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Fast charging of lithium–ion batteries is essential for accelerating a widespread use of electric vehicles; however, its adoption significantly increases battery thermal stress and the risk of thermal runaway, particularly in aged cells. This study proposes a simulation-trained digital twin (DT) framework for probabilistic assessment of thermal runaway and critical charging current estimation under fast charging conditions. A dataset is generated using an electrochemical–thermal Single Particle model, varying current rate, capacity, and internal resistance. Then, an encoder–decoder neural network architecture is developed to map and convert static operating conditions into dynamic temperature evolution, enabling efficient surrogate modeling of thermal behavior. The proposed digital twin achieved an MAE of 3.05 °C and a recall of 97.22% for thermal runaway prediction while estimating critical charging currents of approximately 1.35–1.52C. The proposed methodology provides a computationally efficient tool for risk-aware fast-charging strategies, which can be integrated into battery management systems for enhanced safety. While the current study is applied to specific single-cell chemistry and simulation-based training, the framework can be easily extended to online battery systems and operating conditions. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

Reliable state of health (SOH) estimation plays an important role in the safe and stable operation of lithium-ion battery energy storage systems. Nevertheless, the nonlinear degradation characteristics and complex aging behaviors of batteries hinder accurate SOH estimation. This study proposes a Whale Optimization Algorithm (WOA)-optimized Convolutional Neural Network (CNN)–Temporal Convolutional Network (TCN)–Temporal Pattern Attention (TPA) framework for lithium-ion battery SOH estimation. Multiple health factors are extracted from charge–discharge curves to characterize battery degradation behaviors. Neighborhood-based imputation and Hampel-Median Absolute Deviation (MAD) correction are employed to handle missing values and local outliers, while Pearson correlation analysis is applied to evaluate the relevance of extracted features. CNN module captures local degradation patterns, TCN module learns long-term aging dependencies, and TPA mechanism enhances the representation of critical degradation stages. Furthermore, WOA adaptively optimizes key hyperparameters to improve model performance and robustness. The proposed framework is validated using NASA and CALCE battery datasets. Experimental results demonstrate that, compared with the CNN-TCN model, the proposed method reduces RMSE by 7.49–50.81% on NASA datasets and 15.83–60.38% on CALCE datasets, achieving higher estimation accuracy and stability. Full article

Deploying retired lithium-ion batteries in photovoltaic energy storage systems is a promising second-life application, but heterogeneous aging and internal inconsistencies can induce the barrel effect, reducing available capacity and accelerating pack degradation. Existing equalization methods mainly rely on internal battery states and often neglect external irradiance fluctuations. To address this issue, this study proposes an irradiance-aware adaptive fuzzy active equalization strategy based on a multichannel bidirectional flyback converter. A second-order RC equivalent circuit model with a fifth-order OCV–SOC mapping is established to describe the dynamic behavior of retired cells. Then, solar irradiance and its rate of change are introduced into a dual-input fuzzy controller to adaptively regulate the equalization duty cycle according to both SOC inconsistency and PV input fluctuation. A saturation function constrains the active duty cycle below 0.5 to maintain discontinuous conduction mode operation and avoid transformer core saturation. Simulation results under rapid cloud occlusion, stable high irradiance, and persistent weak light show that the proposed strategy reduces equalization time by 13.8%, 4.4%, and 8.4%, respectively, compared with SOC-only fuzzy control. Under a publicly measured irradiance condition, the proposed strategy achieves the shortest equalization time of 3267.4 s, reducing the time by 24.2%, 27.7%, 29.0%, and 32.9% compared with traditional threshold-based, SOC-only fuzzy, maximum–minimum SOC, and PID-based strategies, respectively. Full article

Accurate state of charge (SoC) estimation requires mathematical models that consider individual user usage patterns to ensure optimal performance of lithium-ion battery (LiB) systems. Accurate SoC estimation improves battery life, driving comfort and avoids failure. In this paper, Pi and T equivalent circuit models were developed and validated with the 1RC and 2RC Thevenin models. Simulations were carried out to test their behavior under different rates of charging and discharging (1C, 2C, and 3C). The proposed models were validated by using real data from battery tests. At high C-rates, the 1RC and 2RC models show large variations, but the Pi model provided the best agreement with the experimental data for all rates, and the T model provided the second-best fit. In this paper, the impact of battery aging and degradation on the accuracy of the Extended Kalman Filter (EKF) and Coulomb Counting (CC) methods for estimating the SoC was studied, emphasizing the need to include them in the models. The results emphasized the importance of advanced modeling approaches for efficient battery management and point to directions for future research. Full article

Battery engineers often work with repeated cycle-level monitoring signals that are related to ageing but are not direct capacity or resistance measurements. This paper studies how such a signal can be used to detect faster change and to decide whether the measured record is long enough to locate the transition reliably. We analyse 14 lithium-ion cell records from a processed Hawaii Natural Energy Institute (HNEI) cycle-level table, using the charging-to-discharge duration ratio (C/D) as a practical charge/discharge-duration indicator. The corresponding original HNEI measurement files were checked to improve the cell description and to examine whether a capacity-based comparison was possible. They confirm substantial capacity fade, but they also contain non-physical capacity entries near cycle 370; these entries are not used as validation of C/D transition cycles. We compare a linear reference, broken-line regression, a smoothing-spline curvature check, an aggregate Bayesian smooth-transition model, and a battery-level hierarchical Bayesian model. The hierarchical model estimates a mean battery-level transition cycle of 553.9 cycles (95% credible interval: 547.1–560.9), with substantial battery-to-battery variation (standard deviation about 142 cycles). All 14 batteries show a positive increase in the rate of change of C/D. Randomly removing about half of the measurements while retaining the full test span widens uncertainty but preserves the acceleration conclusion and nearly preserves battery ordering. In contrast, cutting off the late part of the test record strongly destabilizes transition timing and extrapolation. The approach is therefore useful as a retrospective screening and test-interpretation tool for a chosen ageing-related signal. It is not a direct capacity-knee detector, a mechanism diagnosis, or a remaining-useful-life predictor. Full article

Reliable state of health (SOH) and remaining useful life (RUL) estimation is essential for lithium-ion battery diagnostics, prognosis, and management across cell, module, and pack levels. Yet the reported performance metrics often remain tied to controlled cell datasets, with batteries degrading due to chemistry shifts, protocol variation, temperature changes, inconsistent and insufficient measurements, and pack-level heterogeneity. This critical review investigates what evidence is required before an SOH/RUL estimator can be considered trustworthy, transferable, and suitable for battery management system deployment. Based on a de-duplicated classified set of 176 scientific works and a supplementary evidence audit workbook, this review synthesizes model-based, machine learning, deep learning, transfer learning, physics-informed, impedance-based, thermographic, relaxation-based, and digital twin approaches through observability, robustness, uncertainty calibration, transferability, and deployment feasibility. A compact mathematical framework formalizes the health inference, domain shift, cross-fidelity degradation, calibrated uncertainty, and BMS-facing validation criteria. The analysis argues that deployment-ready battery health intelligence should be evaluated as an evidence system rather than as a point prediction task. The proposed multi-fidelity validation framework links synthetic cells, controlled aging, module (pack) testing, fleet shadow operation, and closed-loop safety-governed deployment using acceptance criteria, based on worst-domain error, calibration data, warning risk, and computational feasibility. Full article

The growing deployment of electric vehicles requires battery management systems that maintain cell uniformity while reducing hardware complexity and improving energy efficiency. Many cell-balancing methods rely on converter-based architectures and remain validated only through simulation. This study experimentally validates a previously published adaptive recombination strategy using a prototype with second-life Panasonic NCR18650PF lithium-ion cells. The system employs dynamic series-parallel reconfiguration, relay-based switching, isolated voltage monitoring, and adaptive control to redistribute energy without dedicated balancing converters. Six test cases were evaluated under resting, charging, and discharging conditions using simultaneous and sequential schemes. Complete balancing was achieved in all test cases within the measurement resolution of the prototype. The experiments reproduced the main balancing mechanisms predicted by simulation, particularly under resting and discharging conditions, while also revealing practical deviations under charging operation. These deviations indicate that real current-sharing behavior, cell aging, contact resistance, wiring losses, and measurement constraints can influence recombination performance in ways not fully captured by ideal simulation models. The study therefore provides first-stage hardware evidence for the feasibility of adaptive recombination balancing and identifies key implementation requirements for future real-time, safety-rated, and scalable BMS development. This research contributes to SDG 7 by supporting improved lithium-ion battery utilization and energy efficiency for sustainable electric mobility. Full article

Lithium-ion batteries (LIBs) have been widely used in different fields as energy storage systems, such as electric vehicles and power grids. The performance of LIBs degrades with usage, which poses challenges for battery management. Thus, accurate online estimation of the state of health (SOH) is critical to ensure reliability and prolong the service time of LIBs. To achieve this, data-driven methods have become popular due to the capability of learning the mapping between SOH and measurements without prior knowledge of aging mechanisms. However, the online estimation performance of these methods cannot be guaranteed, since the models are trained offline and do not have the capability of online updating when new data are collected. In addition, the inputs for these methods are constructed with the voltage–capacity (V-Q) curve within a fixed voltage interval, which can hardly be realized in real-life applications due to the randomness of the charging or discharging process. This study proposes a Sequential Gaussian Process (Seq-GP) model-based LIB SOH estimation method, where model parameters can be updated using newly collected LIB data, such that online estimation can be fulfilled. Moreover, a novel feature extraction method is presented using random parts of the LIB V-Q curve to meet the requirements for practical applications. The proposed method is evaluated on two public battery datasets, showing competitive estimation accuracy together with online updating, uncertainty quantification, and low computational cost under the tested protocol. The results will be beneficial for online SOH estimation of LIBs in practical scenarios. Full article

Accurate state-of-health (SoH) prediction is vital for safe and efficient battery management, enabling extended lifespan and improving technologies such as electric vehicles and stationary energy storage systems. In this work, a graph neural network-based deep learning framework is proposed to predict the SoH of a commercial nickel–manganese–cobalt oxide (NMC) lithium-ion technology. Health indicators obtained from the in-house-generated experimental aging dataset are used to train and validate the model across batteries subjected to diverse operating conditions. The hybrid architecture combines graph neural networks (GraphSAGE) with convolutional neural networks (CNN) and long short-term memory (LSTM) blocks, capturing both local structural relationships and temporal patterns in the battery data. Evaluation results show strong predictive performance, achieving an R² of 0.989 and a mean squared error of 3.23 × 10⁻⁶. These findings suggest that the proposed methodology could be deployed as a useful diagnostic tool. Full article

Battery use in trolleybuses improves energy efficiency and enables driving outside routes with overhead contact lines. This paper analyses the ageing process of lithium-ion batteries by determining the State of Heath (SOH) curve based on real-world data collected during six and a half years of trolleybus operation. The battery, manufactured using NMC technology (lithium-nickel-manganese-cobalt LiNiMnCoO₂), was used as an onboard energy storage unit. The battery pack powers the trolleybus on the non-electrified route segments and improves its energy efficiency. In this paper, the ageing process of the lithium-ion battery in such a vehicle was investigated, using recorded data for each day of operation between 2016 and 2023. The SOH of the battery was estimated on the basis of three criteria: specific SOC range, specific battery voltage range and specific battery pack temperature range. Under these circumstances, the values of electric charge and energy flow into the battery were analysed, allowing the obtainment of the battery SOH value. Empirical distributions of random variables related to the minimum and maximum battery temperature and battery discharge current were presented. Based on these empirical distributions, statistical descriptors representing health indicators were calculated. The environmental factors (temperature, SOC, discharge and charge currents) that had a significant impact on the ageing process of the battery under test were analysed as well. Full article

Lithium-ion batteries experience structural degradation during operation and storage, which can negatively impact performance, safety, and service life. Early identification of these degradation-induced structural changes is important for battery health assessment and reliability monitoring. This study proposes a deep learning-based framework for classifying the structural condition of 18650 lithium-ion batteries using X-ray micro-computed tomography (µCT) images. The proposed approach combines centroid-based core cropping, image normalization, three-slice stacking, and transfer learning using a fine-tuned InceptionResNet-V2 architecture. Three adjacent µCT slices are stacked into an RGB-like representation to preserve local three-dimensional structural information while maintaining compatibility with a two-dimensional convolutional neural network. The original classification head of InceptionResNet-V2 was replaced with a custom classification block consisting of dropout layers, fully connected layers, and a SoftMax classifier optimized for battery condition recognition. The framework was evaluated using four battery structural conditions: pristine, cycle-aged, calendar-aged, and thermally cycled cells. Experimental results demonstrated an overall classification accuracy of 96.62%, with a precision of 95.62%, sensitivity of 96.94%, specificity of 98.92%, and F1-score of 96.20%. Comparative analysis with previously reported battery imaging studies demonstrated that the proposed framework achieves competitive performance while addressing the challenging task of structural condition classification from µCT imagery. The results demonstrate the potential of combining advanced X-ray imaging and transfer learning for automated lithium-ion battery structural health assessment and degradation monitoring. Full article

Accurate prediction of remaining useful life (RUL) for lithium-ion batteries remains a critical yet complex challenge due to highly non-linear degradation dynamics and profound data heterogeneity across varying operational profiles. While convolutional neural networks (CNNs) have shown promise in battery health management, traditional architectures struggle with gradient vanishing in deep feature spaces and lack the adaptive capacity to filter early-cycle noise under diverse degradation conditions. To improve robust RUL estimation across heterogeneous benchmark datasets, this paper proposes a deep learning framework that integrates residual connections with dual-attention mechanisms (ResCNN). Specifically, the residual structures effectively mitigate gradient degradation during the extraction of abstract degradation patterns. Concurrently, a synergistic Squeeze-and-Excitation (SE) and Multi-Head Attention module adaptively calibrates channel-wise feature importance and captures long-range temporal dependencies inherent in complex capacity fade processes. The proposed framework is evaluated under a wide spectrum of degradation conditions and distinct cathode systems (LFP and LCO) using both dataset-specific train/validation/test protocols and strict source-to-target cross-dataset transfer tests. Experimental results demonstrate that ResCNN achieves consistently lower prediction errors than baseline models across the evaluated datasets and maintains positive explanatory power on unseen target datasets without target-domain training. Ablation studies further validate the synergistic contribution of each architectural component toward capturing intrinsic battery aging phenomena. Full article

State of Health (SOH) serves as a key metric in assessing the performance of lithium-ion batteries. It is challenging for a single sensing signal to fully characterize the multi-physics evolution characteristics during battery degradation, which limits the accuracy and robustness of SOH estimates. Therefore, a lithium-ion battery SOH estimate method combining electrical and ultrasonic features with a frequency-enhanced decomposed Transformer (FEDformer) is proposed. To begin with, a multi-condition battery aging dataset is constructed through experiments, comprising electrical and ultrasonic signal data from 7828 cycles. Subsequently, 20 electrical and ultrasonic features are extracted from multiple perspectives, and 12 strongly correlated features are selected via the Spearman correlation coefficient. Finally, the FEDformer is employed to establish the SOH estimate model, where the accuracy, robustness, and generalization of the SOH estimates are comparatively analyzed across different input features, models, and cross-aging conditions. The results demonstrate that, compared to using electrical features alone, the combined ultrasonic features improve the estimation performance by more than 40% on average. Furthermore, in the cross-aging datasets, the mean absolute error and root mean square error of the SOH estimates are 0.52% and 0.63%, respectively, validating the robustness and generalization capability of the proposed method. Full article

In this study, we investigate the effect of aging on the thermal runaway characteristics of 314 Ah lithium iron phosphate batteries with different cycles (0, 400, and 1000 cycles), with the batteries being overcharged to thermal runaway with a 0.5 C charging rate. The results indicate that aging significantly reduces the severity of thermal runaway for a battery. Fresh batteries exhibited intense jet fires with a peak temperature of 501.4 °C, while aged batteries produced only heavy smoke without obvious flames, with peak temperatures dropping to 401.2 °C. Aging leads to the thickening of the SEI film, increased internal resistance, and an unstable voltage response, extending the thermal runaway trigger time from 1979 s to 4039 s, but with a lower trigger temperature. The negative tab consistently remained the core heat accumulation point, with temperature differences of 10–30 °C compared to other wall surfaces, and the core temperature during thermal runaway exceeded 500 °C. The transition from casing rupture to jet fire occurred within only 2 s, indicating an extremely short safety response window. Through this research, we provide critical insights for the aging assessment and thermal safety management of energy storage batteries. Full article