Search Results (983)

Amidst the background of accelerated global energy transition, the safety risk of lithium-ion battery energy storage systems, especially the fire hazard, has become a key bottleneck hindering their large-scale application, and there is an urgent need to build a systematic prevention and control program. This paper focuses on the fire characteristics and thermal runaway mechanism of lithium-ion battery energy storage power stations, analyzing the current situation of their risk prevention and control technology across the dimensions of monitoring and early warning technology, thermal management technology, and fire protection technology, and comparing and analyzing the characteristics of each technology from multiple angles. Building on this analysis, this paper summarizes the limitations of the existing technologies and puts forward prospective development paths, including the development of multi-parameter coupled monitoring and warning technology, integrated and intelligent thermal management technology, clean and efficient extinguishing agents, and dynamic fire suppression strategies, aiming to provide solid theoretical support and technical guidance for the precise risk prevention and control of lithium-ion battery storage power stations. 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

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

To address the limitations of conventional single-stack fuel cell hybrid systems using equivalent hydrogen consumption strategies, this study proposes a multi-stack energy management strategy incorporating fuel cell health degradation. Leveraging a fuel cell efficiency decay model and lithium-ion battery cycle life assessment, power distribution is reformulated as an equivalent hydrogen consumption optimization problem with stack degradation constraints. A hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) approach achieves global optimization. The experimental results demonstrate that compared with the Frequency Decoupling (FD) method, the GA-PSO strategy reduces hydrogen consumption by 7.03 g and operational costs by 4.78%; compared with the traditional Particle Swarm Optimization (PSO) algorithm, it reduces hydrogen consumption by 3.61 g per operational cycle and decreases operational costs by 2.66%. This strategy ensures stable operation of the marine power system while providing an economically viable solution for hybrid-powered vessels. Full article

As an important component of current power and energy storage systems, lithium-ion batteries have essential scientific significance and application value in terms of accurately and reliably diagnosing their aging to determine system performance, identify potential faults in modules, and prolong their service life. For this purpose, this paper first briefly describes the working principle of lithium-ion batteries and illustrates the possible impacts of various aging mechanisms on the state of battery capacity. Secondly, starting from both implementable and laboratory perspectives, it sorts out and summarizes the diagnostic mechanisms and applicable scenarios of current typical battery aging state assessment and diagnosis methods. Then, targeting the specific aging mechanisms involved in batteries, it elaborates on the targeted diagnosis processes for each aging mechanism. Finally, combined with implementable and laboratory diagnosis methods, it systematically summarizes a highly standardized and universal routine diagnosis process for battery aging. In addition, in combination with the latest development of aging diagnosis and related technologies, this paper reflects on and discusses the possible future development directions of battery diagnosis technologies. Full article

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized the ignition source to the lateral heel edge of the pouch cell, correlating precisely with peak mechanical stress identified through gait analysis. Remarkably, the cyclic load was less than 10% of the single crush load threshold specified in safety standards. Key findings reveal multiple contributing factors as follows: the uncoated polyethylene separator’s inability to prevent stress-induced internal short circuits, the circuit design’s lack of battery health monitoring functionality that permitted undetected degradation, and the hazardous placement inside clothing that exacerbated burn injuries. These findings necessitate a multi-level safety framework for lithium-ion battery products, encompassing enhanced cell design to prevent internal short circuit, improved circuit protection with health monitoring capabilities, optimized product integration to mitigate mechanical and environmental impact, and effective post-failure containment measures. This case study exposes a critical need for product-specific safety standards that address the unique demands of wearable lithium-ion batteries, where existing certification requirements fail to prevent real-use failure scenarios. Full article

High-power lithium-ion batteries impose stringent requirements on output power. Tetramethylene sulfone (TMS), serving as a novel electrolyte additive, effectively enhances the stability of electrolytes under high-voltage conditions due to its high flash point and high dielectric constant, thereby boosting the output performance of lithium-ion batteries. In this work, we selected lithium hexafluorophosphate (LiPF₆) as the lithium salt, using a solvent carrier consisting of a mixture of ethylene carbonate (EC), dimethyl carbonate (DMC), and ethyl methyl carbonate (EMC). TMS was added as an additive to create a novel high-power electrolyte system. We prepared five electrolytes with different TMS concentrations and conducted in-depth investigations into their impacts on the performance of lithium-ion batteries. The findings indicate that the electrolytes with TMS ratios of 2 wt% and 5 wt% demonstrated good synergistic cathode–anode stability in the NCM//soft carbon system, and the electrolyte with a 5 wt% TMS ratio demonstrated the most significant improvement in the overall performance of the full battery. Full article

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

MXenes, a family of two-dimensional carbide and nitride nanomaterials, have demonstrated significant promise across various technological domains, particularly in energy storage applications. This review critically examines scalable synthesis techniques for MXenes and their potential integration into next-generation rechargeable battery systems. We highlight both top-down and emerging bottom-up approaches, exploring their respective efficiencies, environmental impacts, and industrial feasibility. The paper further discusses the electrochemical behavior of MXenes in lithium-ion, sodium-ion, and aluminum-ion batteries, as well as their multifunctional roles in solid-state batteries—including as electrodes, additives, and solid electrolytes. Special emphasis is placed on surface functionalization, interlayer engineering, and ion transport properties. We also compare MXenes with conventional graphite anodes, analyzing their gravimetric and volumetric performance potential. Finally, challenges such as diffusion kinetics, power density limitations, and scalability are addressed, providing a comprehensive outlook on the future of MXenes in sustainable energy storage technologies. Full article

This paper presents the results of the mathematical modeling and experimental studies of charging a traction lithium-ion battery of a passenger electric car using an integrated charger based on a traction voltage inverter. An original three-stage charging algorithm (3PT/PN) has been developed and implemented, which provides a sequential decrease in the charging current when the specified voltage and temperature levels of the battery module are reached. As part of this study, a comprehensive mathematical model has been created that takes into account the features of the power circuit, control algorithms, thermal effects and characteristics of the storage battery. The model has been successfully verified based on the experimental data obtained when charging the battery module in real conditions. The maximum error of voltage modeling has been 0.71%; that of current has not exceeded 1%. The experiments show the achievement of a realized capacity of 8.9 Ah and an integral efficiency of 85.5%, while the temperature regime remains within safe limits. The proposed approach provides a high charge rate, stability of the thermal state of the battery and a long service life. The results can be used to optimize the charging infrastructure of electric vehicles and to develop intelligent battery module management systems. Full article