Search Results (1,692)

For lithium-ion batteries, reliable fault detection for charging voltage is essential for operational safety and thermal failure prevention. However, existing battery monitoring solutions face a dual challenge: task-specific models are primarily challenged by limited transferability, while powerful foundation models impose prohibitive computational demands that preclude their integration into resource-constrained edge devices. To address these challenges, this paper proposes a lightweight foundation model for fault detection built upon the IBM Granite TinyTimeMixer (TTM) foundation model. Firstly, we fine-tune the pre-trained TTM backbone with a hybrid loss using only few-shot normal charging sequences, enabling the model to learn the healthy voltage dynamics of batteries. Secondly, a dual-track data pipeline is proposed to adapt to irregular data, where a regular inference grid is generated in parallel with raw asynchronous measurements being retained for preserving vital high-frequency components. Thirdly, a vertical residual alignment mechanism is introduced to align irregular measurements with a continuous prediction curve derived from the TTM model’s grid prediction, enabling precise residual computation despite sampling mismatches. Finally, an empirical 99.99th quantile extreme threshold is calibrated using normal residual distributions to suppress false alarms caused by heavy-tailed sensor noise. Experiments on a lab dataset of 174 battery cells demonstrate that the proposed foundation model detects all fault batteries with zero false positives, which validates its effectiveness and robustness in battery fault detection. Full article

Buried interfaces and interphases often govern performance loss in rechargeable batteries, although their functions are frequently inferred from spatially averaged composition, surface-sensitive measurements, or cell-level electrochemical response. In this Perspective, an interface denotes the geometrical boundary between adjacent phases, whereas an interphase denotes a finite-thickness region whose composition or structure differs from those of the adjoining bulk phases. Rather than organizing the discussion by focused ion beam (FIB) modality or battery chemistry alone, we adopt an architecture-first, evidence-bounded framework and compare three classes of buried-interface architecture: engineered particle coatings; electrochemically generated solid electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI) regions together with lithium-metal deposits; and solid–solid contacts in all-solid-state batteries. For each class, the formation route and required function are related to spatial descriptors, including thickness distribution, lateral continuity, pore or gap topology, chemical gradients, contact area, and contact retention. FIB-enabled cross-sectioning, tomography, and correlative spectroscopy can register morphology, chemistry, and contact geometry within a common spatial frame, but they do not directly measure ionic conductivity, electronic leakage, adhesion energy, or local reaction rate. Such functional attribution therefore requires complementary electrochemistry, spectroscopy, modeling, temporal observation, and representative sampling. Across the three classes, durable interfacial function depends on chemically selective transport pathways that remain spatially continuous and mechanically viable during processing, cycling, and storage. Full article

With the rapid development of recycling and secondary utilization of end-of-life battery materials, it is crucial to clarify the impact of full-lifecycle degradation on the thermal safety limits of lithium-ion batteries. This study focuses on a 16 Ah NCM613|graphite pouch battery. First, it analyzes the evolution of capacity decay, thickness expansion, and internal resistance during cycling at room temperature (25 °C) and high temperature (45 °C). Furthermore, an adiabatic accelerated calorimeter (ARC) is employed to investigate the influence of different states of health (SOH) levels (95% and 85%) on the battery’s thermal runaway characteristics. The findings indicate that, macroscopically, batteries in all states follow similar voltage–temperature failure pathways, with mass loss rates confined to a narrow range of approximately 16%, emphasizing the low catastrophic potential of mid-nickel chemistry. However, the microscopic kinetic mechanisms exhibit significant anisotropy: although thickness and internal resistance display no apparent abrupt increase during the late stage of room temperature aging, the capacity exhibits a highly nonlinear plunge behavior. The severe internal lithium plating side reaction triggered by this phenomenon causes the self-heating onset temperature to drop rapidly from 130.0 °C in the fresh state to 79.7 °C. Concurrently, the activation energy of the exothermic side reaction, fitted using a simplified Arrhenius equation, exhibits a non-monotonic variation with aging progress. In the early stages of aging at 95% SOH, due to high temperatures promoting more significant growth of the interfacial film or moderate film formation at room temperature enhancing interfacial thermal stability, the activation energies for both aged batteries increase, and the energy barrier at high temperatures is slightly higher than at room temperature; however, during the deep aging stage at 85% SOH, due to the degradation of active material components and the emergence of lithium plating characteristics, the energy barrier significantly decreases, with high-temperature-aged batteries exhibiting a greater reduction, highlighting the cumulative negative impact of prolonged high-temperature exposure on thermal safety. The research provides a core scientific basis for establishing a battery safety early warning and dynamic health management system covering the entire lifecycle. Full article

Microgrids are emerging as a promising solution to enhance renewable energy integration, energy reliability, electricity access, and sustainability in Morocco. This paper reviews the development of sustainable microgrids in the Moroccan context by analyzing existing projects, system configurations, control approaches, and energy management strategies. In line with Morocco’s objective of reaching 52% renewable electricity capacity by 2030, the reviewed studies show that hybrid microgrids combining photovoltaic, wind, battery storage, diesel backup, and pumped hydro storage can improve energy autonomy, reduce dependence on fossil fuels, and support a more sustainable energy transition. Across the reviewed case studies, reported performance indicators include renewable energy penetration of up to 97%, a Loss of Power Supply Probability (LPSP) of 0.0489, Levelized Cost of Energy (LCOE) values ranging from 0.038 to 0.17 USD/kWh, and energy cost reductions of up to 20.7% in building-integrated photovoltaic applications. These values are study-specific and should be interpreted as indicative performance outcomes rather than directly comparable benchmarks, since they depend on system size, load profile, storage technology, tariff structure, and optimization assumptions. The review also highlights the role of advanced control and optimization techniques, such as particle swarm optimization, model predictive control, equilibrium optimizer, and adaptive energy management systems, in improving power balance, reliability, cost-effectiveness, and sustainability. Finally, this paper identifies the main technical, economic, regulatory, and institutional barriers limiting large-scale sustainable microgrid deployment in Morocco and proposes recommendations to support decentralized, resilient, and environmentally sustainable renewable energy systems. Full article

Vehicle-to-grid (V2G) integration in commercial building microgrids (CBMGs) offers a promising path for grid support, economic arbitrage, and resilience enhancement. However, practical implementation is hindered by the optimization–execution gap, where high-level aggregated commands fail to match low-level physical charger capacities and individual battery boundaries, as well as by the lack of sociotechnical coupling under extreme weather events, where vehicle owner range anxiety dominates. To address these challenges, a closed-loop multi-timescale energy management system for V2G-enabled CBMGs under exogenous meteorological conditions is proposed. The framework features an integrated four-layer cyber–physical control architecture connecting macroscopic day-ahead scheduling, receding-horizon model predictive control (MPC), discrete real-time parking slot allocation with hardware safety boundary constraints, and equipment-level power flow execution. To handle extreme events, an exogenous meteorological stress index is formulated to quantify ambient structural hazards and temperature deviations, which are then mapped to owner range anxiety and loss-aversion behaviors using prospect theory. Rather than relying on heuristic rule-switching, the optimizer executes a smooth and continuous transition from normal economic peak-shaving to active pre-disaster energy reservation and load demand survival. The cyber–physical system is validated using high-fidelity simulations under typical summer and winter blizzard scenarios. The results demonstrate that the proposed hierarchical architecture successfully eliminates optimization–execution mismatches and guarantees zero load shedding. Furthermore, sensitivity analyses establish the optimal system configuration with the critical defense tolerance of 0.6 and the baseline anxiety ratio of 4, which successfully resolves the trade-off between premature defensive actions and insufficient energy reserves while considering human behavioral uncertainty. Full article

The rapid growth of battery energy storage systems, renewable energy integration, electric vehicles, and DC microgrids has significantly increased the demand for compact, efficient, and bidirectional isolated DC–DC converters. Conventional Dual Active Bridge (DAB) converters commonly employ high-frequency transformers to provide galvanic isolation and bidirectional power transfer. Although transformer-based DAB converters offer excellent performance, their magnetic components increase converter volume, weight, core losses, leakage inductance, manufacturing complexity, and overall cost. Consequently, recent research has explored alternative high-frequency energy transfer techniques based on capacitive coupling, aiming to reduce magnetic components while preserving efficient resonant power conversion.This paper proposes a Capacitively Coupled Dual Active Bridge (CC-DAB) converter employing high-power metallized polypropylene (MKPH) capacitors as the high-frequency energy transfer medium. The proposed converter operates at a relatively low switching frequency while investigating the safe operating conditions of the capacitive coupling network to ensure reliable and efficient power transfer. A microcontroller-based single-phase-shift (SPS) modulation strategy is implemented to generate the gate-driving signals of the full bridges, whereas the switching frequency is selected to achieve zero-voltage switching (ZVS) throughout the investigated operating range. The phase-shift angle ($\varphi$) regulates the transferred power by controlling the voltage difference between the primary and secondary bridges across the capacitive coupling network. The proposed converter is analyzed theoretically and validated through simulation and experimental testing. Experimental results demonstrate stable bidirectional power transfer, soft-switching operation, and a peak conversion efficiency of 91.3% at a relatively low switching frequency of 52 kHz. The experimental verification confirms the practical feasibility of capacitive coupling for resonant bidirectional power conversion and demonstrates its potential as an alternative architecture for low- and medium-power applications requiring compact size, high efficiency, reduced magnetic component requirements, and reversible energy transfer. Furthermore, the proposed topology contributes to the ongoing development of transformerless resonant converters by experimentally validating a high-frequency capacitive coupling network capable of supporting efficient bidirectional power flow under practical operating conditions. Full article

This paper proposes a Quantum-Machine-Learning-enhanced Double Deep Q-Network (QML-DDQN) for the supervisory control of battery–supercapacitor hybrid energy storage systems in islanded microgrids. This method combines a variational quantum circuit as a nonlinear state encoder with a DDQN decision layer for safe discrete dispatch. Three representative islanded cases, Island 1, Island 2, and Island 3, were used to evaluate the robustness under different scales, renewable profiles, and reliability requirements. Compared with deterministic optimization, predictive control, metaheuristics, and classical reinforcement-learning baselines, the proposed controller delivers the best overall trade-off among operating cost, renewable utilization, diesel reduction, and loss-of-power-supply risk. On the three-case averages, QML-DDQN reduces daily cost and LPSP by 0.99% and 4.04% relative to DDQN, by 2.91% and 7.32% relative to DQN, and by 9.09% and 16.63% relative to MILP; it also lowers curtailment and diesel share by up to 13.02% and 9.09%, respectively, across the same benchmark sets. The largest gains appear under volatility-dominated and stress-scenario conditions, where the quantum encoder strengthens the state representation, and the DDQN backbone mitigates value overestimation. These results highlight the practical advantages of the QML-DDQN as a resilient and high-value supervisory strategy for islanded hybrid energy storage operations. Full article

High penetration of distributed energy resources (DERs) is reshaping radial distribution networks, yet weather-dependent generation, variable demand, and feeder-level surplus–deficit imbalance can compromise voltage quality and coordinated operation. Existing planning approaches often optimize feeders independently and therefore provide limited insight into how local DER portfolios should support inter-feeder energy exchange under time-varying conditions. This study proposes a weather-driven two-stage Geese V-Formation Algorithm (GVFA) framework for planning DER integration and feeder coordination in a practical five-feeder 11 kV Tajeeyaat/North Baghdad system, with complementary validation on a five-instance IEEE 33-bus benchmark cluster. Stage 1 optimizes the siting and sizing of photovoltaic units, wind turbines, battery energy storage systems, capacitor banks, and feeder-specific auxiliary resources using backward/forward-sweep load flow. Stage 2 uses hourly surplus–deficit profiles to select tie-switch configurations and exchange capacities for feeder-to-feeder energy sharing. The framework is evaluated through convergence analysis, optimizer comparison, N-1 contingencies, and seasonal load-growth tests. For the practical system, 24 h aggregate losses decreased from 11,258.1571 to 3367.8481 kWh-eq, corresponding to a 70.0853% reduction. The minimum-voltage range improved from 0.9497–0.9898 to 0.9897–0.9997 p.u., while grid-import reduction reached 94.0270%. For the IEEE-33 cluster, 24 h aggregate losses decreased from 31,746.4712 to 6381.1403 kWh-eq, corresponding to a 79.8997% reduction. The minimum-voltage range improved from 0.8268–0.8632 to 0.9465–0.9683 p.u., while grid-import reduction reached 84.4596%. The framework provides a planning-oriented, sensor-ready decision-support basis for DER siting, voltage-support assessment, grid-import reduction, and candidate inter-feeder exchange corridors. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

To address challenges in state-of-health (SOH) estimation and remaining useful life (RUL) prediction for lithium-ion batteries under fast-charging conditions—strong nonlinear degradation, difficulty in explicit mechanism modeling, and unstable generalization due to distribution shifts across batteries and protocols—this paper proposes the PIKAN (Physics-Informed KAN) framework. Under the physics-informed machine learning (PIML) paradigm, PIKAN uses Kolmogorov–Arnold networks (KAN) to learn complex nonlinear degradation mappings. Leveraging automatic differentiation for derivative information, it constructs optimizable physics residuals to achieve end-to-end collaborative optimization of data fitting and physical consistency. For dynamics constraints, PIKAN adopts a unified scheme of “explicit mechanism constraints + implicit dynamics learning”: for explicitly characterizable mechanisms (e.g., Verhulst equation), residuals are directly constructed; and for hard-to-explicitly-express mechanisms (e.g., RUL prediction), DeepHPM is introduced to learn implicit dynamics terms and embed them into physics residuals, ensuring consistent modeling across tasks. Additionally, an uncertainty-based adaptive multi-task weighting strategy dynamically balances data loss, dynamics residual loss, and derivative-consistency loss, enhancing training stability and engineering applicability. Cross-protocol validation on the MIT–Stanford–Toyota fast-charging dataset (124 cells) shows: in the SOH task, PIKAN-D achieves RMSPE = 0.240/0.330 and MAE = 0.161/0.206 on #124/#100 (Case 1), outperforming PINN-D (RMSPE = 0.517/0.477); in Case 2 (stronger distribution shift), for cell #108, the RMSPE drops from 1.393 (1DCNN) to 0.699, improving robustness. In the RUL task, for #101 (Case 2), RMSE/MAE reduce from 52.12/40.70 (PINN-D) to 33.14/25.83, validating PIKAN’s effectiveness in cross-cell and multi-protocol scenarios. Full article

This study investigates the performance, energy flow, efficiency, and environmental impact of a C-segment battery electric vehicle (BEV). As BEVs are increasingly considered a sustainable alternative to conventional internal combustion engine vehicles, a detailed understanding of their energy utilization and operational emissions is essential. A MATLAB/Simulink-based vehicle model incorporating an 88.5 kWh battery pack, a 160 kW permanent magnet synchronous motor (PMSM), regenerative braking, and longitudinal vehicle dynamics was developed. The developed model was validated by comparing the simulated vehicle performance characteristics with the publicly available specifications and performance data of the reference TOGG T10F vehicle. The vehicle was evaluated under the WLTP Class 3 driving cycle, while the effects of aggressive and high-speed driving conditions were further investigated using the US06 and Artemis Motorway 150 cycles. The results indicate a net vehicle energy consumption of 136.4 Wh/km and a driving range of 623 km under WLTP conditions. The PMSM achieved average efficiencies of 93.4% in traction mode and 92.7% in regenerative braking mode, while the cumulative battery-to-wheel drivetrain efficiency reached 81.5%. In addition, approximately 19.9% of the consumed energy was recovered through regenerative braking. Vehicle emissions were also assessed using different electricity generation mixes based on the rated energy consumption, including charging losses, yielding operational emissions between 27.7 and 116.0 gCO₂e/km. The findings demonstrate that the developed model provides realistic performance predictions and confirm the potential of BEVs to achieve high efficiency and substantially lower emissions than conventional passenger vehicles. Full article

Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and may produce non-physical degradation trends due to the absence of domain knowledge constraints. To address these limitations, this work proposes a monotonic Physics-Informed Residual MLP neural network framework for SOH estimation using the NASA battery dataset (B0005, B0006, B0007, and B0018). The proposed model incorporates a physics-based monotonic degradation constraint by penalizing positive gradients of SOH with respect to cycle index, thereby enforcing physically consistent capacity fade behavior. A loss function is employed to improve robustness and enhance late-cycle learning. Experimental results demonstrate that the proposed approach achieves an RMSE of 0.0287, MAE of 0.0181, and MAPE of 2.69%, indicating accurate and stable SOH prediction across multiple degradation patterns. The use of physics-informed constraints markedly enhances deterioration consistency and diminishes overfitting relative to solely data-driven models. The proposed structure offers a faithful solution for State of Health estimation in practical battery management systems. Full article

Recycling of lithium-ion batteries (LIBs) remains challenging due to high energy requirements, losses of key elements like lithium, and the heterogeneity of waste streams arising from different cathode chemistries. This study evaluates multiple recycling methods for LIBs black mass (BM), to recover critical raw materials and other valuable components. Three types of BM including nickel–manganese–cobalt (NMC), lithium iron phosphate (LFP) and a heterogeneous mixture of cell phones and laptops (HL; German: Handy/Laptops), were treated using froth flotation, pyrometallurgy, and biohydrometallurgy and their respective recovery efficiencies were assessed. The flotation results revealed that the HL sample had the lowest mis-recovery of non-ferrous metals into the froth product (around 10%), leading to further flotation only for HL. During screening, 94–99% of iron, phosphorus, and carbon in LFP-type BM were recovered in the fine fraction (<45 µm), while 92–99% of lithium, cobalt, manganese, nickel, and carbon in NMC-type BM were recovered in the same fraction. During precipitation, 99% of iron and 100% of phosphorus were recovered from LFP bioleachates at pH 3, while ~97–100% of dissolved cobalt, manganese, and nickel were recovered from NMC bioleachates. These findings confirm that no single recycling method is optimal for all battery chemistries. Full article

In this paper, we present LightBAL, an ultra-lightweight deep learning framework for real-time active cell balancing and onboard balancing control in electric vehicle (EV) battery management systems (BMSs). Although active cell balancing can improve battery utilisation and performance, applying deep learning-based balancing control strategies remains prohibitive in typical embeddable BMS platforms because of the computational complexity and inference latency of deep models. In response to this issue, we propose an AI-physics-informed controller that forecasts the voltage difference of a single cell, the SoC variation, and the optimal balancing current based on proportional feedback closed-loop (FCLL) control. The introduced framework exploits wavelet-based adaptive denoising, multi-scale hierarchical feature learning using a cooperative Principal Component Analysis (PCA) and autoencoder feature extraction technique, and a lightweight One-Dimensional Convolutional Neural Network (Conv1D) coupled with Bidirectional Long Short-Term Memory (BiLSTM) (Conv1D-BiLSTM). The implemented lightweight network is further trained by model compression methodologies such as knowledge distillation and 8-bit quantisation-aware training, aiming for efficient deployment on edge devices. Experimental validation on the multivariate battery time-series dataset demonstrates that LightBAL achieves an F1-score of 96.64%, a balancing efficiency of 94.30%, and a Mean Absolute Error (MAE) of 0.0379, outperforming methods based on conventional ANN, LSTM, and CNN. LightBAL without compression takes only 1.26 s to conclude on a PC workstation; the inference latency of the embedded light model is as low as 28.7 ms. In addition, hardware-in-the-loop (HIL) validation on the Raspberry Pi 4 platform indicates that the framework can fulfil real-time inference requirements under normal operating conditions, taking 28.7 ms per balancing process. Simulation shows that the proposed approach significantly decreases cumulative balancing energy loss by 12.4% across several driving cycle conditions. Full article

Battery autonomy is one of the main factors limiting the endurance of autonomous underwater vehicles (AUVs). Conventional charging through electrical connectors is inconvenient in marine environments since connectors are exposed to corrosion and usually require manual intervention or docking procedures. Inductive wireless power transfer (WPT) avoids these drawbacks, although the conductive nature of seawater introduces additional effects, such as eddy current losses and parasitic capacitance between the coils. These effects modify the resonance conditions of the compensation network and, in turn, reduce the transfer efficiency. This paper presents the design and experimental assessment of an inductive charger for a commercial and specific underwater drone operating under seawater conditions. A square coil geometry, selected to match the available installation area on the vehicle, was analysed together with two compensation networks (LCC-S and LCC-LCC) and two coil designs with 20 and 25 turns. Based on an analytical characterisation, their performance was evaluated for different coil separations and operating temperatures. Among the analysed configurations, the LCC-S topology with 25 turns provided the best compromise between efficiency and tolerance to gap variations. A laboratory prototype was subsequently built and tested in saline water with NaCl concentrations of 2%, 3%, and 4%, reaching an efficiency close to 87% at 266 W. These results confirm that the proposed design is suitable for underwater wireless charging under representative marine salinity conditions. Full article