Search Results (162)

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way to describe the structure and interaction of battery cells, modules and packs. At the same time, battery applications often suffer from very limited labeled data, especially for new chemistries, extreme operating conditions and second-life use. This review analyzes how graph representation learning can be combined with few-shot learning to support key battery management tasks under such data-scarce conditions. We first introduce the basic ideas of graph representation learning, including models based on neighborhood aggregation, contrastive learning, autoencoders and transfer learning, and discuss typical data, model and algorithm challenges in few-shot scenarios. We then connect these methods to battery state estimation problems, covering state of charge, state of health, remaining useful life and capacity. Particular attention is given to approaches that use graph neural models, meta-learning, semi-supervised and self-supervised learning, Bayesian deep networks, and federated learning to extract transferable features from early-cycle data, partial charge–discharge curves and large unlabeled field datasets. Reported studies show that, with only a small fraction of labeled samples or a few initial cycles, these methods can achieve state and life prediction errors that are comparable to or better than conventional models trained on full datasets, while also improving robustness and, in some cases, providing uncertainty estimates. Based on this evidence, we summarize the main technical routes for few-shot battery scenarios and identify open problems in data preparation, cross-domain generalization, uncertainty quantification and deployment on real battery management systems. The review concludes with a research outlook, highlighting the need for pack-level graph models, physics-guided and probabilistic learning, and unified benchmarks to advance reliable graph-based few-shot methods for next-generation intelligent battery management. Full article

The rapid growth of new energy vehicles necessitates accurate battery state of health (SOH) assessment to ensure safety and reliability. However, real-world SOH estimation is challenging because users rarely perform full charge–discharge cycles, leaving only fragmented charging segments that obscure true battery capacity. To address this, we propose a data-driven method that reconstructs a virtual full-charge cycle. By clustering charging segments based on temperature and current, the approach creatively splices multiple incomplete curves from similar mileages and conditions into a complete charging profile. This enables robust full-capacity estimation on a large-scale real-world vehicle dataset, achieving estimation errors below 2% when compared with offline validation tests. The method offers a practical and scalable solution for SOH monitoring and fleet management using field data. Full article

Accurate estimation of the open-circuit voltage (OCV) as a function of state of charge (SOC) is critical for reliable battery-management system (BMS) design in lithium-ion battery applications. However, at low temperatures, polarization effects distort the measured OCV–SOC profile due to premature voltage cutoffs during low-rate testing. This paper presents an offsetting-based correction method that reconstructs the truncated portions of the OCV curve by extrapolating the charge/discharge data beyond the cutoff points using simple voltage offsets. The approach is applied entirely in post-processing, requiring no modification to standard test protocols. Experimental validation using Samsung EB575152 Li-ion cells across a wide temperature range (−25 °C to 50 °C) demonstrates that the method restores the full OCV span, reduces apparent capacity loss, and improves consistency across cells and temperatures. The proposed technique offers a practical and effective enhancement to standard OCV testing procedures for temperature-aware SOC modeling. Full article

As a critical energy component in electric vehicles, energy storage systems, and other applications, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for performance optimization and safety assurance. To this end, this paper proposes a hybrid model named Transformer–KAN, which integrates Transformer architecture with Kolmogorov–Arnold Networks (KANs) for precise SOH estimation of lithium-ion batteries. Initially, five health features (HF1–HF5) strongly correlated with SOH degradation are extracted from the historical charge–discharge data, including constant-voltage charging duration, constant-voltage charging area, constant-current discharging area, temperature peak time, and incremental capacity curve peak. The effectiveness of these features is systematically validated through Pearson correlation analysis. The proposed Transformer–KAN model employs a Transformer encoder to capture long-term dependencies within temporal sequences, while the incorporated KAN enhances the model’s nonlinear mapping capability and intrinsic interpretability. Experimental validation conducted on the NASA lithium-ion battery dataset demonstrates that the proposed model outperforms comparative baseline models, including CNN–LSTM, Transformer, and KAN, in terms of both RMSE and MAE metrics. The results indicate that the Transformer–KAN model achieves superior estimation accuracy while exhibiting enhanced generalization capabilities across different battery instances, indicating its strong potential for practical battery management applications. Full article

NMOSFET, whose gate is on the top of the n-p-n junction with gate oxide in between, is called the n-channel transistor. This bipolar junction underneath the gate oxide may provide an n-n-n-conductive channel as the gate is applied with a positive bias over the threshold voltage (V_th). Conceptually, the definition of an n-type or p-type semiconductor depends on whether the corresponding Fermi energy is higher or lower than the intrinsic Fermi energy, respectively. The positive bias applied to the gate would bend down the intrinsic Fermi energy until it is lower than the original p-type Fermi energy, which means that the p-type becomes strongly inverted to become an n-type. First, the thickness of the inversion layer is derived and presented in a planar 40 nm MOSFET, a 3D 240 nm FinFET, and a power discrete IGBT, with the help of the p (1/m³) of the p-type semiconductor. Different ways of finding p (1/m³) are, thus, proposed to resolve the strong inversion layers. Secondly, the conventional formulas, including the triode region and saturation region, are already modified, especially in the triode region from a continuity point of view. The modified formulas then become necessary and available for fitting the measured characteristic curves at different applied gate voltages. Nevertheless, they work well but not well enough. Thirdly, the electromagnetic wave (EM wave) generated from accelerating carriers (radiation by accelerated charges, such as synchrotron radiation) is proposed to demonstrate phonon scattering, which is responsible for the Source–Drain current reduction at the adjoining of the triode region and saturation region. This consideration of reduction makes the fitting more perfect. Fourthly, the strongly inverted layer may be formed but not conductive. The existing trapping would stop carriers from moving (nearly no mobility, μ) unless the applied gate bias is over the threshold voltage. The quantum confinement addressing the quantum well, which traps the carriers, is to be estimated. Full article

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. The framework integrates ensemble learning with an improved Transformer architecture to achieve accurate and stable performance across various degradation conditions. Specifically, multiple PiFormer base models are trained independently under the Bagging strategy to enhance generalization. Each PiFormer consists of a stack of PiFormer layers, which combines a cross-channel attention mechanism to model voltage–current interactions and a local convolutional feed-forward network (LocalConvFFN) to extract local degradation patterns from charging curves. Residual connections and layer normalization stabilize gradient propagation in deep layers, while a purely linear output head enables precise regression of the continuous SOH values. Experimental results on three datasets demonstrate that the proposed method achieves the lowest MAE, RMSE, and MAXE values among all compared models, reducing overall error by 10–33% relative to mainstream deep-learning methods such as Transformer, CNN-LSTM, and GCN-BiLSTM. These results confirm that the Bagging-PiFormer framework significantly improves both the accuracy and robustness of battery SOH estimation. Full article

This paper introduces a novel Lithium-Ion Battery (LIB) State-of-Charge (SOC) estimation approach that integrates Akima–Savitzky–Golay curve reconstruction with a Bayesian-optimized, adaptive Extended Kalman Filter (EKF). The method addresses crucial SOC estimation challenges by means of three foundational advancements: (i) a refined open-circuit voltage (OCV)-SOC curve reconstruction grounded in Akima interpolation coupled with Savitzky–Golay filtering, (ii) an adaptive EKF weighting strategy, and (iii) systematic hyperparameter value optimization executed through Bayesian optimization. Comprehensive performance validation utilizes an extensive dataset collected from LG HG2 18650 cells across temperatures of −20 °C to 40 °C, incorporating multiple standard driving cycles—namely HPPC, UDDS, HWFET, LA92, and US06 cycles. The proposed method achieves an improved estimation accuracy with an average Root Mean Square Error (RMSE) of 2.65% over the different operating conditions and temperature variations. Notably, the method markedly enhances SOC estimation reliability in the critical mid-SOC range (20–80%), while preserving the computational overhead necessary for real-time integration into Battery Management Systems (BMSs). The adaptive weighting successfully compensates for the present physical limitations, thereby delivering a resilient SOC estimation tailored for Electric Vehicle (EV) battery applications. Full article

The state of health (SOH) of lithium-ion batteries (LIBs) is a key parameter that is crucial for delaying their lifespan degradation and ensuring safe use. To further explore the potential of charge curves in SOH estimation for LIBs, this paper proposes a method based on multi-dimensional health features and a genetic algorithm–particle swarm optimization (GAPSO)–bidirectional gated recurrent unit (BiGRU) neural network for SOH estimation. First, we extracted differential thermal voltammetry curves from the charging curve and defined the peak, valley, and their positions. Then, based on the charging temperature curve, we defined the time at which the maximum charging temperature occurs and the average charging temperature. Subsequently, we validated the correlation between the aforementioned six health features and SOH using the Pearson correlation coefficient. Finally, we used the multi-dimensional health features as model inputs to construct the BiGRU estimation model and employed the GAPSO hybrid strategy to achieve global adaptive optimization of the model’s hyperparameters. Experimental results on different LIBs show that the proposed method has relatively high accuracy, with an average absolute error and root mean square error of no more than 0.2771%. The comparison results with various methods further verify the superiority of the proposed method. Full article

This work introduces 2-(azulen-1-yldiazenyl)-5-(thiophen-2-yl)-1,3,4-thiadiazole (L) as a functional monomer capable of forming stable, redox-active films with high affinity for lead in aqueous solutions. L was synthesized and characterized using physical chemical methods and electrochemistry. Polymer films of L were prepared through oxidative electro polymerization on glassy carbon electrodes in L solutions in 0.1 M TBAP in acetonitrile. They were characterized through electrochemistry. The surface of chemically modified electrodes (CMEs) prepared through controlled potential electrolysis (CPE) at variable concentrations, potentials, and electric charges was characterized through scanning electron spectroscopy, atomic force microscopy, and Raman spectroscopy, which confirmed the films’ formation. Electrochemical sensing of the films deposited on these CMEs was tested with respect to heavy metal (HM) ion analysis in aqueous solutions to obtain sensors for HMs. The obtained CMEs presented the best characteristics for the recognition of Pb among the investigated HMs (Cd, Pb, Cu, and Hg). Calibration curves were obtained for the analysis of Pb(II) in aqueous solutions, which allowed for the estimation of a good detection limit of this cation (<10⁻⁸ M) for non-optimized CMEs. The resulting CMEs show promise for deployment in portable environmental monitoring systems, with implications for public health protection and environmental safety. Full article

High-specific-energy lithium-ion batteries face accelerated degradation and safety risks. To ensure stable and safe operation of such batteries in electric vehicles throughout their service life, this study proposes a quantitative aging mechanism analysis method based on electrode equilibrium potential reconstruction under rest conditions. First, by integrating the single-particle electrochemical model with equilibrium potential reconstruction, a quantitative mapping framework between State of Charge (SOC) and electrode lithiation concentration is established. Subsequently, to address the strong nonlinearity between equilibrium potential and lithiation concentration, the State Transition Algorithm (STA) is introduced to solve the high-dimensional coupled parameter identification problem, enhancing aging parameter estimation accuracy. Finally, the effectiveness of the proposed method was validated using a commercial NCM622/graphite power cell as the research object, and the battery’s aging pathways were analyzed using differential voltage analysis (DVA) and incremental capacity analysis (ICA) methods. Experimental results indicate that the OCV curve fitting achieved a maximum Root Mean Square Error of 0.00932, while quantitatively revealing the degradation patterns of electrode lithiation degrees during aging under both fully charged (SOC = 100%) and fully discharged (SOC = 0%) states. Full article

This research presents an experimental analysis of the influence of atmospheric pressure plasma on the performance of a micro horizontal-axis wind turbine blade. The investigation was conducted using an NACA 4412 airfoil equipped with a dielectric barrier discharge (DBD) plasma actuator. The electrodes were configured asymmetrically, with a 2 mm gap and copper electrodes that are 0.20 mm in thickness. A high voltage of 6 kV was applied, resulting in a current of 0.071 mA and a power output of 0.426 W. Optical emission spectroscopy identified the excited components through the interaction of the high-voltage AC electric field with air molecules: ${\mathrm{N}}_2$, ${\mathrm{N}}_2^{+}$, ${\mathrm{O}}_2^{+}$, and O. The electrohydrodynamic force mainly results from the observed charged ions that, when accelerated by the electric field, transfer momentum to neutral molecules via collisions, leading to the formation of the observed jet plasma. The findings indicated a notable enhancement in aerodynamic performance attributable to the electrohydrodynamic (EHD) flow generated by the plasma. The estimated electrohydrodynamic force ($8.712\times {10}^{-4}$ N) is capable of maintaining the flow attached to the airfoil surface, thereby augmenting flow circulation and, consequently, enhancing the lift force. According to blade element theory, the lift and drag coefficients directly influence the torque and mechanical power generated by the wind turbine rotor. Schlieren imaging was utilized to observe alterations in air density and flow patterns. Lissajous curve analysis was used to examine the electrical discharge behavior, showing that only 7.04% of the input power was converted into heat. This indicates that nearly all input electric energy was transformed into EHD force by the atmospheric pressure plasma. Compared to traditional aerodynamic control methods, DBD actuators are a feasible alternative for small wind turbines due to their lightweight design, absence of moving parts, ability to be surface-embedded without altering blade geometry, and capacity to generate active, dynamic flow control with reduced energy consumption. Full article

Accurate parametric modeling of lithium-ion batteries is essential for battery management system (BMS) design in electric vehicles and broader energy storage applications, enabling reliable state estimation and effective thermal control under diverse operating conditions. This study presents a detailed characterization of lithium-ion cells to support advanced BMS in electric vehicles and stationary storage. A second-order equivalent circuit model is developed to capture instantaneous and dynamic voltage behavior, with parameters extracted through Hybrid Pulse Power Characterization over a broad range of temperatures (−10 °C to 45 °C) and state-of-charge levels. The method includes multi-duration pulse testing and separates ohmic and transient responses using two resistor–capacitor branches, with parameters tied to physical processes like charge transfer and diffusion. A weakly coupled electro-thermal model is presented to support real-time BMS applications, enabling accurate voltage, temperature, and heat generation prediction. This study also evaluates open-circuit voltage and direct current internal resistance across pulse durations, leading to power capability maps (“fish charts”) that capture discharge and regenerative performance across SOC and temperature. The analysis highlights performance asymmetries between charging and discharging and confirms model accuracy through curve fitting across test conditions. These contributions enhance model realism, thermal control, and power estimation for real-world lithium-ion battery applications. Full article

The inevitable decline in battery performance presents a major barrier to its widespread industrial application. Adaptive and accurate estimation of battery capacity is paramount for battery operation, maintenance, and residual value evaluation. In this paper, we propose a novel battery capacity estimation method based on an approximate open circuit voltage curve. The proposed method is rigorously tested using both lithium–iron–phosphate (LFP) and nickel–cobalt–manganese (NCM) battery packs at multiple charging rates under varied aging conditions. To further enhance capacity estimation accuracy, a voltage correction strategy is implemented utilizing the incremental capacity (IC) curve. This strategy also verifies the potential benefits of increasing the charging rate to shorten the overall test duration. Eventually, the capacity estimation error is consistently controlled within 2%. With optimal state of charge (SOC) interval selection, the estimation error can be further reduced to 1%. Clearly, our proposed method exhibits excellent compatibility across diverse battery materials and degradation states. This adaptability holds substantial scientific value and practical importance. It contributes to the safe and economic utilization of Li-ion batteries throughout their entire lifespan. Full article

Lithium-ion batteries play a vital role in human society. Therefore, it is of critical significance to reliably predict the evolution of State of Health (SOH) degradation patterns in order to improve the high accuracy and stability of lithium-ion battery SOH prediction. This paper proposes a novel SOH predication method by combing the four-vector intelligent metaheuristic (FVIM) with the CNN-LSTM-Attention basic model. The model adopts the collaborative architecture of a convolutional neural network and time series module, strengthens the cross-level feature interaction by introducing a multi-level attention mechanism, then uses the FVIM optimization algorithm to optimize the key parameters to realize the overall model architecture. By analyzing the charging voltage curve of lithium-ion batteries, the health factors with high correlation are extracted, and the correlation between the health factors and battery capacity is verified using two correlation coefficients. After the model is verified on a single NASA battery aging dataset, the model is compared with other models under the same relevant parameters and environmental settings to verify the high-precision prediction of the model. During the analysis and comparison process, CNN-LSTM-Attention-FVIM achieved a high fitting ability for battery SOH prediction estimation, with the mean absolute error (MAE) and root mean square error (RMSE) within 0.99% and 1.33%, respectively, reflecting the model’s high generalization ability and high prediction performance. Full article

Current state-of-health (SOH) point prediction methods are highly accurate during early cycles. However, the prediction error increases significantly with increasing numbers of battery charging and discharging cycles, especially in the later stages of degradation. This leads to the intensification of uncertainty regarding SOH, which seriously affects the accuracy and safety of judgments about battery failure. To solve this problem and overcome the limitation of human parameter tuning, this study proposes a method for predicting the SOH interval of lithium batteries based on a stochastic differential equation (SDE) and the chaotic evolutionary optimization (CEO) algorithm to optimize the TSKANMixer network. First, battery charge/discharge curves are analyzed, and health features were extracted to establish a SOH estimation model based on TSKANMixer. Then, the hyperparameters of the TSKANMixer model were optimized using the CEO algorithm to further improve the prediction performance. Finally, the prediction of SOH intervals was implemented using SDE based on the CEO-TSKANMixer model. The results show that the CEO optimization brought the RMSE of SOH prediction for the three cells down to no more than 1%, which was 72.70% lower than that of the baseline model. The PICP of the SDE-based interval prediction model exceeded 90% for all of them, and the NMPIW was no more than 6.47%. This indicates that the model can accurately quantify the SOH uncertainty and effectively support the early warning of the risk of battery failure in the late stages of attenuation. The method can also be used for SOH interval prediction for subsequent battery clusters, reducing the computational complexity of cell-by-cell analysis and improving the overall efficiency of battery management systems. Full article