Search Results (145)

Battery health data from real-world applications are vital for optimizing and predicting battery lifetime. This study presents the design and verification of an active battery-diagnostic system and method to collect such data. The system measures battery pack capacity and resistance by applying a diagnostic protocol via a bi-directional charger. This was demonstrated on a stationary-energy-storage application, under real-world conditions, to verify the system’s design requirements. Measurements at the start and the end of the demonstration period of a month resulted in an observed degradation of 1.79 ± 0.34% battery capacity and an increase of 1.42 ± 0.75% in battery resistance. The successful measurements of capacity and resistance prove the compatibility of the system with real-world battery systems and confirm the design requirements were met. The system was able to perform autonomous and in situ measurements while only requiring the addition of software to the battery management system and by using the bi-directional charger of the energy storage system. By repeatedly applying the same diagnostic protocol over time, this system enables consistent tracking of battery health. Full article

Due to the high energy density and long cycle life, lithium-ion batteries play an important role in electric transportation and energy storage systems. Therefore, accurate state of health (SOH) estimation is of great significance for battery lifetime management and safe operation. Existing SOH estimation methods are highly dependent on data and suffer from insufficient physical interpretability and limited cross-scenario generalization. To address the issues, introducing physical information constraints enables physical consistency requirements to be incorporated into the estimation process and confines the estimation to the physically feasible domain, thereby improving prediction performance and enhancing physical interpretability. From the perspective of sources of physical constraints and forms of constraint implementation, the review systematically summarizes the current research. Regarding the sources of constraints, equivalent circuit constraints, mechanism-based constraints, and degradation-dynamics constraints are introduced, and the advantages, disadvantages, and applicable scenarios of typical implementation forms, including physics-informed loss, physics-informed initialization, physics-driven architecture design, and virtual physics-driven fusion, are summarized. Finally, current challenges and future research directions are outlined based on a comprehensive comparison of existing studies, with the aim of providing a useful reference for future research on physics-informed SOH estimation. Full article

Background/Objectives: Longer lifetime exposure to endogenous estradiol (L_EE2) has been associated with lower risk of age-related cognitive decline and dementia. Complementary to cognitive decline, behavioral and functional decline are also predictive of dementia risk; however, the association between L_EE2 and these domains is underexplored. We investigated whether L_EE2 is correlated with later-life changes in behavior and function. Methods: Baseline data from 1156 females enrolled in the CAN-PROTECT study were analyzed. L_EE2 was estimated based on the length of the reproductive period (menopause age–menarche age) plus years pregnant and scaled in 5-year increments. Objective cognition was measured using the CAN-PROTECT neuropsychological battery, while subjective cognition, behavior, and function were measured using the Revised Everyday Cognition (ECog-II) scale, Mild Behavioral Impairment Checklist (MBI-C), and Standard Assessment of Global Everyday Activities (SAGEA) scale, respectively. Linear regressions modeled the association between L_EE2 and neuropsychological performance. Three separate negative binomial regression models examined the association between L_EE2 and ECog-II, MBI-C, and SAGEA total scores. All models adjusted for menopause hormone therapy, menopause type, age at first childbirth, body mass index, age, education, and ethnocultural background. Results: Each five-year increase in L_EE2 was associated with a lower MBI-C score (count ratio [CR] = 0.89, 95% CI [0.82, 0.97]) and lower SAGEA score (CR = 0.91, 95% CI [0.84, 0.98]). L_EE2 was not significantly associated with any objective or subjective cognitive measures. Conclusions: Longer L_EE2 may associate with lower severity of later-life behavioral and functional symptoms in older women. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Lithium-ion batteries (LIBs) are pivotal for energy storage in electric vehicles and renewable systems, but how to effectively monitor their conditions and ensure their operational reliability is still a concern today. This study employs electrochemical impedance spectroscopy (EIS) to systematically investigate the evolution of impedance characteristics in nickel–cobalt–manganese oxide (NCM) lithium-ion batteries (LIBs) under varying states of charge (SOCs), states of health (SOHs), temperatures, and mechanical compression displacements. Results reveal that higher SOC and temperature reduce impedance by enhancing ion kinetics and interfacial activity, with $R_{ct}$ (charge transfer resistance) exhibiting a U-shaped dependence on SOC, minimized at 40–60%. As SOH declines from 100% to 80%, $R_{SEI}$ (SEI film resistance) and $R_{ct}$ increase progressively, reflecting SEI thickening and electrode degradation. Mechanical compression (0–8 mm) elevates all resistances, particularly $R_{ct}$ at high SOC, due to structural deformation and hindered diffusion. DRT (distribution of relaxation times) spectra highlight amplified low-frequency peaks with aging and low SOC, underscoring diffusion limitations. These findings elucidate multi-scale failure mechanisms, from interfacial polarization to structural instability, providing a framework for non-invasive health monitoring and lifetime prediction. Full article

Parallel electric–hydraulic hybrid (PEHH) powertrains offer benefits of lower energy consumption and increased battery lifetime compared to pure electric ones. These merits can be extended with advanced control methods that optimally deploy on-board energy sources. This paper proposes a nonlinear model predictive control (NMPC) energy management strategy (EMS) for a PEHH wheel loader. The optimization minimizes energy usage and battery degradation by selecting the optimal power ratio between the electric and hydraulic subsystems. The state prediction is based on a discrete nonlinear dynamic model and an estimate of the future exogenous inputs developed from a high-fidelity digital-twin model of a wheel loader. The NMPC formulation is compared to a baseline rule-based EMS inspired by offline optimal control. The proposed NMPC results in 31.7% less battery degradation and 9.14% energy consumption reduction even with a 20% error in the preview information. Hardware-in-the-loop (HiL) experiments validate our results and show that the NMPC EMS can be implemented in real time even with higher prediction error increasing the maximum computational time. Full article

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), with different ageing trends. The prognosis algorithms are tuned with three different percentages of the data, allowing for the evaluation of the methods at different ageing stages. The comparison and analysis of the results show that ESN outperforms other methods; it has the lowest prediction error (mean absolute percentage error less than 1.4% at initial ageing of the cells) and the shortest training time, making it the most appropriate method for automotive applications. Full article

Increasing electric vehicle (EV) adoption raises concerns about EV waste management and the impact on the environment. To improve energy efficiency and exploit their remaining usable capacity, the retired batteries may be repurposed in second-life applications. This paper predicts the usable second-life capacity of retired EV batteries, considering the European Union (EU) regulation regarding the mandatory recycled critical material quotas in newly manufactured batteries from 2031 onwards. Based on political influences and the market’s capacity to return to pre-pandemic values, four scenarios are proposed regarding future EV sales in the EU market. The algorithm implemented in Matlab R2025a indicates the batteries that must be recycled to meet the mandatory targets and the ones that can be repurposed as battery energy storage systems. Historical data and future predictions are used to determine the number of EV batteries sold, lifetime, the market’s chemistry share and the usable capacity for second life. The annual mandatory recycled critical material content is compared to the available recyclable mass from both retired batteries in the current year and those that are already active in their second life. The economic analysis reveals the scenario with the highest total revenue, including the cascade benefits and recycling value. Full article

In recent years, renewable energy production has expanded rapidly, becoming an essential component of the global energy transition. However, the inherent variability and unpredictability of renewable generation require technologies that can provide grid stability and operational flexibility. Battery Energy Storage Systems (BESS) play a central role in addressing this challenge, but their long-term effectiveness depends on a thorough understanding of their degradation mechanisms. This work aims to model and predict the capacity and power degradation of a real-world BESS operating in the electricity market, bridging the gap between laboratory-based ageing studies and field applications. Several degradation indicators, such as available capacity evolution, DC efficiency evolution, conductivity loss, and loss of lithium inventory, were evaluated to determine which models best describe the system’s ageing behaviour. Some estimations were found inaccurate and subsequently excluded, while the remaining analyses enabled a detailed characterisation of BESS performance over time. Using operational data collected between November 2022 and October 2024, results indicate a linear capacity degradation reaching 4.57% over 23 months (490 equivalent cycles), from approximately 9600 kWh to 9150 kWh, with a Mean Absolute Percentage Error (MAPE) of 0.2%. DC efficiency exhibited a two-phase evolution, with an initial rise followed by a slow reduction trend. These findings confirm that ageing effects can be effectively evaluated using operational data, enabling reliable lifetime forecasting for BESS assets. Full article

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

This paper proposes a unified state-of-health (SoH) predictive energy management and adaptive grid-forming (GFM) control framework for battery energy storage systems, addressing the conflict between battery lifetime degradation and dynamic stability under grid-support operation. A composite degradation model is incorporated into a multi-timescale EMS to anticipate aging trends, while real-time virtual inertia, damping, and impedance are adjusted according to instantaneous SoH. Simulation results demonstrate that, compared with conventional non-SoH-aware control, the proposed method reduces transient overshoot by up to 32%, shortens settling time by 25–40%, and lowers peak battery current stress by 12–23% under aged (60% SoH) conditions. Moreover, the unified framework maintains consistent damping performance across different aging stages, whereas traditional approaches exhibit significant degradation. These quantitative improvements confirm that jointly embedding SoH prediction into both dispatch scheduling and GFM control can effectively enhance transient performance while extending battery lifetime. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

The rapid expansion of Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) protocol technologies in large-scale Internet of Things (IoT) deployments highlights the need for precise and analytically grounded energy consumption (EC) estimation of battery-powered LoRa end devices (DVs). Since LoRa DV instantaneous EC strongly depends on key transmission parameters, primarily including spreading factor (SF), transmit (Tx) power, and LoRa message packet size (PS), accurate modelling of their combined influence is essential for optimizing LoRa end DV lifetime, ensuring energy-efficient network operation, and supporting transmission parameter-adaptive communication strategies. Motivated by these needs, this paper presents a comprehensive multiple linear regression modelling framework for quantifying LoRa end DV EC during one transmission and reception LoRa end DV Class A communication cycle. The study is based on extensive high-resolution electric-current measurements collected over 69 measurement sets spanning different combinations of SFs, Tx power levels, and PS values. Based on measurement results, a total of 14 multiple linear regression models are developed, each capturing the joint impact of two transmission parameters while holding the third fixed. The developed regression models are mathematically formulated using linear, interaction, and polynomial terms to accurately express nonlinear EC behavior. Detailed statistical accuracy assessments demonstrate excellent goodness of fit of the developed EC multiple linear regression models. Complementary numerical analyses of regression models EC data distribution further validate regression models’ reliability, and highlight transmission parameter-driven variability of Lora end DV EC. The results of numerical analyses for LoRa end DV EC data distribution show that specific combinations of SF, Tx power, and PS transmit parameters amplify or mitigate EC differences, demonstrating that their joint variability patterns can significantly alter instantaneous energy demand across operating conditions. These interactions underscore the importance of modelling parameters together, rather than in isolation. The developed regression models provide interpretable mathematical formulations of instantaneous LoRa end DV EC prediction for transmission at different combinations of transmission parameters, and offer practical value for energy-aware configuration, battery-lifetime planning, and optimization of LoRa network-based IoT systems. Full article