Search Results (901)

Power flow analysis is essential for managing power systems, helping grid operators ensure reliability and efficiency. This paper explores the use of machine learning (ML) techniques as surrogates for computationally intensive power flow calculations to evaluate the effects of distributed energy resources, such as battery energy storage systems (BESSs), on grid performance. In this paper, a case study is presented where XGBoost (eXtreme Gradient Boosting) and Artificial Neural Networks (ANNs) are trained to simulate power flows in a medium-voltage grid in Norway. The impact of BESS units on line loading, transformer loading, and bus voltages is estimated across thousands of configurations, with results compared in terms of simulation time, error metrics, and robustness. In this paper it is proven that while ML models require considerable data and training time, they offer speed-up factors of up to 45×, depending on the predicted parameter. The proposed methodology can also be used to assess the impact of other grid-connected assets, such as small-scale solar plants and electric vehicle chargers, whose presence in distribution networks continues to grow. Full article

With the exponential growth of Internet of Things (IoT) and wearable devices for home automation and industrial applications, vast volumes of data are continuously generated, requiring efficient data collection methods. IoT devices, being resource-constrained and typically battery-dependent, require lightweight protocols that optimize resource usage and energy consumption. Among such IoT devices, this article focuses on Bluetooth-based beacons due to their low latency and the advantage of not requiring pairing for communications. Specifically, to tackle the limitations of beacons in terms of bandwidth and transmission frequency, this article proposes a protocol that modifies beacon frames to include up to three parameters per frame and that allows for making use of configurable beaconing intervals based on the specific requirements of the communications scenario. Moreover, the use of the proposed protocol leads to increased data rates for beaconing transmissions, providing a low latency and a flexible configuration that permits adjusting different parameters. The proposed solution enables end-to-end interoperability in Opportunistic Edge Computing (OEC) networks by integrating a lightweight bridge module to transparently manage BLE advertisement segments. To demonstrate the performance of the devised opportunistic protocol, it is evaluated across multiple scenarios (i.e., in a short-distance reference scenario, inside a home with diverse obstacles, inside a building, outdoors and in an industrial scenario), showing its flexibility and ability to collect substantial data volumes from heterogeneous IoT devices. Full article

The global rise of electric vehicles (EVs) is reshaping the automotive industry, driven by a 25% increase in EV sales in 2024 and mounting regulatory pressure from European countries aiming to phase out thermal and hybrid vehicle production. In this context, the development of advanced battery technologies has become a critical priority. However, progress in electrochemical storage systems remains limited due to persistent technological barriers such as gaps in data, inadequate modeling tools, and difficulties in system integration, such as thermal management and interface instability. Safety concerns like thermal runaway and the lack of long-term performance data also hinder large-scale adoption. This study presents an in-depth analysis of lithium–ion (Li–ion) batteries, with a particular focus on evaluating their charging and discharging behaviors. To facilitate this, a series of automated experiments was conducted using a custom-built test bench equipped with MATLAB (2024b) programming and dSPACE data acquisition cards, enabling precise current and voltage measurements. The acquired data were analyzed to derive mathematical models that capture the operational characteristics of Li–ion batteries. Furthermore, various state-of-charge (SoC) estimation techniques were investigated to enhance battery efficiency and improve range management in EVs. This paper contributes to the advancement of energy storage technologies and supports global ecological goals by proposing safer and more efficient solutions for the electric mobility sector. Full article

This paper presents a calibrated and dynamically responsive simulation framework for hybrid energy systems that integrate Proton Exchange Membrane Fuel Cells (PEMFCs) and batteries, targeting applications in light commercial vehicles (LCVs). The aim is to support the design and assessment of energy management strategies (EMS) under realistic operating conditions. A publicly available PEMFC model is used as the starting point. To improve its representativeness, calibration is performed using experimental polarization curve data, enhancing the accuracy of the stack voltage model, and the air compressor model—critical for maintaining stable fuel cell operation—is adjusted to reflect measured transient responses, ensuring realistic system behavior under varying load demands. Quantitatively, the calibration results are strong: the R² values of both the fuel cell polarization curve and the overall system efficiency are around 0.99, indicating excellent agreement with experimental data. The calibrated model is embedded within a complete hybrid vehicle powertrain simulation, incorporating longitudinal dynamics and control strategies for power distribution between the battery and fuel cells. Simulations conducted under WLTP driving cycles confirm the model’s ability to replicate key behaviors of PEMFC-battery hybrid systems, particularly with respect to dynamic energy flow and system response. In conclusion, this work provides a reliable and high-fidelity simulation environment based on empirical calibration of key subsystems, which is well suited for the development and evaluation of advanced EMS algorithms. Full article

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, can learn from in its efforts to adopt electric vehicles (EVs). To strengthen the body of knowledge and determine the most effective and efficient route to an “EV-ready” nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3962 articles using VOSviewer software, which identified six noteworthy clusters that warranted further discussion. Additionally, we examined the sequential progression of these clusters as follows: (1) the environmental ramifications of electric mobility; (2) advancements in EV technology, including range extension and soundless engines, as well as the capital expenditure (CAPEX) and operating expenditure (OPEX) of purchasing and operating EVs; (3) concerns regarding the effectiveness and durability of EV batteries; (4) the availability of EV charging stations and grid integration; (5) charging time; and, finally, (6) the origin and source of the energy used in the development of e-mobility. Delineating critical aspects in the development of e-mobility can help to equip policymakers and decision makers in Kuwait in formulating timely and economical choices pertaining to sustainable transportation. This study contributes by cross-walking six global bibliometric clusters to Kuwait’s ten EV adoption barriers and mapping each to actionable policy levers, linking evidence to deployment guidance for an emerging market grid. Unlike prior bibliometric overviews, our analysis is Kuwait-specific and heat-contextual, and it reports each cluster’s size and recency to show where the field is moving. Using Kuwait driving logs, we found that summer (avg 43.2 °C) reduced the effective full-charge range by 24% versus pre-winter (approximately 244 km vs. 321 km), underscoring the need for shaded PV-coupled hyper-hubs and active thermal management. Full article

Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data. Full article

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a pivotal role in ensuring high efficiency and durability of battery cells and packs. The BMS monitors and controls the battery charge and discharge to ensure EV safety and optimum operation. This paper is devoted to analyzing BMS circuitry configurations and algorithms. The analysis includes circuit solutions and algorithms for implementing the main BMS functions, such as parameter monitoring, protection, cell balancing, state estimation, charging and discharging management, communication, and data logging. The paper provides insights into the recent research literature on BMS, and the advantages and disadvantages of methods for implementing BMS functions are compared. The paper also discusses the application of artificial intelligence technologies and aspects of further work on next-generation BMS technologies. Full article

The use of power smoothing for renewable energy resources is attracting increasing attention. One widely used resource that could benefit from this technique is the grid-tied photovoltaic (PV) system. Solar energy production typically follows a Gaussian bell curve, with peaks at midday. This paper confirms this pattern by using the bell curve as a reference; however, climate variations can significantly alter this pattern. Therefore, this study aimed to smooth the power supplied to the grid by a PV system. The proposed controller manages the charge and discharge processes of the energy storage system (ESS) to ensure a smooth Gaussian bell curve output. It adjusts the parameters of this curve to closely match the generated energy, absorbing or supplying fluctuations to maintain the desired profile. This system also aims to provide accurate predictions of the power that should be supplied to the grid by the PV system, based on the capabilities of the ESS and the overall system performance. Although experimental results were not included in this analysis, the system was implemented in SIMULINK using real-world data. The controller utilizes a hybrid ESS comprising a vanadium redox battery (VRB) and supercapacitors (SCs). The design and operation of the controller, including curve tuning and ESS charge–discharge management, are detailed. The simulation results demonstrate excellent performance and are thoroughly discussed. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting, version 2.1.4) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

The estimation of the state of health (SOH) of lithium-ion batteries is a critical technology for enhancing battery lifespan and safety. When estimating SOH, it is essential to select representative features, commonly referred to as health indicators (HIs). Most existing studies primarily focus on HIs related to capacity degradation and internal resistance increase. However, due to the complexity of lithium-ion battery degradation mechanisms, the relationships between these mechanisms and health indicators remain insufficiently explored. This paper provides a comprehensive review of core methodologies for SOH estimation, with a particular emphasis on the classification and extraction of health indicators, direct measurement techniques, model-based and data-driven SOH estimation approaches, and emerging trends in battery management system applications. The findings indicate that capacity, internal resistance, and temperature-related indicators significantly impact SOH estimation accuracy, while machine learning models demonstrate advantages in multi-source data fusion. Future research should further explore composite health indicators and aging mechanisms of novel battery materials, and improve the interpretability of predictive models. This study offers theoretical support for the intelligent management and lifespan optimization of lithium-ion batteries. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article