Browse Topic: Lithium-ion batteries

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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
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
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
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
This article presents a novel finite element modeling approach to predict the mechanical response of jellyrolls in large-scale explicit crash simulations up to the experimental occurrence of internal short-circuit. The proposed simplified layered model embeds membrane elements within a solid element mesh to improve the prediction in load cases dominated by the buckling and sliding of the jellyroll’s layered structure. The model was validated against experimental results from in-plane, out-of-plane, and bending tests on jellyroll samples extracted from prismatic lithium-ion cells. The experimental results confirmed the jellyroll’s high compressibility under out-of-plane loads and its behavior as a collection of unconnected layers under in-plane and bending loading. Compared to the widely used crushable foam model, the simplified layered model offered additional flexibility, especially for in-plane and bending load cases. Additionally, it meets critical time increment requirements for explicit analysis and requires a limited number of calibration tests. These results highlight the model’s potential to improve the prediction of the jellyroll’s mechanical behavior in large-scale simulations.
Cioni, DanieleMorin, DavidStrating, ArjanKizio, StephanCostas, Miguel
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
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
This article surveys the most recent data-driven methods of lithium-ion (Li-ion) battery state of health (SOH) estimation methods and dataset resources utilized in electrified vehicles (EV) and their potential adoption for automotive battery management systems. These include regression-based models, ensemble learners, deep neural networks, and physics-informed hybrid methods. The review describes estimation methods found in articles published between 2023 and 2025, and investigates their differences in terms of estimation accuracy, data requirement, interpretability, and real-time deployment ability. The article traverses the dataset space, focusing on laboratory aging datasets, vehicle field–based datasets, telematics-derived records, and synthetic or augmented datasets, to underline that model performance in the estimation of SOH cannot be disentangled from the quality of the data, the operating coverage, and the transfer conditions. Apart from the model design, this work reviews the large-scale estimation pipeline, which involves preprocessing under sensor noise and irregular timestamps, feature extraction from incremental capacity, differential voltage, relaxation response and impedance-related indicators, and uncertainty handling for diagnostics and safety-based decision support. Practical constraints to the deployment of embedded BMS are covered. Such as ECU memory and computing limits, communication overhead, calibration effort, update approach, and functional-safety requirements. The review determines that the distance between laboratory validation and field robustness is large raising a need for more work in this area and also, that domain adaptation, federated learning, and improving benchmarking practice turn out to be promising directions for improving generalization and reproducibility. The article concludes that future advances in automotive SOH estimation will not only rely on better learning algorithms but also on improvement in the availability of realistic and field representative data, the application of robust evaluation mechanisms, and methods that are developed under real BMS constraints.
Nyachionjeka, KumbirayiBayoumi, Ehab H.E.
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
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
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
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
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
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
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 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
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
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
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 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
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
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
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
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
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
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.
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 provides an effective solution for the low-temperature, low state of charge (SOC) application scenario in electric vehicles.
Xiao, YuechanHuang, XinrongWu, ZeZhang, YipuMeng, Jinhao
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 algorithm significantly outperforms traditional electrical signal-based methods, with cumulative SOC estimation errors not exceeding 2.2%. The approach exhibits higher accuracy, improved environmental adaptability, and enhanced robustness. This research confirms the feasibility and effectiveness of using expansion force as a non-electrical quantity for SOC estimation, providing a new perspective for high-precision battery state assessment.
Du, JinqiaoRao, BoTian, JieWu, YizengXu, HaomingJiang, Jiuchun
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 battery safety abuse laboratories, aiming to provide useful references for the healthy and orderly development of the new energy industry and the practice of social responsibility from a practical perspective.
Ren, GaohuiLiu, LeiJiang, ChenglongSun, ZhipengChen, Liduo
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 characteristics, with standard deviations in onset time generally below 7 s, the range is less than 15 s, indicating improved repeatability. The heating wire-triggered method demonstrates stable and reliable repeatability under different power levels and aging states. This study provides critical data and technical references for the standardization of lithium-ion battery thermal runaway testing, offering valuable engineering guidance for battery safety assessment.
Wang, JiaYan, HongtaoZhang, YuemengLin, ChunjingLao, Li
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 Expectation–Maximization (EM) algorithm. Conditional Probability Tables (CPTs) are recalculated through Maximum Likelihood Estimation (MLE) with logical relationships to achieve accurate and adaptive fault probability estimation. A multi-feature fusion indicator, the State Severity Indicator (SSI), is further introduced to evaluate system health in real time. A qualitative comparison with representative fault modeling and detection approaches—including Bayesian Network, FTA-DBN, and various machine learning methods—shows that the proposed BFPN offers a well-balanced trade-off between interpretability and real-time performance. Simulation experiments under both single- and multiple-fault scenarios demonstrate that the proposed framework accurately detects typical fault events and provides early warnings before fault escalation. Under complex coupled fault conditions, it effectively captures fault interactions and predicts cascading failures across subsystems and the overall BESS, showing strong robustness and diagnostic capability for real-time reliability assessment in modern energy storage systems.
Yang, ZhanChen, XiaoboZheng, RuixiangLi, Mian
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
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 discriminative features during the charging process. Subsequently, a two-layer multilayer perceptron classifier produces the category predictions. Experimental results show that the proposed method achieves a classification accuracy of 95.33%, significantly outperforming conventional approaches. Further analysis reveals that the 1D CNN module improves accuracy by approximately 4% by providing efficient feature inputs for global modeling; charging data, compared to discharging data, offer richer information, boosting accuracy by 16.67%; incorporating temporal information under non-uniform sampling enhances time-series modeling effectiveness, yielding a 2.67% accuracy gain; and using only the first 4–12 minutes of charging data can still achieve 92.67% accuracy, indicating that the early charging phase carries high discriminative value. This study provides an effective technical solution for sorting retired batteries and offers valuable insights for advancing the battery recycling industry.
Xiao, HualongLuo, GangWang, LiLin, MingqiangWu, Ji
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 suppressing lithium plating. These conditions improve the uniformity of lithium deposition while alleviating concentration gradients of lithium ions, thus offering valuable insights for battery material design and practical applications. Furthermore, optimized charging protocols are developed on the basis of conventional strategies and their associated impacts on battery behavior. Two novel approaches—the group-based optimized charging protocol and the adaptive optimization-based charging protocol—are proposed by dynamically adjusting the charging rate according to real-time electrochemical states. Validation on the developed electrochemical–thermal model confirms that the proposed protocols can achieve high-rate charging without inducing lithium plating. As a result, charging time is significantly reduced while ensuring safety and reliability. Overall, this research not only provides a comprehensive methodology for modeling and parameter identification but also offers practical strategies for protocol optimization. With solid-state batteries regarded as a promising future technology, the present work provides a potential basis for their advancement.
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-series high-nickel CVD silicon-carbon anode semi-solid battery cells as an example, this paper designs an integrated electro-thermal simulation and optimization scheme for the interior of electric vehicles, as well as an external heat exchange device capable of efficiently exchanging heat with the interior. By establishing a 3D thermal model of the battery, conducting a series of simulations, and comparing the results with the corresponding experimental data, this study not only obtains a relatively comprehensive 3D thermal model and thermal simulation process, but also develops an optimized thermal management solution for the battery module.
Wu, JiayiZheng, BowenKang, MengranZhan, WenweiQi, JiYi, Yong
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
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
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 is quantified through scale-weighted fusion and normalization, enabling accurate evaluation across diverse abnormal modes. Finally, The diagnostic performance of the proposed method is comprehensively validated through three sets of simulation experiments and real-vehicle data collected under realistic operating conditions. The results demonstrate that the proposed method accurately identifies both single-point and clustered anomalies, corresponds closely with actual fault conditions and exhibiting strong generalization capability. In real vehicle validation, the method achieves 95.79% accuracy, 100% recall, and a 93.3% F1 score in abnormal detection tasks. Furthermore, It demonstrates robustness and interpretability, enabling multi-type abnormal detection and fault severity evaluation without reliance on extensive fault datasets, thereby offering high suitablility for online monitoring and early warning in actual Battery Management Systems.
Wei, FuxingYang, LibingWang, ZongleiXia, XueleiShen, JiangweiChen, Zheng
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). This pattern is largely driven by the reversible volume changes in the electrodes as lithium ions are alternately inserted (intercalation) and removed (deintercalation) during electrochemical cycling of the cells. At 0 °C, slower ion transport and reaction kinetics promoted lithium plating, causing irreversible anode expansion and a continuous rise in surface pressure. Concurrently, the depletion of active lithium diminished the electrode’s maximum achievable state of charge (SOC). This limitation curtailed the degree of electrode expansion and contraction throughout charge–discharge cycles, resulting in a decrease in the amplitude of pressure fluctuations on the battery surface during cycling. Numerical simulations confirmed that lithium plating and SOC degradation collectively shaped the mechanical response at low temperatures. The proposed model accurately replicates experimental pressure evolution and distinguishes between reversible and irreversible contributions to volume changes. This work reveals how temperature and mechanical loading jointly regulate surface pressure and capacity retention, offering insights relevant to battery pack design and the optimization of low-temperature performance.
Du, YingyueChen, YingLuan, WeilingChen, Haofeng
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 fusion. In the first layer, decentralized voltage data are standardized using the 3σ rule to identify potentially anomalous batteries. In the secondary analysis phase, a sliding time-window mechanism is adopted to dynamically capture voltage sequences. Within each window, voltage variations are calculated along both vertical and horizontal directions, and statistical metrics, including mean, standard deviation, range, and increment are derived. Principal component analysis is then applied to extract key features, and battery anomalies are evaluated and confirmed based on the maximum LOF score. Experimental validation using datasets from vehicles that experienced thermal runaway events, along with data from 1,000 normal vehicles, demonstrates that the proposed method significantly improves the accuracy of battery fault detection. It also provides early warnings up to 17 days prior to the occurrence of thermal runaway.
Gao, ZhengpengGao, PingpingChang, PenghuiLiu, GangWu, Ji
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