Search Results (260)

The growing need for efficient battery reuse and recycling requires rapid, reliable methods to assess the state of health (SoH) of lithium-ion cells. Conventional SoH estimation based on full charge–discharge cycling is slow, energy-intensive, and unsuitable for dismantled cells with unknown histories. This work presents an automated diagnostic approach using Electrochemical Impedance Spectroscopy (EIS) combined with Electrical Equivalent Circuit (EEC) modeling for fast, non-invasive SoH estimation. A correlation between fitted EIS parameters and cell degradation stages was established through controlled aging tests on NMC-based lithium-ion cells. The methodology was implemented in custom software (BaterurgIA) integrated into a robotic testing bench, enabling automatic EIS acquisition, data fitting, and SoH determination. The system achieves SoH estimation with 5–10% accuracy for cells in intermediate and advanced degradation stages, while additional parameters improve sensitivity during early aging. Compared to conventional cycling methods, the proposed approach reduces diagnostic time from hours to minutes, minimizes energy consumption, and offers predictive insights into internal degradation mechanisms. This enables fast and reliable cell grading for reuse, reconditioning, or recycling, supporting the development of scalable solutions for battery second-life applications and circular economy initiatives. Full article

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged. Full article

Battery-powered automatic guided vehicles (B-AGVs) serve as crucial horizontal transportation equipment in terminals and significantly impact the terminal transportation efficiency. Imbalanced B-AGV availability during terminal peak and off-peak periods is driven by dynamic vessel arrivals. We propose a flexible dual-threshold charging (FDTC) strategy synchronized with vessel dynamics. Unlike the static threshold charging (STC) strategy, FDTC dynamically adjusts its charging thresholds based on terminal workload intensity. And we develop a collaborative B-AGV scheduling and routing optimization model incorporating FDTC. A tailored Dijkstra-Partition neighborhood search (Dijkstra-Pns) algorithm is designed to resolve the problem in alignment with practical scenarios. Compared to the STC strategy, FDTC strategy significantly reduces the maximum B-AGV running time and decreases conflict waiting delays and charging times by 25.04% and 24.41%, respectively. Moreover, FDTC slashes quay crane (QC) waiting time by 40.78%, substantially boosting overall terminal operational efficiency. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries confronts two critical challenges: the extreme scarcity of labeled data in large-scale operational datasets and the mismatch between existing methods (relying on full charging–discharging conditions) and shallow charging–discharging conditions prevalent in real-world scenarios. To address these challenges, this study proposes a self-supervised learning framework for SOH estimation. The framework employs a dual-time-scale collaborative pre-training approach via masked voltage sequence reconstruction and interval capacity prediction tasks, enabling automatic extraction of cross-time-scale aging features from unlabeled data. Innovatively, it integrates domain knowledge into the attention mechanism and incorporates time-varying factors into positional encoding, significantly enhancing the capability to extract battery aging features. The proposed method is validated on two datasets. For the standard dataset, using only 10% labeled data, it achieves an average RMSE of 0.491% for NCA battery estimation and 0.804% for transfer estimation between NCA and NCM. For the shallow-cycle dataset, it achieves an average RMSE of 1.300% with only 2% labeled data. By synergistically leveraging massive unlabeled data and extremely sparse labeled samples (2–10% labeling rate), this framework reduces the labeling burden for battery health monitoring by 90–98%, offering an industrial-grade solution with near-zero labeling dependency. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

Non-active current in the time domain is considered for application to the diagnostics and classification of loads in power grids based on waveform-distortion characteristics, taking as a working example several recordings of the pantograph current in an AC railway system. Data are processed with a deep autoencoder for feature extraction and then clustered via k-means to allow identification of patterns in the latent space. Clustering enables the evaluation of the relationship between the physical meaning and operation of the system and the distortion phenomena emerging in the waveforms during operation. Euclidean distance (ED) is used to measure the diversity and pertinence of observations within pattern groups and to identify anomalies (abnormal distortion, transients, …). This approach allows the classification of new data by assigning data to clusters based on proximity to centroids. This unsupervised method exploiting non-active current is novel and has proven useful for providing data with labels for later supervised learning performed with the 1D-CNN, which achieved a balanced accuracy of 96.46% under normal conditions. ED and 1D-CNN methods were tested on an additional unlabeled dataset and achieved 89.56% agreement in identifying normal states. Additionally, Grad-CAM, when applied to the 1D-CNN, quantitatively identifies the waveform parts that influence the model predictions, significantly enhancing the interpretability of the classification results. This is particularly useful for obtaining a better understanding of load operation, including anomalies that affect grid stability and energy efficiency. Finally, the method has been also successfully further validated for general applicability with data from a different scenario (charging of electric vehicles). The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency. Full article

The design and investigation of electrochromic devices have advanced significantly, including distinct applications such as self-charged smart windows, aerospace interactive windows, low power flexible and ecofriendly displays, automatic dimming rearview, wearable smart textiles, military and civilian camouflage systems, electrochromic sensors, among others. Although significant progress has been made in related fields, achieving the full potential of electrochromic devices to meet the standards of maturity and practical applications remains a persistent challenge. Electrochromic devices are typically multilayered structures that can be designed as either rigid or flexible systems, depending on the type of substrate employed. Conventional electrochromic devices comprise layered structures that include transparent electrodes, electrochromic materials, ionic conductors, and ion storage materials. On the other hand, multifunctional systems integrate bifunctional materials or distinct functional layers to simultaneously achieve optical modulation and additional capabilities such as energy storage. The development of advanced materials, comprehensive electrochemical kinetic analysis, the optimization and advancement of process techniques and deposition methods, and innovative device designs are active areas of extensive global research. This review focuses on the recent advances in multifunctional electrochromic materials and devices with particular emphasis on the integration of electrochromic technology with other functional technologies. It further identifies current challenges, proposes potential solutions, and outlines future research directions focused on advancing this technology in both niche and scalable applications. Full article

Point defects in two-dimensional materials such as MoS₂ can critically impact their electronic and optoelectronic properties. Precise identification of these defects is essential for understanding defect physics and device performance. In this work, we acquire high-resolution scanning tunneling microscopy (STM) images of monolayer MoS₂ and apply the Segment Anything Model (SAM) to automatically segment possible defect regions in the STM images. Each segmented region is then classified by a convolutional neural network (CNN) architecture into defect categories. This deep learning pipeline is trained on augmented STM image data and evaluated against manual annotations. The model achieves a classification accuracy of 95.06% on a modest dataset comprising merely 198 samples, demonstrating its robustness despite limited data availability. We also perform density functional theory (DFT) calculations of representative defect structures to support interpretation of the STM features. Charge density isosurfaces of the DFT models reveal localized mid-gap states associated with sulfur vacancies, consistent with STM observations. The integration of SAM segmentation, CNN classification, and DFT modeling provides a comprehensive approach to quantify defect populations in MoS₂. These results show the potential of combining data-driven image analysis with physics-based modeling to accelerate defect characterization in 2D materials. Full article

This work describes the development of an embedded standalone measurement system that monitors the aging of batteries using impedance spectroscopy. The system generates a multisine stimulus that contains the frequency components at which the battery impedance is measured. Coherent generation and sampling is assured, and Goertzel filters, one for each measurement frequency, are updated with each new sample. This architecture reduces memory requirements because the current and voltage of the measured samples are discarded after processing. Aging is monitored, as the system is able to automatically perform complete or partial charge/discharge cycles as well as measurement cycles without requiring user interaction. Full article

With the spread of electric vehicles (EVs), the EV load will have a significant impact on the planning and operation of the grid and the operation of the electricity market. Due to the charging and discharging characteristics of EVs, as well as their randomness and dispersion, it is feasible and challenging to introduce EV loads into the grid as a means of frequency regulation and peak shaving of the power system. In this paper, considering multi-subject distribution networks and the interaction of source–grid–load–storage with Intelligent Transportation Systems (ITS), a density peak clustering (DPC) algorithm based on principal component analysis is employed to analyze the spatial and temporal characteristics of EV loads and identify the access status of EV charging stations and EV load status in each region in real time, as well as analyze the adjustable capacity and adjustable range of EV loads. Based on the adjustable capacity of the EV load, the optimization objectives include the maximum regulation of the EV load and the most economical operation cost. An accurate load regulation strategy based on automatic active control (APC) is proposed to reduce the maximum frequency deviation by 25% by integrating the load regulation of electric vehicles into the original AGC frequency regulation. At the same time, the feasibility of electric vehicles in peaking and standby scenarios is studied and verified through simulation cases, which can reduce the peak value of thermal power generation by 15% and 10% in the morning and evening. Full article

Efficient and safe charging of lithium-ion batteries is essential for maximizing their lifespan and performance. This paper presents the design and implementation of a microcontroller-based Li-ion battery charger that employs real-time monitoring, adaptive charging strategies, and built-in safety mechanisms. The system integrates a CC/CV charging approach with automatic current regulation, overcharge protection, and reverse polarity detection. A current sensor module ensures continuous monitoring, while an LCD interface provides real-time feedback on charging parameters. Experimental validation was conducted using multiple Li-ion cells in various conditions, like new, aged, and deeply discharged, to evaluate charging behavior and safety under different scenarios. The system successfully regulated current and voltage, managed preconditioning for low-voltage cells, and transitioned smoothly between charging phases. A key contribution of this work is the development of a low-cost, microcontroller-based platform that enables flexible implementation and testing of diverse charging strategies. Its open-source architecture and modular design make it highly suitable for research, educational use, and experimental development in battery management systems. Future enhancements may include the integration of adaptive algorithms based on internal resistance and temperature, enabling smarter and more efficient charging. Full article

Rechargeable batteries are a key component of sustainable future systems, as their performance directly affects energy efficiency, maintenance costs, and system reliability. Assessing performance requires evaluating parameters such as the state of health (SoH) of the battery, which necessitates developing a system capable of efficiently gathering large amounts of data. This article presents a safe, simple, versatile, and automated system designed to test and characterize various types of battery cells. The system is conceived as a practical tool capable of automatically collecting the required data for analysis, thus enabling the determination of the performance parameters of a battery cell. The proposed system incorporates an innovative approach based on the concatenation of charge/discharge data, allowing for a more reliable evaluation of battery performance. Experimental tests show the interest and performance behavior of the proposed system. Full article

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the end-to-end. AI Detector Optimization framework (AIDO), which leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for a layer arrangement and material composition that align with known calorimeter principles. The success of this proof-of-concept study provides a foundation for the future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments. Full article

This paper presents the design and implementation of a linear, stable, low-power and PVT insensitive floating active resistor, which is realized using TSMC 40 nm CMOS process technology. By incorporating the automatic tuning circuit, this work has achieved improved performance metrics, which include low process sensitivity, reduced temperature coefficient, and good linearity. Monte Carlo (MC) simulations are conducted to evaluate the active resistor’s performance under variations in temperature, process, and supply voltage. The proposed design has demonstrated an average resistance process sensitivity of 0.64%, a temperature coefficient (T.C.) of 57 ppm/°C across −25 °C to 85 °C, and a linearity figure of merit (FOM) of 2.4 × 10⁻² V⁻¹ with a resistance close to MΩ level. It can achieve a linear resistance tuning range of 430.5 kΩ to 1.714 MΩ. The typical power consumption of a single active resistor is 0.25 µW at 2.1 V bootstrapped supply voltage through a Dickson charge pump (DCP) circuit using a DC input of 1 V. These results have confirmed that the proposed active resistor can function as a robust and efficient resistor for low-voltage integrated circuits and systems. Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article