Nickel-rich cathode materials (LiNi1−x−yCoxMnyO2, NCM) are regarded as one of the most promising cathode candidates for solid-state batteries (SSBs) due to their high energy density and low cost. However, during electrochemical cycling, continuous lithium-ion insertion/extraction generates diffusion-induced stress (DIS) that fractures particles and accelerates capacity fade. Furthermore, NCM particles are subjected to external pressure during manufacturing, and inherent process non-uniformities result in varying pressurized coverage (defined as the ratio of covered area of active materials with solid-state electrolytes), which significantly influence particle cracking behavior. Based on chemo-mechanical coupling models, extensive work have investigated particle cracking behavior during charge-discharge processes. While limited research addressing crack evolution under concurrent electrochemical loading and external pressure. Thus, we developed a chemo-mechanical coupling model with globally embedded cohesive elements within polycrystalline NCM (PC-NCM) particles to simulate fracture behavior during single charge-discharge cycles. The effects of external pressure, charge/discharge C-rate and pressurized coverage are evaluated. Simulations demonstrate that external pressure significantly mitigates particle cracking. Notably, this crack-suppression effect intensifies with reduced pressurized coverage. This work provides critical insights into fracture mechanisms of NCM cathodes materials, offering fundamental guidance for electrode design optimization.

Lithium-ion batteries (LIBs) have drawn substantial scientific interest because of their impressive energy storage capabilities and long-term operational stability. In recent years, new battery material systems have emerged, among which LMFP (LiMnxFe1−xPO4) is regarded as a promising candidate for future battery development, combining high energy density with enhanced safety. However, research on the thermal runaway (TR) behavior of LMFP-based batteries remains scarce, leaving their cell-level safety unverified. This study modifies the conventional state of charge (SOC) classification method by measuring the oxidation state of cathode materials at specific voltages. By testing the thermal runaway (TR) temperature and gas release characteristics of LMFP hybrid batteries under different voltage states, it reveals the influence of cathode oxidation state on TR behavior. The results demonstrate that when the NCM (LiNi₀.₅Co₀.₂Mn₀.₃O₂) component remains unoxidized, the battery does not undergo complete TR. The self-heating temperature (T₁) increases as the voltage decreases. However, comparative analysis of 3.9 V and 4.2 V batteries indicates that the oxidation state of NCM has the most significant impact on peak TR temperature. Furthermore, the severity of TR weakens with decreasing voltage, whereas the explosion hazard from vented gases intensifies, peaking at 3.5 V. This work fills a critical research gap in understanding the cell-level TR behavior of LMFP-based batteries, providing a theoretical foundation for their further optimization.

Lithium plating on graphite remains a critical failure pathway for rechargeable cells, yet how non-uniform mechanical loading biases intercalation and triggers plating in real time is not fully resolved. We develop an operando optical platform that combines in-situ microscopy with digital image correlation (DIC) to map deformation while a coupled electro-chemo-mechanical model links stress to both solid-state diffusion and the plating overpotential. Under uniform stack pressure, deposition appears spatially even. In contrast, localized pressure generates stress concentrations that accelerate lithiation near load edges and lithium plating at deformation hotspots. Quantitative image analysis showed that lithiation proceeded much faster in deformation hotspots, where lithium deposition preferentially occurred compared with unpressurized regions. The model reproduces these trends by showing that hydrostatic stress lowers the effective diffusion barrier in graphite and shifts the thermodynamic/electrokinetic balance for lithium deposition, establishing positive feedback between deformation and plating. Together, these results provide a mechanistic bridge from non-uniform pressure to non-uniform plating, and suggest practical strategies to suppress plating heterogeneity and enhance durability and safety during high-rate operation, such as mitigating local pressure and reinforcing coating mechanics.

This study employed a simple low-temperature hydrothermal and sintering method to prepare TiO2 (referred as T) heterostructure with different surface hydroxyl concentrations. Auger analysis indicated the binding energy peaks at 565 eV and 570 eV were corresponded to Cu2+ and Cu0, respectively, which proved that the heterostructure was TiO2/CuO/Cu (referred as TC). Meantime, XPS and FTIR indicated that altering the calcination temperature was beneficial for controlling the proportion of surface hydroxyl groups (-OH). Among the samples, TC calcined at 300 degrees C (denoted as TC-300 degrees C) exhibited the highest photocatalytic performance, delivering an H2 evolution rate of 182.68 mu mol g-1 h-1, which is 3.3 times and 1.9 times higher than those of T and the uncalcined TC sample, respectively. DFT calculations showed that the rate-determining step of the oxygen evolution reaction (OER) occurred at the *OH adsorption step. By comparing different hydroxyl adsorption sites on TiO2, CuO, and Cu, it was confirmed that hydroxyl groups adsorbed near Ti atoms were more favorable for improving catalytic activity. Additionally, the electron transfer process in TiO2/CuO/Cu heterostructure was proposed. This provided insights for the preparation of highly efficient catalysts that can promote photocatalytic water splitting half-reactions and improve hydrogen production efficiency.

Accurate long-cycle remaining useful life (RUL) prediction is critical for improving battery safety and reducing maintenance costs in electric vehicles and energy storage systems. A new iterative deep learning model based on bidirectional Mamba (Bi-Mamba) and physical constraints is proposed, which is optimized by the whale optimization algorithm (WOA), and only the early capacity data is used to predict the RUL of long-cycle battery. By integrating a denoising autoencoder (DAE) and positional encoding, the proposed model achieves enhanced robustness against noise and improved learning of temporal dependencies in battery capacity degradation trajectories. To effectively mitigate cumulative errors inherent in long-sequence forecasts, the framework applies a ten steps iterative prediction strategy. Validation on two public battery datasets (CALCE and MIT-Stanford) demonstrates that the proposed method attains significantly lower prediction errors (average relative errors of 1.60 % and 0.67 %, respectively) compared to advanced benchmark models including bidirectional long short-term memory (Bi-LSTM), bidirectional gated recurrent unit (Bi-GRU), and DeTransformer, especially in scenarios exhibiting capacity fluctuations phenomena. Experiments on different prediction starting points show that the model has good robustness, and the prediction accuracy gradually improves with the backward shift of the prediction starting point. The proposed approach highlights a practical and effective solution suitable for real-time deployment in battery management systems (BMS), laying foundations for further advancements in multi-sensor data fusion and embedded predictive maintenance implementations.

Achieving efficient and safe charging while effectively mitigating degradation induced by lithium plating is crucial for fully unleashing the performance and extending the lifespan of lithium-ion batteries. This study proposes a fast charging optimization control method that integrates anode potential awareness with a Transformer-based model predictive control (MPC) framework to address the complex multi-physics coupling constraints during fast charging. To enable multi-step prediction of the anode potential, 30 candidate features are initially constructed based on measurable parameters and their temporal derivatives. A robust feature set is then established by selecting 5 most discriminative input variables through correlation analysis and the Null Importance method. Subsequently, a Transformer-based state predictor is developed to perform accurate joint prediction of voltage, temperature, and anode potential under typical conditions, including dynamic loads and constant current charging. The root mean square errors (RMSE) for voltage, temperature, and anode potential predictions are 11.62 mV, 0.251 degrees C, and 3.67 mV, respectively. Building on the prediction model, an MPC framework is further developed using the particle swarm optimization (PSO) algorithm. This framework enables real-time optimization of the charging current trajectory under multi-dimensional safety constraints, including voltage upper limit, temperature upper limit, and anode potential lower limit. Results demonstrate that the proposed method can achieve a closed-loop integration of accurate state prediction and optimal control during charging, effectively suppressing constraint violations of key variables while balancing charging efficiency, cycle life, and safety. The method exhibits strong engineering adaptability and promising scalability for practical applications.

Accurate and rapid remaining useful life (RUL) prediction of batteries under various extreme conditions is crucial for battery management systems. However, existing methods often face challenges such as limited datasets under extreme conditions, high model complexity, and weak interpretability. Therefore, this paper proposes a hybrid framework based on pruning domain-adaptive convolutional neural networks (CNN) and long short-term memory (LSTM) to study RUL prediction under different fast-charging conditions using the MIT dataset. First, four voltage-related feature matrices are extracted. Using maximum mean discrepancy (MMD) constraints, the CNN-LSTM is trained with source domain and limited target domain data to align distributions. Neuron pruning is then applied to the fully connected layer to compress the model. Results demonstrate that under sparse target domain data, the domain adaptation approach achieves significantly lower prediction errors than fine-tuning. The pruned model maintains low prediction errors while reducing parameters by 42.32%. Further, an explainable algorithm quantifies regional data contributions to identify critical voltage intervals. Ultimately, precise predictions are achieved using only key data from the 2.9–3.2V range, fully demonstrating the method's efficiency. This study provides a lightweight and interpretable solution for cross-domain battery RUL prediction under fast-charging conditions.

Lithium-ion batteries (LIBs) are susceptible to accelerated degradation, swelling, and safety concerns under fast-charging conditions, largely due to gas generation and related side reactions. This work investigates the electrochemical degradation mechanisms and coupled gas generation/self-absorption behaviors of pouch cells aged by fast charging. Through a combination of electrochemical impedance spectroscopy (EIS) and distribution of relaxation times (DRT) analysis, we identify a pronounced impedance rise and capacity fade linked to lithium plating on the anode and interfacial layer thickening on both electrodes. The gas emitted caused by lithium plating during storing in fully discharged batteries is primarily H2 (∼70 %), with CO2 and hydrocarbon gases constituting the remainder. Significantly, during constant-voltage charging at high SOC, the swollen cells exhibit a remarkable shrinkage phenomenon, driven by in situ absorption of the internal gas. Extensive surface and structural characterizations indicate that both the fully lithiated graphite anode and high-voltage cathode actively contribute to H2 absorption and reactive consumption of CO2, leading to partial recovery of cell capacity and a temporary reduction in internal resistance. Nevertheless, this process accelerates interfacial growth (SEI/CEI) and triggers additional side reactions, ultimately lowering thermal stability as confirmed by Accelerating Rate Calorimetry. These findings offer critical insights into the synergistic degradation pathways of fast-charged LIBs and highlight the potential of leveraging gas self-absorption for mitigating swelling. More broadly, this study underscores the importance of managing gas behavior to balance performance, lifetime, and safety in next-generation LIBs systems.

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.

With high energy density and long cycle life, lithium-ion batteries (LIBs) are currently the most promising electrochemical devices for electric vehicles and energy storage. However, the safety and reliability of LIBs can be significantly compromised in low-temperature cyclic due to anode lithium plating and other factors which are still unclear. Therefore, it is essential to reveal the thermal-gas stability of LIBs under low-temperature cyclic. This study investigates the thermal runaway (TR) characteristics and gas production characteristics after TR of 18650-type NCA LIBs across four states of health (SOH), from 100% to 70%. Using Glove box, Electrochemical impedance spectroscopy, Scanning electron microscope, X-ray photoelectron spectroscopy, Accelerating rate calorimetry, and Gas chromatography, the research identifies critical trends in temperature rate, gas composition and explosion risk. After around 150 cycles, there is a significant and rapid decline in capacity. The internal resistance of batteries continues to increase, lithium is precipitated on the anode, and the cathode experiences particle fragmentation. Comparing the 70% SOH batteries with the 100% SOH, it is observed that more Li2O, Li2CO3 and LiF appeared on the anode. The triggering time of TR was 41.38% earlier, and the maximum temperature during TR decreased by 7.89%. The mass loss of the 70% SOH batteries were 11.85% higher than that of the 100% SOH. The gas production volume of the 80% SOH is the lowest, while that of the 70% SOH is the highest. Compared with the 100% SOH batteries, the upper limit (UEL) of gas production explosion for 70% SOH decreases by 3.67%, while the lower limit (LEL) increases by 24.46%. This indicates that the gas production of fresh batteries has a wider range of explosion limits. These research findings provide crucial insights for enhancing the safety and reliability of LIBs during operation, storage, and recycling processes.

The safety of lithium-ion batteries (LIBs) during operation has attracted widespread attention, particularly under high-temperature float charge (HTFC) conditions. Previous studies have mainly focused on capacity fade and gas evolution during HTFC aging, while the impact on battery thermal safety remains poorly understood. In this work, the evolution mechanisms of thermal safety of LIBs under HTFC conditions were systematically investigated by combining electrochemical performance, morphological, structural, gas evolution, and thermal analyses. A novel finding of this study is the identification of a critical temperature threshold (similar to 135 degrees C) that separates two opposite thermal stability patterns. Below this threshold, thermal stability increases with decreasing state of health (SOH), primarily due to decomposition and regeneration of the solid electrolyte interphase (SEI). Above the threshold, thermal stability decreases with decreasing SOH because of separator aging and cathode degradation, which substantially raise the risk of internal short circuits and thermal runaway (TR). Moreover, gas analysis reveals that the gases generated during HTFC aging (mainly CO2 and light hydrocarbons) are of low flammability and impose limited explosion risk; instead, they accelerate capacity degradation with minimal direct impact on thermal safety. Overall, this work fills a critical gap by demonstrating that swelling of LIBs under HTFC conditions cannot be universally classified as hazardous; instead, their thermal stability must be evaluated across distinct temperature ranges, providing a new perspective for safety assessment of LIBs under complex service conditions.

Data-driven approaches have shown great promise for battery state of health (SOH) diagnosis in electric vehicles (EVs). Despite the extensive data accumulated by historical fleets, reusing it to reduce reliance on costly aging tests remains challenging due to domain shifts across battery chemistries and varying conditions. This paper proposes a physics-guided transfer learning framework to facilitate cross-fleet SOH diagnosis. Drawing from approximately 300 million field data points from 30 vehicles, a feature set capturing electrochemical evolution and cumulative history is extracted to transfer aging knowledge from nickel-cobalt-manganese (NCM) commercial to LFP passenger fleets. Within a stacked autoencoder framework, gradient-based degradation laws integrating both electrochemistry and historical usage are embedded to enable noise-robust and cross-chemistry transfer across diverse real-world EV fleets. Our method achieves strong transferability, yielding a root mean square error (RMSE) of 0.98% on the target fleet. It also demonstrates high data efficiency, achieving an acceptable 1.80% RMSE using data from one target vehicle. Furthermore, physical constraints enable generalizability to unseen aging stages: finetuning with only the initial 50% or 30% of target data achieves RMSEs of 1.40% and 1.63%, respectively. This work underscores the feasibility of leveraging historical data to overcome data scarcity in practical battery health management.

Cyclic diffusion-induced stress due to the diffusion of lithium ions gives rise to fatigue in the active material of lithium-ion battery electrodes along with the repeated thermal and mechanical loads, which tends to decrease battery capacity and shorten life. Therefore, this paper comprehensively investigates the fatigue behavior of LiNixMnyCozO2 cathode active particles under cyclic multiload conditions by establishing a multiparticle representative volume element (RVE) model. With the linear matching method (LMM), the reverse plasticity limits of particles with varying numbers but the same volume fraction are calibrated. Additionally, the plastic strain range of cathode active particles is calculated by Direct Steady Cycle Analysis (DSCA), where the influences of different load combinations on fatigue failure are evaluated. A semianalytical model is derived to predict the fatigue damage of active particles under complex multiple loads. Moreover, by utilizing the unified procedure for fatigue and ratchet analysis (UPFRA) with material fatigue parameters, a series of constant fatigue life boundaries is established, and the impacts of working temperature and target structural life on these boundaries are investigated. This research also reveals that the fatigue life of active particles is significantly extended with the particle radius below the critical threshold.

Tailoring the energy transfer (ET) processes, including the singlet energy transfer (SET) or triplet energy transfer (TET) process, in hybrid systems consisting of inorganic nanoparticles and organic molecules can offer a strategy for the design and synthesis of bright fluorescent probes for photodynamic therapy or photocatalytic applications. However, the weak absorption and low quantum yield of lanthanide doped upconversion nanoparticles (UCNPs) limited the performance of the molecule-UCNP hybrid system, and the complex mechanism of the ET process involving singlet and triplet excitons is yet to be fully understood. Here, we report on a strategy for the synthesis of boron dipyrromethene (BODIPY) modified UCNP nanohybrids to engineer the SET or TET processes, specifically 8-(4-carboxyphenyl)-3,5-(4-hydroxyl)styryl-1,7-tetramethyl-pyrromethene fluoroborate (BDP-1) and 8-(4-carboxyphenyl)-2,6-diiodo-3,5-(4-hydroxyl)styryl-1,7-tetramethyl-pyrromethene fluoroborate (IBDP-1). These nanohybrids exhibit enhanced upconversion performance with an 800-times increase in upconversion quantum yield (UCQY) and efficient singlet oxygen (1O2) generation under 980 nm excitation. Our quantum chemistry calculations suggest that the energy transfer in these systems takes place by the Förster resonance energy transfer (FRET) and back energy transfer (BET) in NaYbF4:2%Er3+@BDP-1 (UCNP@BDP-1), and that direct triplet energy transfer (TET) from Yb3+ to anchored IBDP-1 is the dominant energy transfer mechanism in NaYbF4:2%Er3+@IBDP-1 (UCNP@IBDP-1).

Lifespan and safety are the most critical issues for the application of lithium-ion batteries (LIBs). During long-term service, the degradation mechanisms and safety evolution of LIBs remain unclear, posing significant obstacles to battery design and management. This study analyzes the electrochemical degradation mechanisms of LIBs under normal temperature cycling (NTC) and high-temperature cycling (HTC) conditions, linking these mechanisms to the evolution of battery safety. The findings reveal that during NTC, there is a “snowball effect” in performance degradation and safety evolution, leading to sudden death of battery and posing serious safety risks. The degradation pattern of LIBs during NTC and HTC is consistently dominated by the increase of internal resistance and the loss of lithium inventory (LLI). In the aging process, electrolyte consumption and the growth of the solid electrolyte interface (SEI) cause localized lithium plating on the anode, resulting in accelerated capacity decay. However, in batteries subjected to NTC, rapid accumulation of localized lithium plating can trigger a snowball effect, causing electrode deformation, internal short-circuit (ISC) and separator melting. These interacting catastrophic events form a vicious cycle, ultimately leading to battery sudden death and pose significant safety hazards. Additionally, thermal safety analysis reveals the correlation between thermal safety parameters and state of health (SOH), quantifying the thermal safety degradation caused by sudden death. Sudden death directly alters the evolution pattern of battery safety, leading to a severe decline in battery safety. These findings offer new insights into potential safety hazards associated with long-term use of LIBs.

Photodynamic therapy (PDT) has the advantages of strong targeting, few side effects, and little damage to normal tissues, which is considered as a high-quality new non-invasive treatment for cancer treatment. Photosensitizer (PSs), molecular oxygen and light are the three main components of PDT. Boron-dipyrromethene (BODIPY) is a widely used organic fluorescent dye, and its core structure is composed of a central boron atom (B) and two pyrrole rings connected by a methylene bridge. The eight active sites on the parent nucleus allow for a variety of functionalization modifications: positions 2,6 are particularly susceptible to halogenation reactions, and cross-coupling, click reactions and Knoevenagel condensation reactions at positions 3,5 are all commonly used structural modifications. As a new photosensitizer, BODIPY has shown broad application prospects in photodynamic therapy due to its excellent optical properties such as strong controllability, excellent light stability, high molar extinction coefficient (up to 120000M-1cm-1), and high fluorescent quantum yields. In this paper, we mainly focus on the strategy of BODPIY self-modification and the design and synthesis of hybrid systems, and finally summarize the advantages and difficulties of BODIPY as a photosensitizer, and looks forward to the future development of BODIPY.

The development of nickel-rich layered oxide (NRLO) cathode materials for power batteries, aiming to solve the range anxiety problem of electric vehicles. However, NRLO materials face the challenge of structural degradation during fast-charging cycling process. Therefore, it is necessary to comprehensively understand the crack evolution mechanism and the relationship between the capacity degradation and the severity of crack in NRLO materials. In this paper, the quantitative statistical analysis of the cracks in LiNi0.8Co0.1Mn0.1O2 (NCM811) particles is conducted by processing high-resolution Scanning Electron Microscope (SEM) image with machine learning algorithms. The evolution mechanism of NCM811 particle cracks is identified under fast-charging cycles. Through extracting the features of crack, the quantitative relationship of capacity fading and microcrack is explored. The understanding of the continuous crack evolution mechanism for NRLO materials can provide an internal perspective for optimizing the structural design of cathode materials. Machine learning-based image recognition methods are crucial for precisely analyzing the mechanisms of aging performance degradation in batteries. These meticulously quantified crack characteristics play a crucial role in advancing the development and refining the validation of lithium-ion battery life degradation models.

Lithium-ion batteries exhibit a notable decline in performance at low temperatures, including capacity reduction and impedance growth. Therefore, adopting an alternating current pulse (ACP) self-heating is a viable approach for improving battery performance at low temperatures. However, improper selection of ACP amplitude and frequency may induce capacity loss during heating. To overcome this challenge, a novel electrochemical-thermal coupling (ETC) model has been constructed by simultaneously considering the double layer and lithium plating, and combining model accelerated computation. The model can efficiently and accurately calculate capacity loss and temperature rise of batteries under high-frequency (> 10 Hz) currents. The effects of ACP self-heating on the batteries are analyzed using the model, revealing the mechanism by which the combined effects of ACP frequency and amplitude contribute to lithium plating during self-heating. Furthermore, a Bayesian optimization framework is established by combining the model to effectively identify the optimal ACP parameters at different temperatures, accompanied by the proposal of a self-heating strategy that adapts to temperature variations. The self-heating strategy can enhance the temperature rise rate while eliminating capacity loss due to lithium plating. The experimental validation illustrates that batteries can be rapidly heated from -20 degrees C to 11.1 degrees C within five minutes via the optimized strategy without capacity loss after 90 heating cycles. The proposed self-heating strategy achieves a record fast battery temperature rise compared to other studies.

Moderate suppression of thermal quenching of upconversion nanoparticles (UCNPs) can ensure sufficient temperature thermal response over a wider measurement range, which is key to reliable optical temperature measurement performance. Herein, we report a strategy for synthesis of beta-NaYbF4 UCNPs, to achieve both high Er3+ doping concentration and epitaxial NaYF4 inert shell to enhance thermoresponsive upconversion luminescence (UCL). With optimised ligand ratio, shell thickness, and Er3+ concentration, we achieved a 24-fold enhancement in red emission and a 4-fold increase in green emission intensities. Spectroscopic analysis reveals that high Er3+ content strengthens cross-relaxation transitions, while the inert shell suppresses surface quenching, collectively mitigating nonradiative losses. As a result, these core-shell UCNPs exhibits a high relative sensitivity (SR) of 2.7 % K-1 and a temperature resolution (delta T) of 0.18 K at 200 K across a wide operational range from 200 K to 500 K. Furthermore, we demonstrate precise thermal sensing capability under both low- and high-temperature conditions with high signal stability and reproducibility. This work offers an effective strategy for tailoring the optical response of UCNPs and provides a general design framework for high-performance nanothermometers suitable for both cryogenic and wide-range thermal environments.

Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and long-evity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprece-dented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the tran-sition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establish-ment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and prac-titioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative inno-vation and industrial adoption in battery health management.