Search Results (317)

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

This study investigates a novel heat pipe integrated cooling system designed for thermal management of Tesla’s 4680 cylindrical lithium–ion batteries in electric vehicles (EVs). Through a comprehensive approach combining experimental analysis, 1-D AMESim simulations, and 3-D Computational Fluid Dynamics (CFD) modeling, the thermal performance of various wick structures and working fluid filling ratios was evaluated. The experimental setup utilized a triangular prism chamber housing three surrogate heater blocks to replicate the heat generation of 4680 cells under 1C, 2C, and 3C discharge rates. Results demonstrated that a blended fabric wick with a crown-shaped design (Wick 5) at a 30–40% filling ratio achieved the lowest maximum temperature (T_max of 47.0 °C), minimal surface temperature deviation (ΔT_surface of 2.8 °C), and optimal thermal resistance (R_th of 0.27 °C/W) under 85 W heat input. CFD simulations validated experimental findings, confirming stable evaporation–condensation circulation at a 40% filling ratio, while identifying thermal limits at high heat loads (155 W). The proposed hybrid battery thermal management system (BTMS) offers significant potential for enhancing the performance and safety of high-energy density EV batteries. This research provides a foundation for optimizing thermal management in next-generation electric vehicles. Full article

In this work, graphite anodes and lithium iron phosphate (LFP) cathodes are used to examine the effects of sodium hexafluorophosphate (NaPF₆) and potassium hexafluorophosphate (KPF₆) electrolyte additives on the formation of the solid electrolyte interphase and the performance of lithium-ion batteries in both half-cell and full-cell designs. The objective is to assess whether these additives may increase cycle performance, decrease irreversible capacity loss, and improve interfacial stability. Compared to the control electrolyte (1.22 M Lithium hexafluorophosphate (LiPF₆)), cells with NaPF₆ and KPF₆ additives produced less SEI products, which decreased irreversible capacity loss and enhanced initial coulombic efficiency. Following the formation of the solid electrolyte interphase, the specific capacity of the control cell was 607 mA·h/g, with 177 mA·h/g irreversible capacity loss. In contrast, irreversible capacity loss was reduced by 38.98% and 37.85% in cells containing KPF₆ and NaPF₆ additives, respectively. In full cell cycling, a considerable improvement in capacity retention was achieved by adding NaPF₆ and KPF₆. The electrolyte, including NaPF₆, maintained 67.39% greater capacity than the LiPF₆ baseline after 20 cycles, whereas the electrolyte with KPF₆ demonstrated a 30.43% improvement, indicating the positive impacts of these additions. X-ray photoelectron spectroscopy verified that sodium (Na⁺) and potassium (K⁺) ions were present in the SEI of samples containing NaPF₆ and KPF₆. While K⁺ did not intercalate in LFP, cyclic voltammetry confirmed that Na⁺ intercalated into LFP with negligible impact on the energy storage of full cells. These findings demonstrate that NaPF₆ and KPF₆ are suitable additions for enhancing lithium-ion battery performance in the popular artificial graphite/LFP system. 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

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

Solid-state electrolytes (SSEs) have emerged as a promising solution for next-generation lithium-ion batteries due to their excellent safety and high energy density. However, their practical application is still hindered by critical challenges such as their low ionic conductivity and high interfacial resistance at room temperature. The innovative application of 3D printing in the field of electrochemistry, particularly in solid-state electrolytes, endows energy storage devices with attractive characteristics. In this study, ceramic-in-gel polymer electrolytes (GPEs) based on PVDF-HFP/PAN@LLZTO were fabricated using a direct ink writing (DIW) 3D printing technique. Under the optimal printing conditions (printing speed of 40 mm/s and fill density of 70%), the printed electrolyte exhibited a uniform and dense sponge-like porous structure, achieving a high ionic conductivity of 5.77 × 10⁻⁴ S·cm⁻¹, which effectively facilitated lithium-ion transport. A structural analysis indicated that the LLZTO fillers were uniformly dispersed within the polymer matrix, significantly enhancing the electrochemical stability of the electrolyte. When applied in a LiFePO₄|GPEs|Li cell configuration, the electrolyte delivered excellent electrochemical performance, with high initial discharge capacities of 168 mAh·g⁻¹ at 0.1 C and 166 mAh·g⁻¹ at 0.2 C, and retained 92.8% of its capacity after 100 cycles at 0.2 C. This work demonstrates the great potential of 3D printing technology in fabricating high-performance GPEs. It provides a novel strategy for the structural design and industrial scalability of lithium-ion batteries. Full article

This study presents the design and techno-economic comparison of two standalone photovoltaic (PV) systems, each supplying a 1 kW critical load with 100% reliability under Cairo’s climatic conditions. These systems are modeled for both the constant and the night load scenarios, accounting for the worst-case weather conditions involving 3.5 consecutive cloudy days. The primary comparison focuses on traditional lead-acid battery storage versus green hydrogen storage via electrolysis, compression, and fuel cell reconversion. Both the configurations are simulated using a Python-based tool that calculates hourly energy balance, component sizing, and economic performance over a 21-year project lifetime. The results show that the PV/H₂ system significantly outperforms the PV/lead-acid battery system in both the cost and the reliability. For the constant load, the Levelized Cost of Electricity (LCOE) drops from 0.52 USD/kWh to 0.23 USD/kWh (a 56% reduction), and the payback period is shortened from 16 to 7 years. For the night load, the LCOE improves from 0.67 to 0.36 USD/kWh (a 46% reduction). A supplementary cost analysis using lithium-ion batteries was also conducted. While Li-ion improves the economics compared to lead-acid (LCOE of 0.41 USD/kWh for the constant load and 0.49 USD/kWh for the night load), this represents a 21% and a 27% reduction, respectively. However, the green hydrogen system remains the most cost-effective and scalable storage solution for achieving 100% reliability in critical off-grid applications. These findings highlight the potential of green hydrogen as a sustainable and economically viable energy storage pathway, capable of reducing energy costs while ensuring long-term resilience. Full article

Batteries have become the main power source in today’s automotive systems. This paper proposes the design of a fast-balancing passive battery management system (BMS) with remote monitoring for the automotive domain. This system is designed for four series-connected lithium iron phosphate (LiFePO4) cells, which are the preferred choice in the automotive industry. The results show that the proposed BMS can monitor the cell voltages with an error lower than 0.12%, and it can perform the balancing operation successfully with maximum currents of 750 mA during both charging and discharging cycles, not only for LiFePO4 cells, but also for lithium-ion (Li-ion) cells. Furthermore, the cell voltages are sent over the controller area network (CAN) interface for remote monitoring. Full article

Due to their high energy density and power potential, 21700 lithium-ion battery cells are a widely used technology in hybrid and electric vehicles. Efficient thermal management is essential for maximizing the performance and capacity of Li-ion cells in both low- and high-temperature operating conditions. Optimizing thermal management systems remains critical, particularly for long-range and weight-sensitive applications. In these contexts, passive heat dissipation emerges as an ideal solution, offering effective thermal regulation with minimal additional system weight. This study aims to deepen the understanding of passive heat dissipation in 21700 battery cells and optimize their performance. Special emphasis is placed on analyzing heat transfer and the relative contributions of convective and radiative mechanisms under varying temperature and discharge conditions. Laboratory experiments were conducted under controlled environmental conditions at various discharge rates, ranging from 0.5×C to 5×C. A 3D-printed polymer casing was applied to the cell to enhance thermal dissipation, designed specifically to increase radiative heat transfer while minimizing system weight and reliance on active cooling solutions. Additionally, a numerical model was developed and optimized using experimental data. This model simulates convective and radiative heat transfer mechanisms with minimal computational demand. The optimized numerical model is intended to facilitate further investigation of the cell envelope strategy at the module and battery pack levels in future studies. Full article

The battery pack is a critical component of electric vehicles, with lithium-ion cells being a frequently preferred choice. Lithium-ion cells are known for long life, high power and energy density, and are reliable for a broad range of temperatures. However, these batteries have a drawback of over-voltage, under-voltage, thermal runaway, and especially, state of charge or voltage imbalance. Among these, the cell imbalance is particularly important because it causes an uneven power dissipation in each cell, resulting in non-uniform temperature distribution. This uneven temperature distribution negatively affects the lifetime and efficiency of a battery pack. Cell imbalance is mitigated by cell balancing techniques, of which several methods have been presented over the last few years. These methods consider different power electronics circuits and control approaches to optimise cell balancing characteristics. This paper reviews basic to advanced cell balancing techniques and compares their circuit designs, costs, switching stresses, complexity, sizes, and control techniques to highlight the recent trends and future directions. This paper also compares the recent trend of machine learning integration with basic cell balancing topologies and provides a critical analysis of the outcomes. Full article

Polymer electrolytes (PEs) provide enhanced safety for high–energy–density lithium metal batteries (LMBs), yet their practical application is hampered by intrinsically low ionic conductivity and insufficient electrochemical stability, primarily stemming from suboptimal Li⁺ solvation environments and transport pathways coupled with slow polymer dynamics. Herein, we demonstrate a molecular design strategy to overcome these limitations by regulating the Li⁺ solvation structure through the synergistic interplay of conventional Lewis acid–base coordination and engineered hydrogen bond (H–bond) networks, achieved by incorporating specific H–bond donor functionalities (N,N′–methylenebis(acrylamide), MBA) into the polymer architecture. Computational modeling confirms that the introduced H–bonds effectively modulate the Li⁺ coordination environment, promote salt dissociation, and create favorable pathways for faster ion transport decoupled from polymer chain motion. Experimentally, the resultant polymer electrolyte (MFE, based on MBA) enables exceptionally stable Li metal cycling in symmetric cells (>4000 h at 0.1 mA cm⁻²), endows LFP|MFE|Li cells with long–term stability, achieving 81.0% capacity retention after 1400 cycles, and confers NCM622|MFE|Li cells with cycling endurance, maintaining 81.0% capacity retention after 800 cycles under a high voltage of 4.3 V at room temperature. This study underscores a potent molecular engineering strategy, leveraging synergistic hydrogen bonding and Lewis acid–base interactions to rationally tailor the Li⁺ solvation structure and unlock efficient ion transport in polymer electrolytes, paving a promising path towards high–performance solid–state lithium metal batteries. Full article

In response to the global imperative to reduce greenhouse gas emissions and fossil fuel dependency, electric vehicles (EVs) have emerged as a sustainable transportation alternative, primarily utilizing lithium-ion batteries (LIBs) due to their high energy density and efficiency. However, LIBs are highly sensitive to temperature fluctuations, significantly affecting their performance, lifespan, and safety. One of the most critical threats to the safe operation of LIBs is thermal runaway (TR), an uncontrollable exothermic process that can lead to catastrophic failure under abusive conditions. Moreover, thermal runaway propagation (TRP) can rapidly spread failures across battery cells, intensifying safety threats. To address these challenges, developing advanced battery thermal management systems (BTMS) is essential to ensure optimal temperature control and suppress TR and TRP within LIB modules. This review systematically evaluates advanced cooling strategies, including indirect liquid cooling, water mist cooling, immersion cooling, phase change material (PCM) cooling, and hybrid cooling based on the latest studies published between 2020 and 2025. The review highlights their mechanisms, effectiveness, and practical considerations for preventing TR initiation and suppressing TRP in battery modules. Finally, key findings and future directions for designing next-generation BTMS are proposed, contributing valuable insights for enhancing the safety and reliability of LIB applications. Full article

The decarbonization of offshore infrastructures is relevant to advancing global climate goals. This study presents a renewable-based energy system tailored for the Oceanic Platform of the Canary Islands (PLOCAN), designed to achieve full energy autonomy and eliminate greenhouse gas emissions. A hybrid configuration integrating photovoltaic panels, vertical-axis wind turbines, lithium-ion batteries, a proton exchange membrane (PEM) electrolyzer, and a PEM fuel cell was developed and evaluated through detailed resource assessment, system simulation, and techno-economic analysis under real offshore constraints. The results confirm that complete decarbonization is technically feasible, with a net present cost approximately 15% lower than the current diesel-based system and a total suppression of pollutant emissions. Although the transition entails a higher initial investment, the long-term economic and environmental gains are substantial. Offshore green hydrogen emerges as a key vector for achieving energy resilience and sustainability in isolated marine infrastructures, offering a replicable pathway towards fully decarbonized ocean platforms. Full article

Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts. Full article

The formation of unstable solid electrolyte interphases (SEIs) on the surface of lithium metal anodes poses a significant barrier to the commercialization of lithium metal batteries (LMBs). Rational modulation of solvation structures within the electrolytes emerged as one of the most effective strategies to enhance interfacial stability in LMBs; however, this approach often compromises the structural stability of the bulk electrolyte. Herein, we present an innovative method that improves interface stability without adversely affecting the bulk electrolyte’s structural stability. By employing ZSM molecular sieves as efficient ion channels on the lithium metal anode surface—termed ZSM electrolytes—a more aggregated solvation structure is induced at the lithium metal interface, resulting in an anion-rich interphase. This anion-enriched environment promotes the formation of an SEI derived from anions, thereby enhancing the stability of the lithium metal interface. Consequently, Li||Cu cells utilizing the ZSM electrolyte achieve an average coulombic efficiency (CE) of 98.76% over 700 h. Moreover, LiFePO₄||Li batteries exhibit stable cycling performance exceeding 900 cycles at a current density of 1 C. This design strategy offers robust support for effective interfacial regulation in lithium metal batteries. Full article