Search Results (633)

In Australia’s National Electricity Market (NEM), community-scale battery energy storage systems (BESS) operate under five-minute price volatility and frequent negative pricing. However, unmeasured internal states and degradation processes constrain the effectiveness of rule-based and simplified optimisation methods for real-time arbitrage. To address these challenges, this study proposes a data-driven digital-twin framework for real-time management of a 1 MW/4 MWh grid-connected community BESS. The framework integrates a control-oriented single-particle model (SPM), an Unscented Kalman Filter (UKF)-based estimation layer for state-of-charge (SOC), state-of-health (SOH) and internal-state estimation and a degradation-aware nonlinear model predictive control (NMPC) strategy. Within this architecture, the SPM provides an interpretable electrochemical representation, the estimation layer reconstructs internal states from measurable signals, and the NMPC performs five-minute rolling arbitrage subject to voltage, power, and SOC constraints while accounting for ageing-related costs and ramp penalties. Simulation case studies based on high-volatility daily price profiles from four NEM regions indicate that the proposed framework can coordinate arbitrage-oriented dispatch, constraint-aware operation, and degradation-related cost consideration under the tested conditions. These results suggest the potential of the SPM–UKF–NMPC digital-twin architecture for supporting real-time community-scale BESS management, while further validation under forecast uncertainty and hardware or field conditions remains necessary. Full article

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This paper proposes a Newton-iteration-enhanced three-spectral-line RIFE algorithm implemented on a heterogeneous FPGA platform (Zynq-7000 SoC). The method performs coarse frequency estimation using the three-spectral-line RIFE to mitigate FFT fence effects, followed by Newton-based quadratic refinement, enabling high estimation accuracy with reduced FFT size. A fast–slow loop architecture is adopted, where the FPGA (PL) performs real-time interference suppression and the ARM (PS) handles system control and parameter updates. Experimental results show that, under static interference, the proposed method achieves a 10.9 dB improvement over direct estimation algorithms. Under chirp interference, it significantly outperforms both direct estimation and conventional iterative methods. In GNSS closed-loop tests, the proposed approach extends the anti-jamming margin to 82 dB J/S. Overall, the proposed method effectively balances estimation accuracy and processing latency, providing a practical solution for GNSS anti-jamming in high-dynamic environments. Full article

Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when the model is fully specified, but their online solve time increases rapidly with cluster size; learning-based methods are fast but often rely on soft constraint penalties or external feasibility repair. We propose the Physics-Constrained Mamba-3 MIMO Aggregator (PC-M3), an amortized, constraint-aware sequence model that integrates a MIMO Mamba backbone, a history-dependent differentiable projection, a sparse routing layer, and an aggregation–disaggregation consistency loop, scaling AI-EMS from a single battery to ten-thousand-vehicle clusters in one forward pass. PC-M3 assigns every EV to one channel of a multi-input multi-output (MIMO) state-space recurrence and embeds the per-vehicle state-of-charge, power and energy constraints as a differentiable in-loop projection, jointly producing the cluster-level flexibility envelope and the per-vehicle charging trajectory. A sparse Routing-Mamba mixture-of-experts layer adaptively allocates capacity to behaviourally distinct sub-populations without supervised labels, and a consistency-trained aggregation–disaggregation loop binds the predicted envelope to the executed dispatch, forming a digital-twin-style predictive EMS pipeline that couples cluster dispatch with per-vehicle SoC evolution. On a single NVIDIA A100, PC-M3 sustains 0.34 s inference for 10,000 EVs over a 24-h horizon, about 18× faster than an Informer baseline and 2.4× faster than PowerMamba. Evaluated on the open ACN-Data and ElaadNL workplace and public charging corpora and on a 10,000-vehicle NREL dsgrid-TEMPO 2030 stress test, PC-M3 reduces the normalised envelope Hausdorff distance from 9.7% (PowerMamba) to 3.4%, cuts closed-loop cluster tracking RMSE from 1.45 MW (model predictive control) to 0.82 MW, and maintains zero observed feasibility violations with respect to the specified or imputed per-vehicle polytopes on every evaluated session. The framework provides a scalable, predictive, constraint-aware AI-EMS for V2G/G2V virtual-power-plant operation of large EV fleets. Full article

The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and RUL estimation based on the analysis of the NASA battery dataset, characterized by significant heterogeneity in operating conditions, temperature regimes, and cycle durations. The study combines a physically informed feature engineering process with machine learning models, including tree-based ensembles, kernel methods, and neural networks. The dataset is analyzed from an electrochemical, thermal, and impedance perspective, highlighting the role of internal resistance evolution, SOC–voltage characteristics, and temperature dynamics as indicators of battery degradation. Based on these observations, two regression problems are formulated: a local window-based representation for SOC estimation and a cycle-level representation for RUL prediction. Particular attention is devoted to the impact of dataset heterogeneity, feature construction, and target representation on the predictive behavior of the considered models. In addition, the work investigates the effect of normalized RUL representations and provides an interpretability-oriented comparison of the learned regressors through feature-importance analysis and parity plots. Experimental results show that SOC estimation is a comparatively well-conditioned problem, achieving high accuracy across nonlinear models, although the dominant role of temporal and current-derived features highlights the strong dependence of the prediction task on the structure of the experimental protocol. In contrast, RUL prediction exhibits significantly higher complexity due to long-term degradation uncertainty and inter-battery variability. The introduction of a normalized RUL representation substantially improves prediction accuracy and stability, particularly for ensemble-based approaches. Feature importance analysis confirms that capacity-related variables dominate RUL estimation, while voltage, temporal, and current-derived features play a central role in SOC prediction. Overall, the results show that physically interpretable feature construction combined with ensemble learning methods provides an effective framework for battery state estimation and degradation analysis under heterogeneous operating conditions. Full article

Accurate battery state-of-charge (SOC) estimation under dynamic operating conditions remains challenging because battery responses are nonlinear, history-dependent, temperature-sensitive, and prone to transient disturbances. To address this problem, this paper proposes a representation-level temporal–frequency symmetry framework, termed the Joint Temporal–Frequency Cross-Domain Attention Network (JTFCD-Net), for joint SOC estimation and voltage reconstruction. Here, symmetry denotes aligned latent representations rather than physical invariance: temporal and frequency-aware views are derived from the same battery process, mapped into the same latent space, and kept at identical temporal resolution and hidden dimensionality. A temporal aggregation block extracts local dynamics at multiple receptive fields, and a Temporal Attention Aggregation Module (TAAM) captures long-range dependence. A Frequency-Aware Attention Module (FAM) then uses global spectral statistics to perform lightweight channel recalibration, thereby injecting coarse frequency-domain information into the temporal representation while preserving the hidden feature shape. A Cross-Domain Attention Module (CDAM) performs bidirectional cross-attention, allowing the two views to query and exchange information. The fused representation is decoded by a main SOC head and an auxiliary voltage reconstruction head, which preserves voltage-response dynamics in the shared representation. Experiments on the CALCE A123 benchmark under multiple fixed ambient temperatures and operating conditions show that JTFCD-Net yields consistently lower errors than the selected baseline methods, while ablation studies confirm the contribution of cross-domain fusion and auxiliary voltage supervision. External validation on the NASA Ames battery aging dataset is also conducted as an independent laboratory-scale cell benchmark. These results indicate that combining temporal modeling with frequency-aware representation learning is a promising direction, although deployment value still requires validation in real BMS settings. Full article

Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems. Full article

Accurate estimation of the internal spatial-temporal temperature distribution is crucial for the safety and performance management of lithium-ion batteries. However, traditional lumped parameter models overlook spatial gradients, while numerical methods for partial differential equations (PDEs) incur high computational costs. This paper proposes a hard constraint physics-informed neural network (HCPINN) framework for the real-time reconstruction of the axial temperature field in 18,650 cylindrical batteries. By restructuring the neural network’s solution space through distance functions, the Robin boundary conditions are strictly embedded as hard constraints, ensuring exact satisfaction of the prescribed Robin boundary conditions within the mathematical model and eliminating boundary loss terms. An electro-thermal coupled model considering the Arrhenius effect and state-of-charge (SOC) dependent internal resistance is integrated into the loss function to capture the nonlinear heat generation dynamics. Experimental validation across discharge rates from 1C to 4C demonstrates that the HCPINN achieves high estimation accuracy with a mean absolute error (MAE) below 0.34 °C. Furthermore, by leveraging the continuous differentiability of the model, this study quantifies the evolution of spatial temperature gradients and reveals the ideal heat transfer coefficients required for thermal equilibrium are inverted, providing a quantitative basis for the design of advanced battery thermal management systems (BTMS). Full article

Reconfigurable battery modules can dynamically adjust the connection topology among battery cells, significantly improving the energy utilization efficiency of battery energy storage systems. However, existing state estimation methods focus primarily on individual battery cells. Frequent topology changes cause traditional State of Charge (SOC) estimation algorithms to accumulate large errors due to mismatches in equivalent capacity and internal resistance, making them ineffective for reconfigurable battery modules. To address this limitation, this paper proposes a Gated Recurrent Unit–Transformer architecture for precise SOC estimation in reconfigurable battery modules. The model uses a Gated Recurrent Unit to capture the temporal continuity of electrochemical evolution and employs the Transformer’s self-attention mechanism to analyze discrete topology changes. Experimental results show excellent estimation accuracy across different initial SOC levels, with a mean absolute error as low as 0.085% at full charge and 0.035% at 50% SOC. The architecture demonstrates strong topology self-identification capability and maintains high robustness even under abrupt voltage steps caused by reconfiguration. This method provides accurate and reliable state estimation for large-scale two-layer reconfigurable battery systems while reducing control complexity and improving operational efficiency. Full article

Accurate battery state-of-charge (SOC) estimation is a core function of battery management systems (BMSs) for electric vehicles (EVs), as it directly affects energy management, safety, and reliability. However, battery aging significantly degrades the accuracy of conventional SOC estimation methods by causing capacity loss, increased internal resistance, and changes in voltage response characteristics. To address these issues, this study proposes an aging-aware SOC estimation method that combines an equivalent-circuit model (ECM) with an extended Kalman filter (EKF). In the proposed framework, aging effects are explicitly incorporated by using offline-identified SOH-dependent model parameters, including effective capacity, RC parameters, and SOC–OCV characteristics, and scheduling these parameters within the EKF prediction and correction process according to the available SOH information. Furthermore, the performance of the proposed method is experimentally validated under an Urban Dynamometer Driving Schedule (UDDS) using cylindrical lithium-ion cells with large current fluctuations. The experimental results demonstrate that the proposed aging-aware EKF maintains stable SOC estimation performance not only in the initial battery state but also throughout the gradual aging process and up to the end of battery life. These results demonstrate the potential of SOH-scheduled, aging-aware EKF-based SOC estimation to improve SOC accuracy in aged batteries under the investigated laboratory and dynamic load conditions. Full article

Accurate state of charge (SOC) estimation is essential for the reliable operation and energy management of electric agricultural machinery, particularly electric tractors operating under complex field conditions. This study aims to improve SOC estimation accuracy and robustness by proposing a hybrid method that integrates a gated recurrent unit (GRU) neural network with a fractional-order extended Kalman filter (FOEKF). The GRU model is employed to capture the nonlinear behavior of lithium-ion batteries, while the FOEKF is used to mitigate noise and compensate for model uncertainties, forming a coupled data-driven and model-based framework. Experiments were conducted on lithium-ion batteries for electric tractors under hybrid pulse power characterization (HPPC) conditions at 15 °C, 25 °C, and 35 °C. These experiments can effectively simulate the dynamic power fluctuation characteristics of the battery caused by variations in electric tractor operating conditions during agricultural operations in different seasons. Experimental results demonstrate that, compared with conventional GRU and FOEKF methods, the proposed GRU-FOEKF method achieves lower estimation errors and improved robustness. In particular, at 25 °C, the proposed method achieves an MAE of 0.9% and an RMSE of 1.1%, outperforming the compared algorithms. These findings indicate that the proposed strategy is a feasible and effective solution for battery management systems in electric agricultural machinery, contributing to the development of smart and sustainable agriculture. Full article

Lithium-ion pouch cells exhibit significant irreversible expansion during long-term cycling, which determines overall performance and induces degradation failure without an appropriate mechanical fixture. However, the synergistic mechanism of mechanical pre-torque and battery state on battery electrochemical performance is unclear. To address this issue, this study reveals the electrochemical characteristic evolution of commercial lithium-ion pouch cells during cycling degradation, under varying mechanical pre-torques (0 N·m, 0.5 N·m, 1 N·m, and 1.5 N·m) and at different states of charge (SOCs, 0%, 25%, 50%, 75%, and 100%). Results indicate that moderate pressure (0.5 N·m) optimizes the electrode–electrolyte contact, reducing solid–electrolyte interphase resistance (R_SEI), ohmic resistance (R_O), charge transfer resistance (R_ct), and Warburg coefficient (W) by over 55%, 60%, 30% and 20%, respectively, compared with the free state. High pressure (1.5 N·m) induces impedance rebound due to pore compression, with the increment ranging from 20% to 40%. Furthermore, synergistic impact analysis proves that pressure alters impedance sensitivity to SOC, with changing rates amplifying from <5% per SOC unit under low pressure to 10–15% under high pressure, particularly exacerbating interface passivation at low SOC and side reactions at high SOC. Moreover, a Gaussian process regression (GPR) based adaptive SOC estimation model is developed, incorporating impedance features and pressure paths, achieving a root mean square error of 2.1% and enhancing accuracy by 10–15% over conventional methods in high-pressure scenarios. This study provides guidance for the next-generation pouch cell module design and management. Full article

The battery management system (BMS) is the core of ensuring the safety and performance of new energy vehicles, and real-time high-precision estimation of battery state of charge (SOC) is its key function, which directly affects battery safety, endurance, and service life. Faced with the challenges brought by high energy density and ultra-fast charging technology, lithium-ion batteries exhibit strong nonlinear and time-varying characteristics, making it difficult for existing SOC estimation methods to balance computational efficiency and accuracy. This study proposes a Bayesian-based Hammerstein multi-model (MM) fusion algorithm for accurate lithium battery SOC estimation across a wide temperature range, especially under low-temperature conditions. First, two Hammerstein SOC submodels are constructed: a traditional polynomial Hammerstein model and a TPA-Hammerstein model incorporating the temporal pattern attention mechanism. Second, KV-ADAM is employed for parameter training and identification of the submodels. Finally, a Bayesian weighted fusion strategy is used to dynamically integrate the outputs of the two submodels. The experimental results show that this method significantly improves the accuracy and robustness of SOC estimation, overcomes the limitations of a single model under complex dynamic conditions, provides an effective solution for lithium battery SOC estimation, and helps the safe operation of electric vehicles and the sustainable development of the industry. Full article

This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N ratio, SOC, total nitrogen (TN), and their respective stocks were assessed using standard analytical methods and statistical tests (Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlation and regression analysis). Land use significantly affected all soil parameters except pH. Forest soil showed lower bulk density and lower SOC stocks compared with grasslands. Unmown meadows exhibited the highest SOC and TN concentrations and stocks, while potato fields recorded the highest bulk density and elevated TN stocks, reflecting intensive management impacts on surface soil properties. Forest soils displayed species-specific patterns, with Scots pine and Silver fir showing comparatively lower SOC and TN stocks attributable to historical degradation and site limitations. As the study focused on the uppermost soil layer (0–5 cm), the results should be interpreted more as indicators of surface soil dynamics rather than as estimates of total topsoil carbon and nutrient storage. Correlation analysis revealed strong positive relationships among SOC, TN, and the C:N ratio, and strong negative relationships between SOC and both bulk density and coarse fraction in managed agricultural lands. The findings demonstrate that minimizing soil disturbance and maintaining permanent vegetation cover—particularly through conservation of unmanaged grasslands—offer great capacity for enhancing the soil organic matter accumulation in mountainous ecosystems. Full article

Accurate online tracking of state of charge (SOC) inconsistency in lithium-ion battery packs is essential for safety. It is equally critical for effective battery management in real-world operation. To achieve robust performance in dynamic battery environments characterized by temperature fluctuations and cell aging, a method combining enhanced Rime optimized-unscented particle filter (ERIME-UPF) with cubature smooth variable structure filter-varying boundary layer (CSVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the hysteresis effect dynamic migration model, and the Rint model. First, module SOC is estimated using an ERIME-UPF, which adaptively adjusts the noise covariances of UPF via the enhanced RIME optimizer. Simultaneously, CSVSF-VBL employs the Rint model to estimate cell SOC inconsistencies, incorporating capacity and internal resistance coefficients into the second-order performance chattering to better capture cell inconsistency. Experiments focus on LiFePO₄ batteries under various inconsistencies, temperature, and aging states. The results show that ERIME-UPF achieves an average mean absolute error (MAE) of 0.33% for module SOC estimation, while CSVSF-VBL achieves a peak MAE of 3.28% for cell SOC estimation. Demonstrating superior accuracy and robustness in tracking SOC inconsistency under dynamic and degraded operating conditions. Full article

State of charge (SOC) estimation is a key function in battery management systems (BMSs) because it directly affects safe operation and available energy prediction. In embedded BMS platforms, information from multiple cells must be processed within tight computation and memory budgets. The estimator therefore needs to balance accuracy and implementation cost. This paper presents a lightweight SOC estimation method based on the relationship between open circuit voltage and state of charge (OCV–SOC) in lithium-ion batteries, together with a standalone gauge IP based on finite-state machine (FSM) control. The reference OCV–SOC curve of a commercial 3.7 V lithium-ion cell is approximated by a two-region quadratic model. The IP estimates OCV from the measured terminal voltage with equivalent series resistance (ESR) correction and updates SOC iteratively. To obtain predictable runtime behavior and to suppress oscillatory behavior near convergence, the hardware combines a 1-LSB termination rule with a guard based on a maximum iteration count of $N_{max}=10$. Real-time validation on an FPGA-based battery measurement testbed achieves an overall normalized mean absolute error (NMAE) of 1.6% over charge and discharge data. When synthesized for an Artix-7 XC7A100T, the proposed gauge IP used only 504 LUTs (0.79%) and 580 FFs (0.46%). A TSMC 28 nm MPW implementation further demonstrates feasibility for integration at chip level. Full article