Search Results (843)

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions. Full article

This perspective provides a multidisciplinary assessment of the use of lithium-ion batteries from electric vehicles (EVs) for second-life applications, motivated by the need to improve resource efficiency, reduce environmental impacts, and support a circular battery economy. Second-life deployment requires the integrated consideration of technical performance, legal compliance, and economic viability. The analysis combines a technical evaluation of battery aging mechanisms, operational load effects, and qualification strategies with a legal assessment of the EU Batteries Regulation (EU) 2023/1542 and an economic analysis of market potential and business models (BM). From a technical perspective, the limitations of State of Health (SOH) as a standalone indicator are demonstrated, highlighting the need for multiple health indicators and degradation-aware qualification. A scalable two-step qualification approach, combining qualitative inspection with a standardized quantitative measurement protocol, is discussed. From a legal perspective, regulatory requirements and barriers related to repurposing, waste classification, and conformity assessment are analyzed. From an economic perspective, business model patterns and market dynamics are evaluated, identifying Automated Guided Vehicles (AGVs) and industrial Energy Storage Systems (ESSs) for renewable firming as particularly promising applications. The paper concludes with recommendations for action and key research needs to enable safe, economically viable, and legally compliant second-life deployment. Full article

The rapid growth of electric vehicles (EVs) has created an urgent need for sustainable end-of-life battery management systems. This paper presents a novel AI-driven framework for designing resilient reverse logistics networks that optimize the collection, testing, repurposing, and recycling of EV batteries within a circular economy context. We develop a bi-level optimization model in which the upper level determines strategic facility location decisions under disruption uncertainty, and the lower level employs deep reinforcement learning (DRL) to make dynamic operational decisions including battery routing, State-of-Health (SoH)-based sorting, and inventory management. The model simultaneously optimizes three objectives: total supply chain cost minimization, carbon emission reduction, and resilience maximization. A novel Fuzzy-Robust Stochastic programming approach with Conditional Value-at-Risk (FRS-CVaR) handles hybrid uncertainty from demand variability, supply disruptions, and material price volatility. We propose an enhanced Non-dominated Sorting Genetic Algorithm III (NSGA-III) integrated with Proximal Policy Optimization (PPO) for an efficient solution. The framework is validated through a comprehensive case study of the Gulf Cooperation Council (GCC) region, demonstrating that the AI-driven approach reduces total costs by 18.7%, decreases carbon emissions by 23.4%, and improves supply chain resilience by 31.2% compared to traditional optimization methods. Ablation studies across 10 independent runs with different random seeds confirm the robustness of these findings (95% confidence intervals within ±2.3% for all metrics). Sensitivity analysis reveals that battery SoH prediction accuracy and facility redundancy levels significantly impact network performance. This research contributes to both methodology and practice by providing decision-makers with an intelligent, adaptive tool for sustainable EV battery lifecycle management. Full article

Lithium-ion batteries are widely applied in transportation, communication, and other fields. Nevertheless, during prolonged cycling operation, internal electrochemical reactions inevitably lead to the degradation of the state-of-health (SOH). To ensure the reliability and safety of lithium-ion batteries, accurate SOH estimation is of critical importance. Nevertheless, under practical operating conditions, obtaining fully recorded charge–discharge data is often impractical. Motivated by the practical charging behaviors of lithium-ion batteries, this paper proposes a practical SOH estimation method based on incremental capacity analysis, dynamic time warping (DTW), and gradient-boosting regression trees (GBRTs). Three health indicators—interval incremental capacity features, local capacity–voltage curve similarity, and segmented voltage curve similarity—are extracted. The proposed method requires only 0.13 V and 0.07 V voltage windows on the Oxford and CALCE datasets. The effectiveness of the proposed model is verified across both public datasets and laboratory test data. Experimental results demonstrate RMSE values of approximately 2.5% and 2.0%, respectively. Compared with mainstream SOH estimation algorithms, the proposed approach delivers comparable accuracy while achieving training time reductions of up to 57.6% and 91.9% relative to GPR and SVM, making it suitable for real-time battery management systems. Full article

Bone conduction hearing implant systems (BCHIs) are established treatments for patients with conductive or mixed hearing loss or single-sided deafness when conventional hearing aids are unsuitable. This study evaluated the cost-effectiveness of the active transcutaneous system Sentio versus a similar system, i.e., Osia in an Australian setting. Scenario analyses also compared Sentio to other systems, i.e., Ponto and Baha Attract. A Markov cohort model was adapted from a previously published source to reflect Australian practice, incorporating device acquisition, surgery, maintenance, battery replacement and adverse event management over a 15-year horizon from a healthcare perspective. Effectiveness inputs were derived from published evidence using a naïve indirect comparison. Extensive sensitivity analyses and external validation tested robustness. In the base case, Sentio was associated with lower costs and a small modelled incremental quality-adjusted life years (QALYs) gain versus Osia. Scenario analyses confirmed cost-effectiveness relative to Ponto and Baha Attract, with outcomes below the Australian willingness-to-pay threshold. Health state utility, device price and reimplantation assumptions were the most influential drivers, yet Sentio remained cost-effective in over 95% of simulations. These findings support Sentio as a clinically and economically efficient BCHI in Australia and highlight the need for direct utility and long-term durability data. Full article

Low-voltage primary batteries broadly power small electronics used in health, biomedical, and wearable applications. These devices are generally more sensitive to volumetric capacity than gravimetric capacity. The current state-of-the-art button battery is Zn-Ag₂O, where contributors that limit volumetric capacity include the incorporation of inactive materials in the electrode microstructure such as gelling agents, binders, and conductive additives. Herein, cathode materials of CuO and FeS₂ will be described for small form factor coin/button cells. When paired with Li metal anodes, the operating voltage is similar to Zn-Ag₂O. The key innovation is that they will be processed into all-active-material (AAM) electrode architectures, where the electrodes will comprise only electroactive materials and pores that are filled with electrolyte during cell fabrication. The AAM architecture significantly enhanced electroactive material volume utilization, and thus volumetric capacity. FeS₂ and CuO were processed into AAM electrodes under various processing conditions, and Li-FeS₂ and Li-CuO primary batteries were fabricated and evaluated. At the cell level, volumetric capacity of 1300 mAh cm⁻³ was achieved, and in a button cell form factor 395/927, nearly 100 mAh was delivered, which compares favorably with commercially available options, which typically range from 27 to 55 mAh. Full article

Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents various challenges. Generally, this task is handled by a battery management system (BMS). Therefore, this paper provides a brief introduction to the key battery state parameters, such as the state of charge (SOC), state of health (SOH), and state of power (SOP). Subsequently, after a brief overview of BMS structural and software architectures, this work focuses on a detailed description of equivalent circuit models (ECMs) and artificial neural networks (ANNs), which represent part of the modeling approaches available in the literature, providing a characterization of the complex and nonlinear dynamics underlying lithium-ion batteries. These approaches are systematically evaluated, including hybrid methods to highlight their respective advantages, limitations, and suitability for different BMS functionalities. Full article

This work provides an overview of the mechanics of lithium-ion batteries, both from the aging and diagnostics perspective. Battery diagnostics based on mechanical measurements exploit the strong correlation between electrode lithiation and its deformation, resulting in macroscopic cell deformation. Macroscopic deformation is then a proxy for lithium concentration, enabling estimation of state of charge (SOC) and degradation indicators such as loss of active material and lithium inventory. The results demonstrate that SOC estimation algorithms based on deformation measurements are more robust than voltage-based methods, which are sensitive to temperature and aging, requiring constant updates of the algorithm parameters. Moreover, the health of the battery can be assessed through the differential expansion method even under high-current operation, providing results consistent with the traditional differential voltage method but applicable to real-world industrial applications. Mechanics plays a crucial role also in battery degradation. This work presents the application of POLIDEMO, an advanced battery aging model that explicitly accounts for mechanical degradation phenomena, providing a physics-based framework describing the coupled electrochemical–mechanical aging processes in lithium-ion batteries. It enables the prediction of key degradation indicators, including capacity fade—capturing the characteristic knee-point behavior—and the irreversible battery thickness increase associated with long-term aging. The model is validated with multiple aging datasets, demonstrating that parameters calibrated under a single operating condition can accurately predict degradation across diverse aging scenarios. Full article

Battery management systems (BMSs) are integral components of electric vehicles (EVs), as they ensure the safe and efficient operation of lithium-ion batteries. State of health (SoH) estimation is one of the core functions of BMSs, providing an assessment of the current condition of a battery, while prognostics aim to predict remaining useful life (RUL) as a function of the battery’s condition. An accurate SoH estimation allows proactive maintenance to prolong battery lifespan. Traditional SoH estimation methods can be broadly divided into experiment-based and model-based approaches. Experiment-based approaches rely on direct physical measurements, while model-driven approaches use physics-based or data-driven models. Although experiment-based methods can offer high accuracy, they are often impractical and costly for real-time applications. With recent advances in artificial intelligence (AI), deep learning models have emerged as powerful alternatives for SoH prediction. This paper offers an in-depth examination of AI-driven SoH prediction technologies, including their historical development, recent advancements, and practical applications, with particular emphasis on the implementation of widely used AI algorithms for SoH prediction. Key technical challenges associated with SoH prediction, such as computational complexity, data availability constraints, interpretability issues, and real-world deployment constraints, are discussed, along with possible solution strategies. Full article

Battery energy storage systems (BESSs) are increasingly required to provide grid-support services under weak-grid conditions, where the stability of virtual synchronous generator (VSG) control largely depends on the health status and dynamic characteristics of the battery unit. However, existing VSG strategies typically assume fixed parameters and neglect the intrinsic coupling between battery aging, DC-link energy variations, and converter dynamic performance, resulting in reduced damping, degraded transient regulation, and accelerated lifetime degradation. This paper proposes a health-responsive VSG control strategy enabled by real-time energy-dynamics sensing. By reconstructing the DC-link energy state from voltage and current measurements, an intrinsic indicator of battery health and instantaneous power capability is established. This energy-dynamics indicator is then embedded into the VSG inertia and damping loops, allowing the control parameters to adapt to battery health evolution and operating conditions. The proposed method achieves coordinated enhancement of transient stability, weak-grid robustness, and lifetime management. Simulation studies on a multi-unit BESS demonstrate that the proposed strategy effectively suppresses low-frequency oscillations, accelerates transient convergence, and maintains stability across different aging stages. Full article

In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme to extract a number of raw features and their corresponding health status labels. Multidimensional candidate feature sets that capture aging information under different conditions are constructed. Subsequently, a three-stage feature selection strategy, including Pearson and Spearman correlation analysis, hierarchical redundancy elimination, and minimum redundancy maximum relevance, was applied to screen the candidate feature set of each condition, resulting in customized feature sets with condition adaptability. By analyzing the occurrence frequency and mean absolute correlation coefficient of each feature within the custom feature set, a comprehensive feature set with multi-condition adaptability was screened and determined. On this basis, by integrating temporal sequence information and operating condition information, a dual-path fusion estimation model with attention mechanism and condition modulation was established. The validation results of the lithium-ion battery multi-condition cycling aging dataset demonstrate that the model achieves accurate health state estimation, with mean absolute error and root mean square error of 0.8281% and 0.9835%, respectively. Finally, comparisons with other methods were conducted in terms of feature selection strategies and model estimation performance. The results demonstrate that the proposed approach achieves superior estimation accuracy and enhanced interpretability. Full article

Thermal runaway (TR) of lithium-ion batteries (LIBs) represents a critical safety challenge in EV applications. This study explores the potential of data-driven safety management strategies for mitigating TR risks in EVs. To minimize the impact of external environmental factors on the degradation of LIBs, experiments were conducted using an accelerating rate calorimeter (ARC). The intrinsic thermal behavior of six nickel–cobalt–manganese (NCM) cells at different states of health (SOH) and operating temperatures has been captured in created adiabatic conditions. Multiple sensors were deployed to monitor the temperature and electrochemical and environmental parameters throughout the degradation process until TR occurred. The results show that both the thermal and electrochemical stability of LIBs have been affected, exhibiting consistent thermal patterns and early electrochemical instability. Furthermore, even under adiabatic conditions, the degradation of LIBs show synergistic effects with environmental parameters such as chamber temperature and pressure. Correlation analysis further revealed the coupling relationships between the monitored parameters. Through calculating their correlation coefficients, the results indicate advantages of combining thermal, electrochemical, and environmental parameters as being to characterize the degradation of LIBs and enhance the identification of TR precursors. These findings stress the importance of considering the battery-environment system as a whole in safety management of EVs. They also provide insights into the development of data-driven safety management strategies, highlighting the potential for achievement and integration of anomaly detection, diagnosis, and prognostics functions in current EV management frameworks. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Accurate prognosis of the remaining useful life (RUL) for lithium-ion batteries is critical for mitigating range anxiety and ensuring the operational safety of electric vehicles. However, existing data-driven methods often struggle to maintain robustness when transferring from controlled laboratory conditions to complex, sensor-limited, real-world environments. To bridge this gap, this study presents U-H-Mamba, a novel uncertainty-aware hierarchical framework trained on a massive hybrid repository comprising over 146,000 charge–discharge cycles from both laboratory benchmarks and operational electric vehicle datasets. The proposed architecture employs a two-level design to decouple degradation dynamics, where a Multi-scale Temporal Convolutional Network functions as the base encoder to extract fine-grained electrochemical fingerprints, including derived virtual impedance proxies, from high-frequency intra-cycle measurements. Subsequently, an enhanced Pressure-Aware Multi-Head Mamba decoder models the long-range inter-cycle degradation trajectories with linear computational complexity. To guarantee reliability in safety-critical applications, a hybrid uncertainty quantification mechanism integrating Monte Carlo Dropout with Inductive Conformal Prediction is implemented to generate calibrated confidence intervals. Extensive empirical evaluations demonstrate the framework’s superior performance, achieving a RMSE of 3.2 cycles on the NASA dataset and 5.4 cycles on the highly variable NDANEV dataset, thereby outperforming state-of-the-art baselines by 20–40%. Furthermore, SHAP-based interpretability analysis confirms that the model correctly identifies physics-informed pressure dynamics as critical degradation drivers, validating its zero-shot generalization capabilities. With high accuracy and linear scalability, the U-H-Mamba model offers a viable and physically interpretable solution for cloud-based prognostics in large-scale electric vehicle fleets. Full article