Browse Topic: Lithium-ion batteries

Items (1,491)
With the rapid development of the new energy vehicle energy storage industry, lithium-ion battery technology is undergoing a phase of rapid technological advancement. Enhancing battery energy density and safety remains a core challenge in overcoming industrial bottlenecks. During long-term cycling operations, deviations in state of charge (SOC), voltage, and temperature of individual cells inevitably occur, leading to reduced energy utilization efficiency. These deviations may also induce local overcharging and internal short circuits in individual cells, ultimately triggering thermal runaway incidents. While existing battery balancing strategies primarily focus on uniformity regulation, they fail to adequately address the coupling mechanisms of heat generation, heat storage, and thermal runaway propagation during balancing processes. Furthermore, the poor coordination between these strategies and thermal management systems makes it difficult to meet the complex safety requirements of high-energy-density batteries. To enhance the safety and energy utilization efficiency of battery systems during operation, this study focuses on the synergistic optimization of balancing strategies and thermal runaway prevention control. By establishing computer models of individual cells and battery packs in CATIA software, the research analyzes the evolution mechanisms of thermal runaway triggered by system state inconsistencies, while exploring the regulatory patterns of balancing parameters on thermal safety. Utilizing the ANSYS simulation platform, the study systematically examines the impact of three critical parameters—ambient temperature, discharge rate, and coolant flow rate—on battery temperature rise, providing theoretical support and technical references for the design of high-reliability lithium-ion battery pack systems.
Yu, ZhengGong, JiFan, YiLiang, WeiLi, YueweiLiu, FashenXie, MaojunCen, Zucai
This study investigates the suppression of lithium-ion battery (LIB) fires using composite aqueous extinguishing agents, with a 20 Ah lithium iron phosphate battery as the experimental subject. Based on the functional selection of coverage-isolation and cooling-smothering, three aqueous extinguishing agents, S-E-1, S-E-2, and S-E-3, were designed and developed using hydrocarbon surfactants. The results indicate that all three aqueous fire extinguishing agents can effectively suppress LIB fires. Through a comprehensive evaluation of extinguishing time, cooling rate during extinguishing agent release, and physical and chemical parameters of the extinguishing agents, the optimal formulation was determined to be 2% SDS (sodium dodecyl sulfate), 1% SDBS (sodium dodecyl benzene sulfonate), 3.5% CAB (cocamidopropyl betaine), 1% APG0810 (alkyl polyglycoside), 1% CDEA (coconut oil acid diethanolamine), 0.32% CH4N2O (urea), 1.5% NH4H2PO4 (ammonium dihydrogen phosphate), 0.5% C2H6O2 (glycol), 1.5% Na3PO4 (sodium phosphate), and deionised water, demonstrated the best performance. Reducing the extinguishing time to 14 seconds, a 41.7% reduction compared to pure water, and increasing the cooling rate to 0.816 °C·s^–1, which is 4.4 times that of pure water, with no reignition observed. This work contributes to the theoretical principles needed to engineer next-generation fire suppression materials for lithium-ion batteries that are both efficient and eco-friendly.
Yu, TaoZhu, ShunbingLi, KeZhang, Menglan
As the energy density of electric vehicle power batteries continues to increase, efficient and uniform heat dissipation has become critical to their safety and performance. The liquid cooling plate serves as the core component of the battery thermal management system, with its flow channel structure directly impacting heat dissipation efficiency and system energy consumption. Current liquid cooling plate flow channel designs often rely on empirical methods, making it challenging to simultaneously optimize both heat dissipation uniformity and flow resistance performance. This paper focuses on a single lithium battery as the research subject, employing a topology optimization approach to design the liquid cooling plate flow channel structure. Optimization targets include minimizing pressure drop at the inlet/outlet and minimizing temperature difference across the contact surface between the plate and the battery. Under constant inlet cross-sectional dimensions and flow velocity, numerical simulation of fluid heat transfer processes revealed an 11.17% reduction in temperature difference across the contact surface. This enhances lithium battery heat dissipation uniformity while reducing inlet/outlet pressure drop by approximately 10.98%. This approach reduces the system energy consumption of liquid cooling. It enables multi-objective co-optimization design for power battery liquid cooling plate structures. It provides new technical references for the refined design of cooling systems in automotive power battery packs.
Ma, HonghuiZheng, YuqingYang, Minghao
This recommended practice (RP) presents a methodology to evaluate RESS Cells Closure Integrity (Leak Tightness) requirement. This RP applies to two types of RESS Cells, each containing liquid electrolyte: Lithium ion (Li-ion) Cells and Sodium ion (Na-ion) Cells. The Equivalent Channel Method is used as a suggested cell closure integrity requirement for a given RESS Cell design during its production and product validation phases. The Closure Integrity requirements intended to assure no electrolyte leakage and no excessive moisture ingress during the usage of these cells as part of the RESS (Battery Pack), which is crucial to assure the safety and performance of these RESS. This RP specifies non-destructive Integrity (leak) testing processes of the Cell Closure. It describes approved leak testing technologies, testing procedures, tooling requirements, and leak test systems validation/verification requirements. This document may be applied to RESS Cell Closure Integrity testing during their initial product validation and their in-line 100% of production integrity/leak testing. This RP applies to RESS Cells with rigid packaging (cylindrical or prismatic) or flexible packaging (pouch).
Battery Standards Testing Committee
Batteries generate a large amount of heat during operation, and if it cannot be dissipated in a timely and effective manner, it will seriously affect the performance, lifespan, and even safety of the battery. Therefore, battery heat dissipation has become a key challenge in the development of new energy vehicles. The traditional liquid cooling system has problems such as complex design and control, and the need to improve heat dissipation efficiency. To address these issues, this study proposes an optimized design scheme for battery environment heat dissipation control system based on liquid cooling heat dissipation system. This study first conducted an in-depth analysis of the thermal generation mechanism of lithium-ion batteries and studied existing examples of thermal management schemes. On this basis, an innovative forward and reverse circulation device was designed, combined with a liquid cooling heat dissipation structure. The Keil uVision4 programming software was used to write the microcontroller control program, and the circuit was simulated and verified using Proteus simulation software. This study established an experimental platform and conducted physical testing and thermal imaging detection. By collecting temperature change data under different heat dissipation modes and analyzing the experimental data, the results show that the optimized liquid cooling heat dissipation system significantly improves the heat dissipation efficiency. The system exhibits good performance under different cooling modes.
Ding, XvqiangNi, YiweiGu, ChenZhang, JinChen, MingyangJiao, Yunxiao
Thermal runaway assessment in automotive battery development is still largely driven by isolated abuse tests, while design decisions require quantitative insight into how cell geometry, material thresholds, and thermal boundary conditions influence thermal runaway onset and severity. This paper presents a systematic sensitivity study using a coupled electrochemical and thermal model augmented with Arrhenius-based decomposition reactions to represent the dominant exothermic pathways. Thermal runaway onset is defined using a temperature rise-rate criterion to distinguish gradual heating from runaway acceleration. Two trigger modes are considered: an internal short circuit initiated by nail penetration and an external heating trigger. Four parameter groups are investigated: cell length scaling, separator decomposition temperature, external heating power, and the convective heat transfer coefficient to the environment. For the nail-triggered internal short circuit, larger cells exhibit lower peak temperatures but longer times to reach the maximum, indicating a geometry-driven shift from rapid escalation to a slower, more moderated evolution. In the external heating case, increasing cell size significantly delays onset, while peak temperature shows a nonlinear trend and approaches saturation rather than scaling inversely with size. Increasing the separator decomposition temperature also shows a saturation effect because alternative reactions can dominate the triggering sequence. External heating power exhibits a threshold: below a critical level, convective losses balance the input and prevent runaway. Even when external heating is stopped at an intermediate temperature, higher preheating power can still lead to higher peak temperatures due to a larger remaining reactive inventory when the internal short circuit occurs. Improved heat rejection consistently delays onset, reduces peak temperature, and accelerates cool-down. Overall, the study extends prior trigger-specific analyses by providing a unified reduced-order sensitivity view across two abuse pathways and by identifying threshold and saturation behaviors that translate directly into design-relevant robustness levers.
Ceylan, DenizKulzer, André CasalWinterholler, NinaGiek, MichaelWeinmann, Johannes
Hybrid electric vehicles rely heavily on battery pack power capability, which is often compromised by non-uniform aging and thermal gradients. Conventional battery models typically use bulk state-of-health metrics, failing to capture localized degradation that leads to current imbalances and reduced pack utility. This paper presents a multi-scale modelling framework that integrates Electrochemical Impedance Spectroscopy data into a fractional-order equivalent circuit model to simulate localized degradation in Lithium Iron Phosphate cells. Results show that the terminal voltage of LFP cells can be accurately modelled using the proposed fractional-order equivalent circuit with a discrete transfer-function implementation, maintaining root-mean-square errors below 20 mV across most state-of-health and state-of-charge conditions. The validated cell model is then extended to a degradation-aware battery pack representation. The battery pack in this work utilizes a 200-kWh, 800 V architecture consisting of five modules connected in parallel, each module composed of 13 parallel strings of 250 series cells, evaluated under multiple degradation scenarios. By integrating this pack model into a Class-8 series hybrid powertrain simulation, this study quantifies how cell-to-cell heterogeneity impacts vehicle performance under the VECTO regional delivery drive cycle. At the vehicle level, these battery constraints influence engine duty cycles and battery pack stress metrics. When localized degradation reaches up to 40% in one module while the remaining modules degrade up to 20% to 30%, such inhomogeneous degradation reduces the minimum pack terminal voltage by approximately 27% and increases peak discharge current by more than 30%, resulting in more rapid degradation. These battery-level limitations translate into higher fuel consumption by up to 6% in a charge-sustaining scenario.
Safavi, Seyed RezaHomayouni, HoomanShoa, TinaWang, JasonMcTaggart-Cowan, Gordon
Electrification using battery systems is one of the most relevant solutions regarding ecological challenges within multiple application cases such as mobility, power tools or stationary power supply. Nonetheless besides recent achievements in some cases battery systems are still lacking behind operational requirements compared to conventional propulsion systems, therefore limiting the potential of electrification. Especially when purpose design possibilities are limited. Besides improving properties of cell materials, better usage of the available installation space offers potential for optimization of the battery system. The development of battery systems is complex, as it involves multiple system levels and domains, along with a wide range of design options and architectures. Battery cells that can be manufactured in flexible formats enable possibilities to make more efficient use of available installation spaces. At the same time, these additional degrees of freedom increase design complexity and significantly expand the solution space. For example, numerous options for sizing and positioning of the cells are available that are interacting with the cooling system and housing design. Also, additional challenges regarding electrical and thermal load distribution occur using format flexible cells. To support developers, new methods and tools are necessary to handle this complexity. Therefore, the authors present a methodology that includes an installation space optimization using format-flexibly produced pouch cells that generates different possible layouts of cells and modules, an approach for electrical and thermal modeling of the battery system that is applicable for varying cell arrangements as well as possibilities for a fast criteria-based evaluation of different cell and module arrangements that can be used for an overall optimization of the battery system. Finally, the authors are discussing benefits and disadvantages of the presented methodology as well as the usage of format flexibly produced pouch cells using an illustrative case study.
Müller-Welt, PhilipBause, KatharinaSpohn, HannesAlbers, Albert
With the continued expansion of electric mobility, liquid-cooled thermal management systems have become indispensable for ensuring the performance, durability, and safety of automotive battery packs. This work presents a novel cooling-plate design that integrates offset strip-fin turbulators to enhance convective heat transfer between lithium-ion cells and the circulating coolant. A comprehensive multi-region CFD model of the full battery pack is developed, incorporating an implicit lumped-parameter representation of cell heat generation. The numerical predictions are validated against dedicated experimental measurements available in the literature. Subsequently, a parametric study is conducted in which the number of hydraulic sub-modules and the inlet/outlet configurations are systematically varied to generate all feasible design permutations. The resulting configurations are compared to assess thermal performance and to quantify the benefits—as well as the potential penalties—introduced by the turbulators relative to the experimentally validated baseline.
Montenegro, GianlucaOnorati, AngeloDella Torre, AugustoTariq, Muhammad HasnainBonetti, Elisa
Current lithium-ion batteries should generally only be charged above 0 °C, as charging below this temperature can promote lithium plating and irreversible degradation. However, conventional pack-level heating elements increase system mass and design complexity. In addition, heat is transferred from outside into the cell, causing the temperature inside the cell to rise slowly. This study evaluates internal Joule heating of cylindrical Li-ion cells using a zero-mean square-wave current excitation and quantifies the associated aging impact. LG INR21700-M50L cells were tested at 0 °C, −10 °C, and −20 °C with three excitation frequencies (50 Hz, 1 Hz, 10 mHz) at 5 A amplitude. Each cycle consisted of 30 min heating followed by 60 min cooling; reference capacity-based state of health (SOH) was assessed every 50 cycles up to 400 cycles. A maximum surface temperature rise of 14.3 K was achieved, with larger temperature rise at lower ambient temperature and lower excitation frequency. Capacity fade remained below approximately 1% for most conditions; however, at −20 °C and 10 mHz a pronounced SOH decrease to 87% was observed, indicating a critical operating regime. The results provide practical guidance for pulse-heating parameter selection and highlight the need for safeguards and further diagnostics in extreme low-frequency excitation at very low temperatures. This heating approach is particularly suitable for simpler battery-electric applications without thermal management, such as e-bikes or power tools. However, it may also be relevant for applications with existing thermal management systems, as it simplifies battery pack design.
Raiber, StefanAllmendinger, FrankDegler, DavidParschau, Anke
This document establishes standardized dimensional cell geometry, performance-test procedures, and reporting requirements for secondary (i.e., rechargeable) pouch cells used in Group 1 sUAV. It defines reference geometries, test conditions, and uniform data formats to allow direct comparison of pouch-cell performance across manufacturers and to improve interoperability within the sUAV ecosystem.
Battery Cell Size Standardization Committee
Thermal safety in lithium-ion batteries is a critical aspect due to their increasing use in energy storage systems and electric vehicles. To investigate thermal abuse conditions, numerous studies employ specialized equipment to accurately measure physical variables during thermal runaway events. However, such tests typically require robust equipment which limit their availability in conventional laboratory environments. In this context, the present study proposes and evaluates an experimental methodology based on the use of a climate chamber combined with an instrumented reduced-volume container, to reproduce severe external heating conditions. The thermal behavior and gas emissions associated with thermal runaway events were characterized in six cylindrical lithium-ion batteries of two different chemistries. Six cylindrical cells with NMC and NCA cathode chemistries were subjected to thermal abuse tests. In addition, gaseous emissions and mass loss were quantified after the event. Based on these tests, the results showed that NMC cells reached a higher average maximum surface temperature of 1086°C, whereas NCA cells exhibited the highest pressure values, with an average of 24.64 bar. Gas emissions presented high concentrations of CO and CO₂, reaching values of up to 381,555 ppm. Furthermore, both chemistries experienced a mass loss exceeding 50% after the test. Overall, the results indicate that both cell types exhibit similar behavior in terms of gas emissions, while NMC cells show greater thermal severity during the exothermic event. This work demonstrates that a system based on a climate chamber combined with an instrumented container is capable of reproducing severe thermal runaway conditions comparable to those achieved in specialized abuse-testing facilities, enabling the simultaneous characterization of temperature, pressure, and gas emissions in lithium-ion cells.
Penagos Vásquez, Diego AlejandroMarco-Gimeno, JavierMonsalve-Serrano, JavierGarcia, AntonioPerez Balastegui, Jose
Researchers at Columbia Engineering have developed a new gel electrolyte that both improves the lifetime and safety of anode-free lithium batteries, an emerging battery architecture that could dramatically boost energy density while simplifying manufacturing. Although such design promises higher energy density and lower cost, the approach has long been plagued by short battery life and safety concerns caused by unstable lithium plating and parasitic reactions at the electrode-electrolyte interface.
As the electric vehicle era enters full scale, demand is increasing for batteries that can travel farther and last longer. Lithium-metal batteries have been attracting attention as a next-generation technology capable of surpassing the capacity limits of existing lithium-ion batteries. However, during the charging process, needle-shaped crystals called “dendrites” grow, shortening battery life and increasing the risk of fire, which has been identified as the biggest obstacle to commercialization. A Korean research team has developed a key technology that can solve this challenge.
This paper presents a multi-physics modeling approach for a hybrid propulsion system designed for High-Altitude Long-Endurance Unmanned Aerial Vehicles (HALE UAVs), integrating solid oxide fuel cells (SOFCs), lithium-ion batteries, and a jet engine. A dynamic model was developed to analyze the coupled characteristics of pressure, temperature, and power under steady-state conditions. Simulation results demonstrate that the internally integrated system achieves efficient fuel and waste heat recovery, delivering a net power output of 300–700 kW, sufficient to meet the operational demands of HALE UAVs. Key innovations include a heat exchanger maintaining SOFC stack inlet temperatures above 850 K for optimal performance and a compressor-fan subsystem enhancing gas compression efficiency. Experimental validation confirmed the accuracy of the SOFC model, with simulated electrical characteristics aligning closely with empirical data. The proposed hybrid system addresses limitations in specific power and transient response while improving energy density, offering a viable solution for long-endurance flight missions. This study provides a foundational platform for advancing hybrid propulsion technologies in aviation.
Zhang, LinZhang, DiZhao, LuluLi, Xi
To enhance the safety and efficiency of power batteries for new energy vehicles, a high-fidelity thermal management simulation model for lithium-ion batteries was established using a multi-scale coupled approach encompassing "cell-module-pack" levels. Charge/discharge experiments within the 15–45°C temperature range and under various State of Charge (SOC) conditions were conducted to obtain cell characteristic parameters. A second-order RC equivalent circuit model was constructed and validated. A three-dimensional thermal model of the battery pack was developed using the NX and STAR-CCM+software platforms and validated through high/low-temperature humidity tests. Results indicate that simulation errors for battery pack temperature and cooling line pressure were both below 3%. The model accurately simulates thermal behavior from microscopic cell characteristics to macroscopic battery pack dynamics.
Luo, ZhaoyangSong, Lan
A full lithium-ion battery (LIB) pack has hundreds to thousands of cells, coolant flow lines and channels, and channel bends to control cell temperature within its operating window and minimize cell internal resistance, aging, and fire risk. A 75 kWh LIB pack has four modules, and each has 23–25 bricks. Two challenges in battery state predictions for hot and subzero temperatures are battery temperature (Tbatt ) and coolant flow within the whole pack. In this work, a 1D 75 kWh full-pack model with its thermal management system is developed using a holistic reverse-engineering method, which can predict Tbatt at any bricks/modules and inlet/outlet coolant flow characteristics. A Tesla Model Y equipped with dual e-motors is tested on an in-house state-of-the-art chassis dynamometer. The test data at V = 60–80 km/h, 100–150 A constant discharge, and Tbatt = −10°C to 40°C are used to develop the model. The 75 kWh pack model features 4000+ cylindrical cells (96S46P, Panasonic 21700-format), 20+ coolant lines (or plates, tubes), and 700+ flow channels. The model considers heat exchange from cells to the ambient air via coolant (water-glycol), coolant channel walls, adhesive bonding, trays, and cases. Four forced convective heat transfer coefficient correlations (α) from the coolant to the walls are used to predict coolant outlet temperature (T cool, out ) and Tbatt at different bricks. Three coolant flow losses correlations (K) due to pipe friction, and pipe bends are used to predict the coolant pressure drop ∆Pcool across the pack. Optimal α and K correlations are identified using the fully validated pack model, and the transient temperatures at any cell in bricks and the inlet/outlet coolant flow characteristics are well predicted with over 90% accuracy. This work provides guidelines for selecting optimal α and K correlations to develop any 1D fully liquid-based battery pack models for all-weather driving.
Sok, RatnakKusaka, Jin
Electric vertical takeoff and landing aircraft impose significantly higher electrochemical and thermal demands on Li-ion batteries than conventional electric vehicles, yet publicly available aging datasets for this application remain limited in applicability, cell technology, and statistical robustness. This study experimentally characterizes the degradation behavior of state-of-the-art Molicel P45B 21700 cells under realistic Urban Air Mobility operating conditions involving high power demand, rapid turnaround, and repeated cycling. Eight cells are subjected to over 3,000 cycles using a fast constant-current charging protocol and a multi-segment constant-power discharge profile. The discharge profile is derived from a representative 7000-lb winged eVTOL with a 20-mile range, requiring normalized power rates of 6.4E during takeoff and landing and 1.6E during cruise. Periodic Reference Performance Tests are conducted to track capacity fade, internal resistance evolution, and energy efficiency. The cells retained over 90% of their initial capacity after 3,270 cycles, while total energy efficiency remained stable at 91%, comprising impedance and hysteresis-driven components of approximately 94% and 97%, respectively. Direct current internal resistance exhibited an initial decrease before stabilizing, yet total discharged capacity increased from 1.81 Ah to 1.84 Ah, indicating aging-driven polarization effects not captured by DCIR. A zero-order equivalent circuit model underpredicts discharged capacity by approximately 4% for fresh cells, increasing to nearly 6% at cycle 3,270 due to unmodeled time-dependent polarization effects. These results demonstrate that while modern Li-ion cells exhibit strong durability under repetitive high-power usage, the accuracy of battery performance prediction is strongly dependent on dynamic impedance effects beyond conventional DCIR-based models.
Halder, AnubhavGandhi, Farhan
From material selection to system-level performance Transportation's shift toward electric power - whether cars, planes, or big trucks - has made battery engineering a pretty wild, multidisciplinary puzzle. It's not just about coming up with a prototype anymore. To hit the right mix of energy density, power, safety, cost, and longevity, teams need to rethink how they design these systems. Enter simulation and modeling tools. Engineers now use these digital tools to blend electrochemistry, thermal management, materials science, and whole-system design. Instead of jumping straight to building, they try out battery ideas in the virtual world first, speeding up how long it takes to figure out what works and what doesn't and boosting the reliability of those batteries in the real world. Today, battery simulation spans multiple scales from the behavior of active materials within electrodes to the thermal dynamics of the entire battery pack as integrated into a vehicle.
Gupta, Pradyumna (Prady)
As global demand for sustainable energy solutions increases, there is a push to develop alternatives to lithium-ion batteries, which face limitations in cost, resource availability, and safety. In particular, multivalent-ion batteries based on magnesium, calcium, zinc, and aluminum have emerged as promising candidates due to their ability to transfer multiple electrons per ion, offering higher volumetric energy density and greater material abundance. This review examines recent advances in electrode and electrolyte development for these systems, highlighting cathode innovations such as cobalt sulfides for magnesium, NASICON-type and redox-coupled materials for calcium, molybdenum trioxide frameworks for zinc, and organic and composite electrodes for aluminum. Electrolyte research has produced improved ionic transport and stability through solvation tuning, hybrid and polymer systems, and deep eutectic solvents. Interfacial engineering is identified as a key enabler for enhancing reversibility, dendrite suppression, and long-term cycling stability. A comparative analysis of the different chemistries found that zinc-ion systems are closest to commercial deployment, aluminum-ion batteries are advancing for grid and flexible devices, and magnesium and calcium-ion batteries hold long-term potential for high-energy applications. The study concludes with future research directions emphasizing solvation control, sustainable materials, and intelligent diagnostics to achieve scalable multivalent battery technologies.
Mittal, VikramShah, RajeshLi, Ivy
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 the functional performance of a system. The noise factors capture material variability and manufacturing tolerances affecting thermal properties. Output responses included the maximum cell temperature Versus time, thermal propagation time to adjacent cells, and total propagation duration across the module, measured in minutes. The validated 1D GT-SUITE model shows strong correlation with experimental data, confirming its reliability to predict thermal propagation time and supporting safer, thermally optimized battery pack designs. The validated model can be integrated into system (battery pack) level 1D thermal simulations, offering a calibrated model for future pack level propagation studies and supporting the development of safer, thermally optimized battery architectures.
Dixit, ManishRaja, VinayakGudiyella, Soumya
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-foam-based compression pads to meet the unique challenges of various battery formats and chemistries. We first characterize the fundamental mechanical properties of these pads under a range of conditions, such as different compression speeds and temperatures, that are directly relevant to realistic battery applications. Additionally, we demonstrate the pad’s long-term mechanical resilience by evaluating their performance over thousands of charge-discharge cycles at different operating temperatures, confirming their ability to maintain consistent pressure over a long time. Finally, we present advanced modeling and simulation approaches for compression pads. These predictive models are crucial tools to accurately forecast mechanical behavior and explore the design space virtually, accelerating the development of optimized solutions of compression pad for battery pack applications.
Deng, WeilinGunashekar, Subhashini
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 approach. The simulation work was carried out using the GT-SUITE software. This study improves battery thermal management strategies by enhancing predictive accuracy and optimizing simulation frameworks for real-world applications. It minimizes overheating risks in practical scenarios, such as prolonged exposure to high ambient temperatures.
Nayaka, Sateesh KumarDixit, ManishGudiyella, 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 variation. The results provide a data-driven framework for designing robust cooling systems that mitigate the risks of localized hotspots and thermal runaway, thereby enhancing the durability and safety of EV battery packs.
El-Sharkawy, AlaaAsar, MonaSerpento, StanSheta, Mai
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-robustness EV thermal protection systems under uncertainty and extreme boundary conditions.
El-Sharkawy, AlaaAsar, MonaTaha, NahlaSheta, Mai
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 mechanical characteristics and battery degradation. Based on the DFDV curves, a novel mechanical feature-based SOH prediction method is proposed. The study found that rapidly changing forces generate a new characteristic peak in the DFDV curves, whose positions and amplitudes exhibit strong linear relationships with SOH. This mechanical approach provides enhanced sensitivity to internal structural changes and degradation processes compared to traditional capacity-based methods. And we point out the superiority and application prospects of this method. This research provides a groundbreaking paradigm for in-situ safety monitoring of batteries using mechanical signals, offering substantial implications for enhancing early warning capabilities under abusive operating conditions and optimizing the safety design of battery systems in electric vehicles and energy storage application.
Niu, ZhiceZhang, ShanXia, Yong
Modern battery management systems have a critical need for highly accurate battery terminal voltage models, which are a key component of algorithms that estimate or predict power capability, range, temperature, and other factors. While electro-chemical and equivalent circuit models are widely used for this purpose, they typically struggle to model efficiently the complex, non-linear dynamics inherent in real-world battery operation. This study proposes a robust, data-driven approach for terminal voltage estimation using a feed-forward neural network (FNN) machine learning model. Characterization and drive cycle tests were performed on a 60 Ah prismatic cell from a Fiat 500e at temperatures ranging from -20 °C to 40 °C. The collected data was used to train and test the models, with model error reported for HWFET, UDDS, US06, and LA92 cycles. Model size was swept between around 100 and 35,000 trainable parameters for an FNN with three inputs – unfiltered power, state of charge, and temperature - to select the best size for the baseline model. Next, low pass filters were applied to measured power and used as additional inputs. The two filter frequencies (ranging from 0.1–200 mHz) resulting in the lowest error were selected. This five-input model was shown to have 42% lower error compared to the baseline (no filters) model, with an average error of just 9.8 mV which is 30 to 50% lower than values reported in literature for equivalent circuit and other machine learning battery voltage models.
Dehury, BiswanathNahidmobarakeh, LucasMohammed, Kamran AhmedPanchal, SatyamGross, OliverKollmeyer, Phillip
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, temperature, and current on plating potential. From these results, an empirical separable closed-form function was derived that is continuous, differentiable, and computationally efficient, enabling onboard real-time implementation. Validation against the high-fidelity model demonstrated strong agreement, with adjusted R2 > 0.99 and RMSE on the order of 1–3 A across the domain. Co-simulation confirmed that the model enforces plating-free charging across cold to hot conditions, while pulse-current tests showed that the continuous limits remain conservative under transient operation. In addition, charge-time analysis revealed an exponential dependence on temperature, leading to a compact correlation for estimating charge durations under varying thermal environments. Unlike detailed electrochemical models, this framework provides a practical, validated function for defining plating-free charging envelopes, directly suited for integration into BMS and supervisory charging strategies.
Sundar, AnirudhGhate, AtharvaZhu, QilunPrucka, RobertBarron, MorganFigueroa-Santos, Miriam
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 evolution was quantified after each block. The result shows battery aging can be divided into three stages: a decelerated, steady, and accelerated aging stage. The degradation of LFP cells is dominated by loss of lithium inventory (LLI), and elevated temperature accelerates the degradation. By combining differential voltage analysis (DVA), direct current internal resistance, electrochemical impedance spectroscopy, and ultrasonic testing, we found that under 45°C free condition, accelerated aging is consistent with intensified SEI growth and electrolyte decomposition, accompanied by increased LLI, gas-generation, and increased resistance. These signals emerge earlier than the apparent capacity divergence and may serve as early indicators for predicting the onset of rapid degradation. Appropriate constrain mitigates aging, and its influence becomes more pronounced when using higher-rate RPTs. At 25°C, high-rate RPTs exhibit an apparent capacity recovery. DVA analyses indicate the recovery originates from gradual activation of lithium. Overall, these findings illustrate and explain the degradation characteristics and capacity recovery phenomenon, providing a reference for connecting laboratory standard tests with on-board BMS capacity estimation.
Zhang, ShanNiu, ZhiceXia, 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 25 °C in both battery heating and cooling modes. The predicted thermal and electrical performance of the BEV is well validated by test data. Then, the complete vehicle model is used to compare the thermal performances of the BEV under cabin heating and cooling modes for various transient driving cycles. The simulated results show that using an external cabin air circulation model can reduce the battery energy consumption and dissipated heat by 9.9% and 2.4%, respectively. This calibrated virtual test rig can be used to evaluate a new HVAC system.
Sok, RatnakKusaka, Jin
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 cycles. Simulation predictions exhibit strong correlation with experimental measurements within ±5%, confirming model fidelity. The findings indicate that rare earth-free propulsion systems and LFP batteries can meet EV performance and safety requirements while significantly reducing reliance on critical materials, supporting sustainable EV development.
Saraswat, ShubhamVishe, Prashant
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 study proposes a performance-based material selection strategy. This strategy is critical for extending the safe egress period mandated by standards such as UN ECE R.100 and GB 38031-2020 and is increasingly vital as safety benchmarks evolve toward longer durations. The synthesis of high-inertia materials with traditional fire-resistant insulation provides a multi-layered defense, enhancing passenger protection by functionally delaying the progression of heat, fire, and hazardous emissions into the occupied cabin space.
El-Sharkawy, AlaaTaha, NahlaAsar, MonaSheta, Mai
State-of-charge (SOC) operating windows strongly affect lithium-ion battery degradation, while conventional aging tests require long durations to establish trends. Coulombic efficiency (CE), defined as the discharge-to-charge capacity ratio, provides an early-life diagnostic for parasitic reactions and long-term performance prediction. Eight 21700 NMC cells were cycled at 25 °C across four SOC windows (0–100%, 20–80%, 40–60%, and 80–100%) using conventional and ultra-high precision cyclers. Capacity retention, resistance growth, and CE were evaluated to quantify depth-of-discharge (DOD) effects. A non-linear aging behavior was observed, with accelerated initial capacity loss followed by stabilization. The 0–100% SOC window exhibited the highest degradation, with ~9% capacity loss per 100 EFC initially, stabilizing to ~3.3% per 100 EFC, corresponding to a projected 80% SOH life of ~440 cycles. In contrast, the 40–60% window showed stabilized fade of only 2.0% per 100 EFC, yielding a projected life of ~2670 cycles (~6× improvement). CE stabilized near unity (≈0.998–1.000) within the first several cycles. Small deviations (e.g., 99.95% vs. 99.99%) revealed irreversible side reactions. Cumulative inefficiency after 30 cycles was lowest for partial SOC windows and highest for full-range cycling, correlating strongly with long-term degradation trends.
Hussein, HudaArora, DipanPanchal, SatyamGross, OliverEmadi, AliKollmeyer, Phillip
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 component level, cathode and anode electrodes, separator, and cell pouch laminate were tested for their thermal properties, including heat capacity, thermal conductivity, and melting points. This data is essential to modeling heat generation and dissipation in the detailed battery cell model. Mechanical behavior of these component materials was tested to understand structural integrity and failure modes. Electrical conductivity of cell component materials was also characterized. These experimentally measured properties and derived parameters may be integrated into a representative multi-physics battery cell model. By providing detailed characterization of a commercial lithium-ion EV cell, this research provides an experimental framework for developing both macro and detailed cell computational models needed for safety design assessments of EV battery systems.
Challa, VidyuRostami-Angas, Masoudkong, KevinWang, LeyuReichert, RudolfKan, Cing-Dao
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
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 thermal and electrical performance is recorded at 60 km/h under cold conditions and during transient driving in summer. The pack is based on the 2RC equivalent circuit model, reduced from the P2D-based NCA/Gr-SiOx Li-ion cell, to accelerate simulation times at the pack and vehicle levels. The approach to identifying ∆P and heat transfer correlations are discussed, with pack model validations under coolant temperatures ranging from 0 to 40 °C and coolant flow rates of 4 to 14 L/min. The thermal and electrical performances (voltage, SOC, ∆P, and temperatures of the coolant, bricks, and modules) of the high-fidelity battery pack model are validated against vehicle test data at 60 km/h driving (ambient temperature Ta = -10 °C) and repeated FTP+HWFET cycle (Ta = 30°C). The whole pack model achieves an average accuracy of 90%, and this work can serve as a guideline for designing battery packs with their BTMS using MBD.
Sok, RatnakKusaka, Jin
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, taxes, and materials. Such situation can be reversed with changes in economies of scale, tax incentives, and automation in the recycling process. Among different battery types and recycling pathways, the resource recycling of NMC 811 batteries is most likely to achieve a competitive advantage through policy support and economies of scale. In contrast, for the resource recycling of LFP batteries, legal companies are unlikely to surpass illegal vendors across all scenarios. To ensure sustainable development of the battery recycling industry, critical strategies should be comprehensively employed, alongside measures such as raising entry barriers, regulating recycling networks, and strengthening supervision to crack down on illegal vendors.
Du, ShilongLi, HaoyangDou, HaoHao, Han
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 use, while hybrid systems range from 2–4 kWh/km depending on fuel and secondary power unit. Control strategies that enable deeper cycling of the traction battery reduce fuel consumption by 7–18 %, with further savings possible from larger battery or genset capacities. Well-to-wheel greenhouse gas emissions vary widely: from near-zero for renewable fuels and clean electricity mixes to over 1,000 gCO2/kWh for fossil-based options. Lifecycle cost analysis indicates that while fuel may represent up to 25 % of total costs, track and station fees dominate operational expenses. Autonomous operation could eliminate oboard staffing costs, amounting to 25–35 %.
Ahrling, ChristofferTuner, MartinGainey, BrianTorkiharchegani, AmirScharmach, MarcelHertel, BenediktAlaküla, Mats
This research demonstrates a new way to make carbon-based battery materials much safer, longer lasting, and more powerful by fundamentally redesigning how fullerene molecules are connected. Today’s lithium-ion batteries rely mainly on graphite, which limits fastcharging speed and poses safety risks due to lithium plating. These research findings mean progress toward safer electric vehicles, longer-lasting consumer electronics, and more reliable renewable-energy storage.
A joint research team led by Professor Soojin Park and Dr. Dong-Yeob Han of the Department of Chemistry at POSTECH, together with Professor Nam-Soon Choi and Dr. Saehun Kim of KAIST, and Professor Tae Kyung Lee and researcher Junsu Son of Gyeongsang National University, has successfully achieved a volumetric energy density of 1270 Wh/L in an anode-free lithium metal battery. This value is nearly twice that of lithium-ion batteries currently used in electric vehicles, which typically deliver around 650 Wh/L. The achievement was published in Advanced Materials.
Asolid, rather than liquid, electrolyte between the opposite electrodes of a battery should, in theory, enable a rechargeable lithium metal battery that is safer, packs much more energy, and charges considerably faster than the lithium-ion batteries commercially available today. For decades, scientists and engineers have explored several paths to realize the great promise of lithium metal batteries. A major problem with the solid, crystalline electrolytes under study has been the formation of microscopic cracks that grow during use until the battery fails.
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.
Researchers at McGill University have made a major breakthrough in advancing all-solid-state lithium batteries, a promising next-generation technology for electric vehicle (EV) batteries.
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 integrated through a fusion network. This model was validated on the CSIE dataset, and the results showed that its performance was superior to that of the single-modal method. At 0.5C discharge rate, the average RMSE of the fusion model was 0.34%, the MAPE was 0.31%, and the R2 reached 0.9937. In addition, this method demonstrates excellent robustness at different discharge rates. Even at a high discharge rate like 3C, it can still maintain high accuracy.
Li, XiaobinHe, NingYang, Fangfang
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 outside the original design domain, with prediction errors under 5% in 93.1% of test cases. The results demonstrate the effectiveness of integrating machine learning with physics-based modeling for reliable, data-efficient prediction of battery behavior under abusive conditions, offering new insights into battery safety design and real-time risk assessment.
Wan, ChengZhan, ZhenfeiChen, Qiuren
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 features for model training. A hybrid deep learning framework integrating the sparrow search algorithm (SSA), convolutional neural networks (CNN), gated recurrent units (GRU), and an attention mechanism was developed. SSA automatically optimize model hyperparameters, mitigating the overfitting risks, while the attention mechanism highlighted informative frequency-domain features, reducing manual feature engineering and enhancing prediction accuracy. Results show excellent performance: for LFP cells, the root-mean-square error (RMSE) and mean absolute error (MAE) are 0.24% and 0.19%, respectively, with a coefficient of determination (R2) of 98.96%; for NCM cells, the RMSE and MAE are 0.99% and 0.88%, with R2 of 97.97%. On the mixed-material dataset, the RMSE, MAE, and R2 reach 0.79%, 0.67%, and 97.84%. These results confirm that the proposed method maintains high accuracy across different cathode chemistries, while significantly reducing testing and modeling costs. The approach shows strong potential for large-scale, automated screening and classification of retired LIBs in practical second-life applications.
Hou, ZhengyuLuan, WeilingSun, ChangzhengChen, Ying
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 depletion delayed onset and reduced flame intensity. Mass loss increased with decreasing SOH, while the effective heat of combustion remained relatively stable. Thus, salt spray intrusion can cause damage to the internal structure of the battery, inhibit electrochemical reactions, and significantly reduce the operational function, thereby bringing potential safety risks.
Tao, LiyanyuShi, XinyuanYang, QinyuanLiu, Jiahao
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/negative current collector and high thermal stable separator with aramid coating layers. This design improved separator breaking point by 84 °C while reduced current collector melting point by 900 °C, thereby it makes current collector shrinks earlier than separator break, this avoids internal short circuit by detaching cathode and anode coating layer when the separator is still in place. The design was applied in high nickel LiNi0.91Co0.03Mn0.05Al0.01O2 cathode and graphite anode chemistry system with a thick coated electrode (4 mAh cm-2, 21 mg cm-2 per coating side). Pouch cell with 5 Ah nominal capacity was fabricated in this cell design. The electrochemical benefits and drawbacks by adopting positive or negative current collectors or both were evaluated, including the affection in cycling stability, cell resistance and rate performance. Nail penetration and thermal ramping was also adopted to evaluate the safety benefit of the design. The cell shows comparable electrochemical performance and improved cell safety after composite current collector and high thermal stable separator adoption.
Liu, JingyuanLu, YongLiu, Haijing
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-discharge cycles and provides multidimensional information throughout thermal runaway (TR) processes, enabling early TR warning and supporting safety-oriented battery design.
Wang, ZiweiLi, ZhenglinGao, YangXuan, Fuzhen
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