Achieving the coexistence of high energy density and fast-charging capability remains a fundamental challenge for lithium-ion batteries. Increasing electrode thickness and compaction density enhances energy density but simultaneously alters the pore structure and restricts lithium-ion transport, leading to concentration polarization, increased resistance, and lithium plating. In this work, we employ X-ray computed tomography (X-CT) and 3D reconstruction to establish quantitative relationships between particle size, compaction density, and key structural parameters (porosity, tortuosity, effective proportion of lithium-ion flux (feff)). Then, an electrochemical model is used to link the liquid-phase kinetic parameters (ionic conductivity (k0) and liquid-phase diffusion coefficient), as corrected by the effective proportion of lithium-ion flux feff, to polarization and lithium-plating behavior, and the maximum current density without lithium plating under various fabrication conditions is finally determined. Results show that small-particle electrodes exhibit superior rate capability at moderate compaction levels, but suffer from rapidly increasing tortuosity and reduced transport efficiency under high compaction and large thickness. Moreover, a double-layer gradient electrode design effectively integrates the advantages of both large- and small-particle architectures, enabling high-rate operation without lithium plating. The double-layer gradient electrode (ρ = 1.6 g/cm3) exhibited ~50% higher performance at 1.5 C compared to the small-particle anode and enabled 2 C charging without lithium plating. This study offers a robust structural design strategy for optimizing thick-electrode architectures toward high-energy, fast-charging LIBs.

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To solve these problems, this study proposes an Adaptive-Weight PINN (AW-PINN) optimized by the Grey Wolf Optimizer (GWO) algorithm, which features a dual-LSTM parallel structure and takes incremental capacity peaks and charged capacity as dual physical constraints. A weight generator LSTM adaptively learns weights for monotonicity losses without manual intervention, and GWO globally optimizes physical loss weights to balance data fitting accuracy and prediction physical consistency. Validated on LiCoO2, NCA, and NCM batteries from CALCE and Tongji University datasets via comparative, ablation, and small-sample experiments, AW-PINN shows superior predictive performance (average RMSE = 0.0076; MAE = 0.0065; MAPE = 0.0072), robustness, and generalization. It integrates battery degradation physics with deep learning, retaining strong fitting capability while enabling physical interpretability.

Battery Emergency Mobile Power Systems (BEMPSs) play a critical role in disaster recovery, remote electrification, and grid reinforcement, where resilient, rapidly deployable power supply is essential. However, conventional grid-forming (GFM) control strategies often rely on static parameters, limiting their adaptability during grid disturbances, weak grid conditions, and operational mode transitions. This paper proposes a novel energy-aware adaptive control strategy for GFM inverters, tailored for EMPS applications. First, a multi-mode operation framework is developed to enable seamless transitions among grid-forming, grid-following (GFL), and islanded modes, incorporating a dual-loop circulating current decoupling mechanism to suppress transient current and provide damping. Second, a dynamic virtual inductance regulation scheme is introduced, adaptively modulating output impedance based on DC link energy, PCC voltage fluctuation, and grid strength estimation. Third, an energy-aware control law ensures real-time adjustment of inverter dynamics, enhancing damping performance towards the grid disturbance. Extensive time-domain simulations validate the proposed strategy’s effectiveness under mode switching and power disturbance scenarios. Results demonstrate superior dynamic performance, reduced transient overshoot, and improved system robustness compared to conventional methods, making the proposed controller highly suitable for flexible deployment.

Electric vehicle (EV) adoption is generating a rapidly increasing stream of retired lithium-ion batteries for second-life deployment. However, thermal safety concerns continue to limit their reuse. This paper reviews second-life battery (SLB) thermal safety and management and organizes existing work through a mechanism-to-deployment framework linking four domains: degradation mechanisms, cell screening, pack configuration, and monitoring. Evidence indicates that thermal risk depends on the degradation pathway rather than capacity fade. In fact, cells with comparable capacity can exhibit substantially different trigger temperatures depending on whether lithium plating or solid-electrolyte interphase (SEI) growth dominates. Therefore, capacity-based screening is insufficient because cells that satisfy capacity thresholds may still remain thermally unstable. The four domains are tightly coupled: the degradation pathway determines screening requirements; screening outcomes constrain pack design; pack topology influences fault escalation; and together these factors determine what monitoring can reliably detect. This review highlights three gaps and outlines future research directions in the field of SLB thermal safety and management: limited aged-cell thermal characterization by degradation pathway, insufficient diagnostic validation under industrial-throughput conditions, and the incomplete translation of screening outputs into design rules.

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO4)2 and Zn(OTf)2, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The presence of carboxyl groups in CSAM and the existence of O-H bonding facilitated ion movement, resulting in enhanced ionic conductivity and preventing dendrite formation. Incorporating these hydrogels with high-performance zinc salts, such as zinc triflate (Zn(OTf)2), resulted in stable symmetric cell cycling over 4000 h with a uniform voltage profile under 1 mA/cm2 and a low overpotential of around 53 mV cycling with CSAM. Rate-dependent full-cell testing showed that the PBI solid electrolyte delivers higher capacity retention at different current densities, whereas CSAM exhibits markedly better long-term stability, even at low voltages, owing to its effective dendrite suppression, which helps preserve cathode performance over extended cycling.

Comment réutiliser les batteries lithium-ion ? Ce n'est pas une question simple : il s'agit d'un défi technologique, scientifique et économique qui demande également un engagement politique et personnel.

Accurately assessing battery health across mixed datasets remains a challenge due to differences in chemistry, format, and usage history. This study presents a reproducible framework for preparing battery cycling data using incremental capacity analysis (ICA), with the aim of supporting machine learning (ML) workflows across both first-life and second-life battery datasets. The methodology includes IC curve generation, feature extraction, encoding and scaling, feature reduction, and unsupervised learning exploration. A two-tiered outlier detection system was introduced during preprocessing to flag edge-case samples. Two clustering algorithms, K-means and HDBSCAN, were applied to the engineered feature space to explore patterns in the IC feature space. K-means revealed broad health-related groupings with overlapping boundaries, while HDBSCAN identified finer clusters and flagged additional ambiguous samples as noise. To support interpretation, PCA and t-SNE were used to visualise the feature space in reduced dimensions. Rather than using clustering as a classification tool, the resulting cluster and noise labels are proposed as structure-aware meta-features for supervised learning. The framework accommodates heterogeneous battery datasets and addresses the challenges of integrating data from mixed sources with varying histories and characteristics. These outputs provide a structured foundation for future supervised classification of battery state of health.

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.

Accurate modeling of Battery Energy Storage Systems (BESS) is essential for optimizing system performance, ensuring operational safety, and extending service life in applications ranging from electric vehicles (EV) to large-scale grid storage. However, the simplifications inherent in conventional battery models often hinder optimal system design and operation, leading to conservative performance limits, inaccurate State-of-Energy (SOE) estimation, and reduced overall efficiency. This paper presents a framework for advanced battery modeling, developed to achieve higher fidelity in SOE estimation and improved power-capability prediction. The proposed model introduces a dynamic energy-based representation of the charging and discharging processes, incorporating a functional dependence of instantaneous power on stored energy. Experimental validation confirms the superiority of this modeling framework over existing state-of-the-art models. The proposed approach reduces SOE estimation error to 0.1% and cycle-time duration error to 0.82% compared to the measurements. Consequently, the model provides more accurate predictions of the maximum charge and discharge power limits than state-of-the-art solutions. The enhanced predictive accuracy improves energy utilization, mitigates premature degradation, and strengthens safety assurance in advanced battery management systems.

Silicon oxide/graphite (SiOx/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase (SEI) growth as independent or unidirectionally coupled processes, neglecting their bidirectional interactions. Here, we develop an electro–thermal–mechanical–gaseous coupled model to capture the dominant degradation processes in SiOx/Gr anodes, including SEI growth, gas generation, SEI formation on cracks, and particle fracture. Model validation shows that the proposed framework can accurately reproduce voltage responses under various currents and temperatures, as well as capacity fade under different thermal and mechanical conditions. Based on this validated model, a mechanistic analysis reveals two key findings: (1) Gas generation and SEI growth are bidirectionally coupled. SEI growth induces gas release, while accumulated gas in turn regulates subsequent SEI evolution by promoting SEI formation through hindered mass transfer and suppressing it through reduced active surface area. (2) Crack propagation within particles is jointly governed by the magnitude and duration of stress. High-rate discharges produce large but transient stresses that restrict crack growth, while prolonged stresses at low rates promote crack propagation and more severe structural degradation. This study provides new insights into the coupled degradation mechanisms of SiOx/Gr anodes, offering guidance for performance optimization and structural design to extend battery cycle life.