Search Results (928)

Standalone photovoltaic systems play an important role in providing reliable renewable energy in remote areas. These systems depend heavily on battery energy storage, especially lithium iron phosphate batteries, which are known for their safety and long cycle life. However, battery degradation remains a major challenge, as high charging currents, temperature variations, and wide state-of-charge fluctuations introduce electro-thermal stress that reduces the useful lifetime of the storage system. To address this issue, this paper presents a Flexible Power Point Tracking (FPPT) strategy supported by a fuzzy-logic-based controller. In this context, battery stress refers to the combined electrochemical and thermal stress induced by high charging currents, elevated operating temperatures, and large state-of-charge (SOC) excursions, which are known to accelerate ageing mechanisms and capacity fade. Based on a review of the existing literature, most FPPT and lifetime-oriented control studies have focused on lithium-ion batteries such as NMC or LCO chemistries, while limited attention has been given to lithium iron phosphate (LiFePO₄) batteries. The goal is to limit battery stress by reducing current peaks, mitigating temperature rise, and smoothing state-of-charge variations, thereby improving battery lifetime without compromising the stability of the standalone PV system. A complete PV–battery model is developed in PLECS and tested using one-year irradiance, temperature, and load data from Perth, Australia. The results show that the FPPT–Fuzzy controller reduces current peaks, stabilises the state of charge, and lowers the thermal impact on the battery when compared with traditional MPPT. As a result, overall degradation decreases and the battery lifetime is extended by approximately 7%. These findings demonstrate that FPPT is a promising method for improving the long-term performance of renewable energy systems based on lithium iron phosphate battery storage. 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 development of electric vehicles (EVs) will inevitably consume substantial scarce resources, posing risks and challenges to their supply chains. From a life cycle perspective, this study innovatively incorporates charging piles (CPs) into the research scope. Six scenarios are established to quantitatively analyze the scrap and recovery volume of 20 metallic and 3 non-metallic strategic mineral resources in lithium-ion batteries (LIBs) and CPs for China’s passenger EVs during 2010–2050. Under six scenarios, the results show that Al in LIBs and Fe in CPs have the highest scrap volumes, increasing from 2.69 t in 2010 to 2.98 × 10⁶ t in 2050 and from 34.76 t in 2024 to 1.14 × 10⁶ t in 2050, respectively. In contrast, Co in LIBs and Zr in CPs have the smallest scrap volumes, increasing from 0.22 t in 2012 to 8.25 × 10⁴ t in 2050 and from 8.8 × 10⁻⁷ t in 2024 to 1.52 × 10⁻⁵ t in 2050, respectively. Over 97% of Li, Co, Ni, and Al originates from LIBs during 2026–2050, while Fe and Cu from CPs show notable growth, underscoring recycling urgency. Recycle-demand analysis in LIB reveals the gap rate for nine elements. Seven elements’ gap rates are 0.39–0.81 (GI = 80%) and 0.25–0.75 (GI = 100%), while Fe’s gap rate turns to 0 in 2045 due to LFP phase-out and P’s gap rate reaches −1.22 (GI = 80%) and −1.77 (GI = 100%) in 2045 before rebounding. Full article

Electronic waste (e-waste) is a growing solid waste stream with largely undisclosed and poorly characterized fluorinated constituents. We evaluated per- and polyfluoroalkyl substances (PFAS) leachability from four e-waste components (phone screens, phone plastics, capacitors, and Lithium-ion batteries) using a 30-day deionized water leaching test. PFAS were extracted by solid-phase extraction using weak anion exchange (WAX) cartridges and analyzed with a liquid chromatography triple-quadrupole mass spectrometer. In addition, the PFAS chemical profiles of e-waste components were characterized by non-targeted analysis. Leachable sums of detected PFAS (∑PFAS) were highest in phone screens (1739–1932 ng·kg⁻¹) and phone plastics (1575–2197 ng·kg⁻¹) and an order of magnitude lower in Lithium-ion batteries (148–158 ng·kg⁻¹) and capacitors (147–243 ng·kg⁻¹). Short-chain perfluoroalkyl acids (PFAAs) (e.g., PFBA, PFHxA) and legacy acids (e.g., PFOA, PFNA) were more prevalent in phone screens/plastics, whereas capacitors and batteries showed mixed sulfonate/carboxylate patterns (PFOS, PFHxS, and 6:2 FTS). Although capacitors and Lithium-ion batteries contained essential PFAS with high hazard potential at trace levels, phone screens and phone plastics pose a greater risk per mass due to higher ∑PFAS levels and larger volumes. Non-targeted analysis using Orbitrap Astral revealed CF₂/CF₂O homologous trends (confidence levels 2–3) with corroborating targeted findings. These findings highlight the need for PFAS-free alternatives, the disclosure of fluorinated additives, and stronger end-of-life management strategies to prevent PFAS releases from e-waste. Full article

Accurate prediction of lithium battery remaining useful life (RUL) is crucial for battery management systems to monitor battery health status. However, RUL prediction remains challenging due to capacity non-stationarity caused by capacity regeneration phenomena. Therefore, this study proposes a novel RUL prediction framework that combines a two-stage decomposition strategy with a parallel Informer-LSTM architecture. First, STL decomposition is employed to decompose the capacity sequence into trend, seasonal, and residual components. The VMD method further refines the residual component from STL, extracting the underlying multiscale subsignals. Subsequently, a parallel dual-channel prediction network is constructed: the Informer branch captures global long-range dependencies to prevent trend drift, while the LSTM branch models local nonlinear dynamics to reconstruct fluctuations associated with capacity regeneration. Experiments on the NASA dataset demonstrate that this framework achieves an MAE below 0.0109, an RMSE below 0.0160, and an R² above 0.9950. Additional validation on the Oxford battery dataset confirms the model’s robust generalization capability under dynamic conditions, with an MAE of 0.0017. This further demonstrates that the proposed RUL prediction framework achieves significantly enhanced prediction accuracy and stability, offering a reliable solution for battery health status detection in battery management systems. 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

Silicon oxide/graphite (SiO_x/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 SiO_x/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 SiO_x/Gr anodes, offering guidance for performance optimization and structural design to extend battery cycle life. 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

The development of lithium-ion batteries necessitates cathode materials that possess excellent mechanical and thermal properties in addition to electrochemical performance. As a prominent functional ceramic, the properties of spinel LiMn₂O₄ are governed by its atomic-level structure. This study systematically investigates the impact of Ni doping concentration on the mechanical and thermal properties of spinel LiNi_xMn_2−xO₄ via first-principles calculations combined with the bond valence model. The results suggest that when x = 0.25, the LiNi_xMn_2−xO₄ shows excellent mechanical properties, including a high bulk modulus and hardness, due to the favorable ratio of bond valence to bonds length in octahedra. Furthermore, this optimized composition shows a lower thermal expansion coefficient. Additionally, Ni doping concentration has a very minimal influence on the maximum tolerable temperature of the cathode material during rapid heating. Therefore, from the perspective of mechanical and thermal properties, this composition could be beneficial for improving the cycling life of the battery, since comparatively inferior mechanical properties and a higher thermal expansion coefficient make it prone to microcrack formation during charge–discharge cycles. Full article

Silicon is considered one of the most promising next-generation anode materials for lithium-ion batteries (LIBs) because of its very high theoretical specific capacity (≈3579 mAh·g⁻¹). However, its practical application is limited by severe volume expansion (>300%), an unstable solid electrolyte interphase (SEI), and low electronic conductivity. Recent progress in nanostructuring has significantly improved the electrochemical performance and durability of silicon anodes. In particular, nanosilicon particles, porous structures, and Si–carbon composites enhance structural stability, cycling life, and coulombic efficiency. These improvements arise from better mechanical integrity and more stable electrode–electrolyte interfaces. This review summarizes recent advances in nanostructured silicon anodes, focusing on particle size control, pore design, composite architectures, and interfacial engineering. We discuss how these nanoscale strategies reduce mechanical degradation and improve lithiation kinetics while also addressing the remaining challenges. Finally, future research directions and industrial prospects for the practical use of nanostructured silicon anodes in next-generation LIBs are outlined. Full article

This work investigated the impact of vehicle-to-grid (V2G) cycling on the service life of lithium-ion cells, using real-world V2G data from commercial electric vehicle (EV) battery chargers. Commercially available cylindrical lithium-ion cells were subjected to long-term storage and V2G cycling under varying state of charge (SOC), depth of discharge (DOD), and temperature conditions. The ageing results demonstrate that elevated temperature (40 °C) is the dominant factor accelerating degradation, particularly at a high storage SOC (>80% SOC) and increased cycle depths (30–80% SOC, 30–95% SOC). A comparison between V2G cycling and calendar ageing over a similar storage period revealed that shallow V2G cycling (30–50% SOC) leads to comparable capacity fade to storage at a high SOC (≥80% SOC). The comparative analysis indicated that 62% of a full equivalent cycle (FEC) of V2G cycling can be achieved daily, without compromising the cell’s lifetime, demonstrating the viability of V2G adoption during EV idle/charging periods, which can offer potential operational benefits in terms of cost reduction and emissions savings. Furthermore, this work introduced the concept of a V2X capability metric as a novel cell-level specification, along with a corresponding experimental evaluation method. Full article

With the increasing energy density of lithium-ion batteries, the heat dissipation performance of air-cooled battery energy storage cabinets has become a critical determinant of both system performance and service life. This performance depends strongly on the geometry of the airflow channels and their influence on the internal flow distribution. In this study, the internal flow field of a battery energy storage cabinet was analyzed, and the airflow-channel geometry was optimized using the BOBYQA algorithm. The results indicate that the risk of thermal runaway is largely associated with inadequate airflow design, which leads to localized heat accumulation. Geometric optimization of the airflow channels reduced the maximum hotspot temperature from 72.9 °C to 57.6 °C. The hotspots were concentrated at the tops of the battery modules. Modifications to the channel geometry increased the airflow velocity and improved its directionality in these regions, thereby reducing both the hotspot temperature and the extent of the affected area. Moreover, slightly increasing the inlet pressure while reducing the outlet pressure produced a more uniform temperature distribution across the tops of the battery modules. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article