ChristofferTuner, MartinGainey, BrianTorkiharchegani, AmirScharmach, MarcelHertel, BenediktAlaküla, Mats
Lithium-ion batteries are critical to Electric Vehicles (EV) and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires characterization of electrical, thermal, and mechanical properties at the full cell and cell-component levels. In this study, a commercial cell from an EV was disassembled, and tested to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell’s power capability. Full cell compression tests were conducted to characterize mechanical behavior under deformation and used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell integrity under mechanical deformation. At the
Challa, VidyuRostami-Angas, Masoudkong, KevinWang, LeyuReichert, RudolfKan, Cing-Dao
Battery thermal runaway is a major safety concern in electric vehicles because of the extreme heat and hazardous gases released during cell failure. These venting events can quickly raise the temperature of the battery enclosure and cabin floor, threatening occupant safety. To address this challenge, this study employs the Design for Six Sigma (DFSS) methodology to design and optimize a thermal protection system that delays and limits heat transfer to the cabin. A physics-based transient heat-transfer model was combined with DFSS principles to systematically evaluate insulation materials, shield layouts, surface emissivity, and layer geometry. An L-18 orthogonal array was used to identify key parameters and quantify their influence on thermal robustness. The optimized architecture reduced cabin-floor temperature rise under severe runaway conditions (600–900 °C vent gas), meeting occupant-egress safety requirements. Findings confirm DFSS as an effective framework for developing high
El-Sharkawy, AlaaAsar, MonaTaha, NahlaSheta, Mai
Thermal runaway in high-voltage lithium-ion battery modules should focus on critical safety and design challenges in electric vehicle applications, which need predictive methods that enhance passenger safety and support regulatory compliance. The primary purpose of a lithium-ion battery in an electric vehicle is to provide reliable energy storage while maintaining safe operation under different operating conditions. This study proposes a Design for Six Sigma (DFSS) methodology to virtually predict and correlate thermal runaway and its propagation in an 800V high-power lithium-ion battery pack module. Conventional propagation analysis relies heavily on physical testing, whereas the DFSS-based virtual framework enables cost-effective evaluation at early design stages. Input factors included are heat transfer pathways, which are sensitive to the temperature changes, as well as thermal propagation time. Control factors are the design or process parameters that engineers use to establish
Dixit, ManishRaja, VinayakGudiyella, Soumya
Non-uniform temperature distribution within lithium-ion battery cells is a critical challenge that accelerates degradation, compromises safety, and reduces pack-level performance in electric vehicles (EVs). This work focuses on modeling and minimizing these thermal gradients through the structured optimization of a liquid-based Battery Thermal Management System (BTMS). A one-dimensional transient thermal model is developed to capture the axial temperature differentials (ΔT) in a cylindrical cell under dynamic drive-cycle loading, incorporating detailed heat transfer from the cell interior through thermal interface materials (TIM) and an aluminum cooling plate to the coolant. Using a Design for Six Sigma (DFSS) approach with an L18 orthogonal array, key control factors—including coolant flow rate, inlet temperature, TIM properties, and plate geometry—are systematically analyzed to identify configurations that optimally balance low average temperature with minimal internal temperature
El-Sharkawy, AlaaAsar, MonaSerpento, StanSheta, Mai
As the utilization of lithium-ion batteries in electric vehicles expands, monitoring the usable cell capacity (UCC) is essential for ensuring accurate state-of-health (SOH) estimation. Battery performance degradation is influenced by temperature and constraints. Capacity tests in laboratory settings are typically conducted at low C-rates to approximate equilibrium conditions, whereas in real vehicle applications, charging currents are often much higher. This discrepancy in rates frequently results in deviations between laboratory characterization and on-board Battery Management Systems (BMS) capacity estimation. To investigate how C-rate of diagnostic Reference Performance Test (RPT) modulates aging effects under temperature and mechanical loading, we conducted long-term cycling tests on lithium iron phosphate/graphite pouch cells at 25°C and 45°C under different constrained conditions. The cycling protocol is a tiered multi-rate protocol. Cells were aged at Block1 under 1C, and UCC
Zhang, ShanNiu, ZhiceXia, Yong
The increasing adoption of electric vehicles (EVs) introduces critical vulnerabilities associated with dependence on rare earth elements used in traction motors and battery systems, impacting supply chain stability, environmental sustainability, and cost scalability. This investigation focuses on simulation-optimized rare earth-free EV propulsion components, including induction-based and wound rotor electric motors employing ferrite and iron-nitride magnetic materials, in combination with lithium iron phosphate (LFP) battery chemistry recognized for enhanced safety and extended cycle life. An integrated multi-physics simulation framework coupled with targeted experimental validation is employed to evaluate efficiency, thermal behavior, and durability of the proposed motor–battery systems. The optimized configurations demonstrate automotive-grade performance, with motor efficiencies ranging from 90–96% and LFP batteries retaining over 84% of nominal capacity after 5,000 charge–discharge
Saraswat, ShubhamVishe, Prashant
Battery thermal management is crucial for ensuring the safety, efficiency, and longevity of lithium-ion battery packs, particularly in electric vehicles (EVs). The primary purpose of a lithium-ion battery in an electric vehicle is to store and provide electrical energy for vehicle propulsion while maintaining safety under different operating conditions. This work proposes a thermal correlation between 1D CFD simulation and experimental test data under passive environmental heat exchange conditions without active coolant flow of a battery pack comprising four modules. An environmental exchange test was conducted using a 50% state of charge (SOC) battery pack, which is stabilized at 25°C to assess passive heat dissipation, thermal soak behavior, temperature distribution, and potential thermal runaway risks. The simulation predictions correlate well within a 1.5°C range compared to test results using ambient temperature and flow inputs, which confirms the reliability of the modeling
Nayaka, Sateesh KumarDixit, ManishGudiyella, Soumya
Accurate estimation of the State of Health (SOH) is crucial for ensuring the safety and reliability of lithium-ion batteries. Compared to conventional electrical signals, battery swelling behavior offers significant advantages as it contains richer aging-related information. This study investigates the aging characteristics of batteries under external mechanical constraints, and proposes an innovative SOH estimation method based on differential force (DFDV) analysis. Cycling tests were conducted on fully constrained LFP prismatic batteries under 0.2 MPa. Throughout the testing, both mechanical and electrical signals were synchronously monitored and recorded. The research systematically analyzes the swelling behavior and aging patterns of batteries. Through incremental capacity analysis (ICA), the aging behavior and underlying mechanisms under room-temperature and constrained conditions were revealed. Simultaneously, mechanical signal analysis demonstrated a strong correlation between
Niu, ZhiceZhang, ShanXia, Yong
A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature at
Sok, RatnakKusaka, Jin
The performance of a full battery pack with its effective thermal management system (BTMS) depends on coolant flow and heat transfer characteristics inside the pack. To develop a full BTMS using model-based design (MBD), the model must capture the coolant pressure drop ∆?? and heat-exchange performance from the cell to ambient air via the coolant, cooling flow channels, air gaps, and pack cases. Predicting battery pack responses (i.e., voltage, SOC, temperature) under all weather conditions is a challenge, as a complete pack contains several hundred to thousands of cells, coolant lines, coolant line bends, and coolant channels. This work presents a detailed approach to identifying heat transfer and ∆P correlations that can capture the real-time thermal-electrical performance of a mass-produced LIB pack under constant speed (in winter) and transient driving (in summer). A vehicle test is conducted using a Tesla Model Y, 2-motor model equipped with a 75-kWh LIB pack. The LIB pack's
Sok, RatnakKusaka, Jin
Lithium plating is a critical barrier to fast charging in electric and hybrid-electric vehicles, occurring at high state of charge (SOC) or low temperatures when Li+ deposits as metallic lithium on the anode surface instead of intercalating into graphite. At low temperatures, plated lithium may form dendrites that pierce the separator and trigger thermal runaway, while at high SOC, irreversible plating accelerates capacity fade by depleting cyclable lithium. Despite extensive study, lithium plating remains difficult to incorporate into battery management systems (BMS) due to computational complexity and the challenge of real-time detection, leading to reliance on conservative lookup maps. This work presents a lightweight empirical model for predicting plating-free charging limits in lithium nickel manganese cobalt (NMC) cells. A high-fidelity pseudo-2D electrochemical model was exercised across a wide range of charge rates and temperatures to capture the coupled effects of SOC
Sundar, AnirudhGhate, AtharvaZhu, QilunPrucka, RobertBarron, MorganFigueroa-Santos, Miriam
With the increasing adoption of electric vehicles (EVs) worldwide, ensuring the long-term reliability and performance of the battery systems has become a paramount engineering challenge. Lithium-ion cells exhibit dimensional changes throughout their operational life, characterized by reversible “breathing”—expansion and contraction during charge and discharge cycles—and irreversible swelling due to aging. Compression pads are critical components for ensuring the lifetime performance of battery packs. The primary function of a compression pad is to act as a compliant cushion between cells. It accommodates these volumetric fluctuations by exerting consistent and optimized pressure. By absorbing the stress from cell expansion and maintaining structural integrity within the module, compression pads mitigate degradation mechanisms and ultimately maximize the durability and safety of the battery system over thousands of cycles. This paper highlights the importance of tailoring elastomeric
Deng, WeilinGunashekar, Subhashini
The advancement of electric vehicles necessitates a rigorous focus on passenger cabin safety, particularly concerning the severe thermal hazard of a lithium-ion battery thermal runaway. Unlike internal combustion engine vehicles, electric vehicles require interior materials that provide superior thermal resistance to slow heat propagation, delay autoignition, and minimize smoke and toxic gas emissions, thereby securing a survivable evacuation window. This paper examines the application of the lumped-capacitance thermal model and the derived thermal time constant (τ) as a foundational framework for evaluating and selecting cabin materials. This approach enables a quantitative, physics-based ranking of materials—including seat composites, sound-deadening layers, electrical insulation, and carpet assemblies—based on their intrinsic ability to delay their own temperature rise under transient heat flux. By integrating materials with a high τ and elevated critical failure temperatures, this
El-Sharkawy, AlaaTaha, NahlaAsar, MonaSheta, Mai
Currently, a persistent concern arises regarding the management of retired Li-ion batteries from electric vehicles (EVs). A potential solution is to repurpose these batteries for less demanding applications, such as energy storage systems. Such repurposed batteries are commonly referred to as second-life batteries (SLBs). In this work, we explore the economic feasibility of implementing SLBs in Stanford University’s EV bus charging station via previously developed technoeconomic decision support model. The model simulates battery aging behaviors across various usage conditions, optimizing the operational parameters of SLBs. The estimated lifetime is expected to be 10 years in an optimal using condition. In addition, an economic sensitivity analysis explores the influences of various factors. Furthermore, we calculate the cost savings of total $82,500 over its second lifetime, which is derived from the adoption of SLB instead of new batteries.
Zhuang, JihanChueh, WilliamOnori, SimonaBenson, Sally M.
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
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
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
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
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
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
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
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
In practical applications, power cells face a mix of external influences such as temperature variations and structural limits (rigid constraints) that trigger intricate electrochemical and mechanical reactions. This study systematically explores the temporal evolution of surface pressure in lithium-ion pouch cells subjected to rigid mechanical constraints under varying thermal conditions, with a specific focus on the interplay among mechanical stress, lithium intercalation, and lithium plating. To investigate the battery’s electrochemical and mechanical responses, this work integrates experimental measurements with an electrochemical–mechanical coupling model. The analysis is performed under initial loads of 0.3, 0.5, and 1.0 MPa at 25 °C (ambient temperature) and 0 °C (representative low-temperature condition). At 25 °C, surface pressure followed a two-stage pattern: first, stress relaxation occurred, followed by a shift into quasi-steady cycling (cycle-to-cycle variations are minimal
Du, YingyueChen, 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
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
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
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
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
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
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
Lithium-ion batteries (LIBs) have become indispensable components in diverse energy applications driven by their high energy density, long cycle life, and low self-discharge. These excellent characteristics are directly influenced by their manufacturing processes, where variations in battery design and processing parameters will lead to significant differences in performance. Therefore, reliable and efficient evaluation of battery performance across manufacturing processes is essential for quality assurance and process improvement. Traditional methods rely on formation cycling and associated electrochemical tests, which are time and cost intensive. Different from them, a simulation-based approach for manufacturing performance evaluation is proposed in this study. The method employs the pseudo two dimensions (P2D) electrochemical model within the PyBaMM framework, where model parameters such as electrode type, electrode size, and particle size are derived from manufacturing data and
Yan, YifeiMeng, JinhaoSong, ZhengxiangZhang, ShiruiPan, YuhaoYang, PeihaoPeng, Jichang
The requirement on high energy density Li-ion batteries demands high energy chemistry system, this rise concerns on batteries’ safety issue. Battery non-active components, including current collectors and separator play important role in improving battery safety. Composite current collectors, which are consisted of a polymer layer between two plated thin metal layers, are widely treated as a solution to reduce safety concerns caused by high nickel layered cathode materials, e.g. LiNi1-x-yCoxMnyO2, LiNi1-x-yCoxAlyO2 and LiNi1-x-y-zCoxMnyAlzO2 with Ni content higher than 0.8. In the meantime, composite current collectors can reduce most weight of current collectors and improve the cell’s gravimetric energy density without replacing cathode or anode materials. Moreover, high thermal stable separator could effectively prevent internal short circuit for it melts in higher temperature. In this work, we came up with a cell design which contains composite current collectors as positive
Liu, JingyuanLu, YongLiu, Haijing
The increasing electrification of marine equipment underscores the need to ensure lithium-ion battery (LIB) safety in corrosive environments. Unlike land applications, shipboard batteries are continuously exposed to salt spray, which accelerates material degradation and raises the risk of thermal hazards. Thus, this study investigates the effects of salt spray corrosion on the electrochemical performance and Thermal runaway (TR) behavior of commercial 18650-type ternary LIBs. Through charge-discharge calorimetry and cone calorimeter tests, variations in voltage response, capacity fade, mass loss, and heat release rate were analyzed under different states of charge (SOC), states of health (SOH), and exposure durations. The results show that corrosion significantly accelerates electrode deterioration, leading to faster capacity decline and voltage plateau shifts. At higher SOH, casing rupture induced earlier TR with violent combustion, whereas at lower SOH, corrosion-induced energy
Tao, LiyanyuShi, XinyuanYang, QinyuanLiu, Jiahao
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
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
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
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
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
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
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
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
Electric vehicle (EV) battery life cycle assessment (LCA) is emerging as a strategic necessity amid booming demand and tightening environmental regulations. This report consolidates key findings and recommendations for EBRR (Electric Battery Reuse & Recycling) to implement a comprehensive LCA program covering EV lithium-ion batteries from cradle-to-grave and cradle-to-cradle perspectives. The study confirms that global Li-ion battery demand is skyrocketing – projected to increase 14-fold by 2030[1] – amplifying the urgency for sustainable battery management (see Figure 1). It outlines the full life cycle stages of EV batteries (raw material extraction, manufacturing, use, and end-of-life) and compares linear vs. circular approaches. Using the ISO 14040/44 framework[18, 19] and industry-standard LCA tools, the report evaluates environmental impacts and identifies hotspots. Key findings show that mining and manufacturing dominate the battery’s carbon footprint, but end-of-life strategies
Asokan, GayathriRaju cEng, RajkumarDhananjaya, ChandanSattigeri cEng, Sudhir V
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