Search Results (82)

Operando mechanical behavior of lithium-ion batteries under aggressive conditions remains insufficiently quantified, especially under combined high-rate and deep-discharge operation. This study investigated strain evolution in a commercial 18650 NMC lithium-ion cell using surface-mounted fiber Bragg grating sensors across 20 sequential conditions combining five discharge rates (1–4.5 C) and four cutoff voltages (2.5–1.0 V). All tests were performed on a single cell using identical 0.5 C constant-current constant-voltage charging, followed by a 2 h rest period and controlled discharge, to systematically evaluate mechanochemical evolution with increasing electrochemical severity. Maximum tensile strain during charging ranged from 45 to 59 µε and showed limited sensitivity to discharge severity. In contrast, discharge behavior exhibited clear rate- and cutoff-dependent transitions from tensile to compressive deformation; the most severe condition (4.5 C, 1.0 V cutoff) produced a peak compressive strain of about −27 µε and the most negative residual strain after relaxation. Although temperature increased monotonically with C-rate, strain evolution was nonlinear and non-monotonic, indicating that electrochemically induced stress dominated over thermal expansion alone. These findings reveal progressive amplification of irreversible deformation under severe discharge and demonstrate the value of fiber Bragg grating sensing for operando assessment of electrochemical–mechanical coupling in cylindrical lithium-ion cells. Full article

This study aims to experimentally evaluate and compare the electrical–thermal performance of a 20-cell 18650 lithium-ion battery pack cooled by a pure phase change material (PCM) and a PCM/TiO₂ nanoparticle composite to identify an effective passive thermal management approach for EV battery applications. Using a controlled charging–discharging system, thermocouple-based temperature mapping, and systematic tests across multiple C-rates (0.75 C–1.5 C), the study measures the variations in battery temperature, generated heat, and voltage behavior as functions of depth of discharge (DOD) and state of charge (SOC). The results show that the PCM/nanoparticle mixture markedly improves thermal conductivity, reduces peak temperature by approximately 8–10 °C compared with pure PCM, delays thermal saturation at higher C-rates, and enables a wider safe DOD range with reduced voltage sag and lower heat accumulation. Based on the experimental temperature/voltage trends in this study, limit DOD to ≤40–50% at high power (≈1.5 C), ≤50–60% at moderate power (≈1 C), and ≤60–70% at low power (≈0.75 C) (i.e., target SOC windows roughly 60–100% SOC at 1.5 C, 40–100% SOC at 1 C, and 30–100% SOC at 0.75 C), with an absolute practical upper DOD limit of ~70% to avoid frequent deep discharge damage; these limits keep peak temperatures below ~40–45 °C, reduce severe voltage sag near cutoff, and greatly extend cycle life because shallower cycling (e.g., 50% vs. 100% DOD) produces many times more cycles. These improvements enhance battery safety, performance stability, and cycle life, making the nanoparticle-enhanced PCM a practical, compact, and energy-efficient solution for passive battery thermal management in electric vehicles. Full article

The article presents the possibilities of integrating artificial intelligence (through specific machine learning techniques) in the design and construction process of a battery in order to optimize its thermal management. The workflow starts from CFD thermal simulations (1C-rate) of a battery (16 Li-ion cells, type 18650, 4 × 4 arrangement), and based on the results, a complex thermal landscape is created through radial basis function (Rbf) interpolation. Furthermore, a robust neural network (NN) model is proposed and validated through the obtained performances, which is used further for the optimization of the design space (DSO) and multi-objective optimization (MOO) processes. The obtained results show that for DSO, a cell spacing of 1.37 mm is proposed for a maximum cell temperature of 25.53 °C, and in the case of MOO, a cell spacing of 2.64 mm (for minimum fan energy consumption). The main conclusion of the obtained results shows that the use of the NN model as a surrogate (the Digital Twin of a physical model) presents two great advantages in the process of designing a battery: running a CFD simulation for each point on the 2D grid would take hours, while the NN model can generate the entire map and find the optimum in less than 2 s, and moreover, thousands of additional points can be evaluated to find the thin limit of optimal models, effectively filtering out thousands of energy-consuming “suboptimal” configurations. 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

Lithium-ion batteries are widely used in portable devices and electronic vehicles (EVs) due to their excellent performance. Because of their internal chemistry, these batteries have non-linear characteristics, their parameters being dependent on temperature and varying over time due to aging. Since electric vehicles are marketed in different regions of the globe with different climates, this has led to increased attention to the problem of the reduced performance of EVs in colder environments. The purpose of this research is to study the effects of preconditioning on Li-ion cells and determine the need for preconditioning in EVs that operate under low-temperature conditions. Additionally, based on the results, alternative coping strategies are also suggested which can be used instead of preconditioning when this is not a viable option. Given this, the 18650 Li-ion cells studied were divided into two categories, cells to be charged/discharged permanently at low temperatures and cells that were to be exposed to the same low temperatures but then preconditioned to ambient temperature before the charge/discharge cycle for a total of 100 performed cycles. It was observed that low temperatures have a direct negative impact on the usable capacity of the cells, accounting for a drop of 8% of the initial value. These effects can be completely negated by preconditioning the cells prior to charging/discharging. After that, the effects of medium-term storage on the capacity of the batteries were investigated to study the possible recovery in the capacity of the cells. Finally, the need for preconditioning the cells is analyzed and alternative methods to mitigate the issues are suggested for equipment where preconditioning is not possible. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Optimizing the operating temperature of lithium-ion batteries is critical for safe, reliable, and efficient cell operation. Manufacturers’ recommendations vary in this area, which is primarily determined by the cells’ chemical composition and internal structural characteristics. Most manufacturers define the maximum charging voltage level as the same or close to the same value, but there are significant differences in the lower threshold voltage. Lithium-ion cells exhibit increased internal resistance at lower state-of-charge levels, resulting in elevated heat generation during operation, with intensity proportional to the depth of discharge. However, using a too low voltage threshold causes a significant loss of usable capacity, which reduces the cell’s energy utilization. The present research aims to define and analyze the optimal value of the lower voltage threshold more precisely, considering both thermal development and usable capacity aspects. A further objective is to determine an optimal energy safety margin level that provides a suitable compromise for longer-term storage. Different 18650 and 21700 standard lithium-ion cell types were tested using various load profiles. The results show that the two cell formats have different electro-thermal behaviors. The 21700 cells show a clear increase in thermal efficiency at around 3.1 V. In contrast, the 18650 cells have a heating pattern that depends heavily on the load. This requires selecting a cutoff that adapts to the discharge rate to prevent excessive thermal stress. These findings indicate that a fixed lower threshold voltage for all cells is not ideal. Instead, we need cutoff strategies that are specific to each cell and can change dynamically. The TER-based evaluation introduced in this work provides a practical framework for defining these adaptive limits. It may improve control in battery management systems in real-world applications. Full article

This study explores the effects of low temperatures on the performance of various lithium-ion batteries (LIBs), comparing different sizes and chemical compositions. Experiments were conducted in a sub-zero temperature environment, examining discharge behavior, internal resistance, and capacity retention. The findings reveal that smaller-sized batteries (18650, 21700) have a marked resilience to cold, outperforming larger 26650 cells, with smaller average capacity declines noted in both LiCoO₂ and LiMn₂O₂ chemistries. The study also introduces a new adaptive filtering technique for better battery behaviour analysis at low temperatures, which avoids distortion of important electrochemical signals. Full article

This paper introduces a novel Lithium-Ion Battery (LIB) State-of-Charge (SOC) estimation approach that integrates Akima–Savitzky–Golay curve reconstruction with a Bayesian-optimized, adaptive Extended Kalman Filter (EKF). The method addresses crucial SOC estimation challenges by means of three foundational advancements: (i) a refined open-circuit voltage (OCV)-SOC curve reconstruction grounded in Akima interpolation coupled with Savitzky–Golay filtering, (ii) an adaptive EKF weighting strategy, and (iii) systematic hyperparameter value optimization executed through Bayesian optimization. Comprehensive performance validation utilizes an extensive dataset collected from LG HG2 18650 cells across temperatures of −20 °C to 40 °C, incorporating multiple standard driving cycles—namely HPPC, UDDS, HWFET, LA92, and US06 cycles. The proposed method achieves an improved estimation accuracy with an average Root Mean Square Error (RMSE) of 2.65% over the different operating conditions and temperature variations. Notably, the method markedly enhances SOC estimation reliability in the critical mid-SOC range (20–80%), while preserving the computational overhead necessary for real-time integration into Battery Management Systems (BMSs). The adaptive weighting successfully compensates for the present physical limitations, thereby delivering a resilient SOC estimation tailored for Electric Vehicle (EV) battery applications. Full article

Sodium-ion batteries (SIBs) are gaining attention in research and industry as a sustainable alternative to lithium-ion batteries (LIBs). However, the advantages of sodium over lithium in terms of accessibility, price, and environmental impact are currently not fully exploited because of inexperience in production, leading to inhomogeneities in their behavior. Using electrical (e.g., open-circuit voltage curve (OCV), electrochemical impedance spectroscopy) and non-electrical measurement methods (e.g., laser scanning microscopy, computed tomography), three widely used LIB technologies and one SIB technology, all with the same rated capacity (1500 mAh) and format (18650), are compared in this article. The study reveals significant differences, such as a 12% lower cell weight at the same rated capacity of the SIB using less windings in the jelly roll, as well as a high energy density cell configuration and a much more severe dependency of the discharge capacity on temperature, exceeding the LIBs by at least a factor of 5. Additionally, the impedance of the SIB differs due to slower ion kinetics on the electrodes, showing relevant differences in both the frequency behavior and the pulse relaxation to the LIBs. An OCV reconstruction indicates the sparsity in the available literature data and the necessity to further investigate the characteristics of the SIB to validate it as a drop-in technology on the market. Full article

Nowadays, immersion cooling-based battery thermal management systems have demonstrated their effectiveness in controlling the temperature of lithium-ion batteries. While previous scientific research has primarily concentrated on traditional dielectric fluids such as mineral oil, the current research investigates the effectiveness of the dielectric fluid FC-40. A three-dimensional Computational Fluid Dynamics model of an eight-cell 18650 battery system was constructed using ANSYS Fluent 19.2 to examine the effect of cooling fluids (air, mineral oil, and FC-40), velocity of flow (0.01 m/s to 0.15 m/s), discharge rate (1C to 5C), and inlet/outlet size (2.5 mm to 3.5 mm) on thermal efficiency as well as pressure drop. The findings indicate that employing FC-40 as the dielectric fluid significantly reduces the peak cell temperature, with an absolute decrease of 2.80 °C compared to mineral oil and 15.10 °C compared to air. Furthermore, FC-40 achieves the highest uniformity with minimal hotspot. On the other hand, as the fluid velocity increases, the maximum temperature of the battery drops, reaching a minimum of 26 °C at a velocity of 0.15 m/s. Otherwise, at lower flow velocities, the pressure drop remains minimal, thereby reducing the pumping power consumption. Additionally, increasing the inlet and outlet diameter of the fluid directly improves cooling uniformity. Consequently, the temperature dropped by up to 4.3%. Finally, the findings demonstrate that elevated discharge rates contribute to increased heat dissipation but adversely affect the efficiency of the thermal management system. This study provides critical knowledge for the enhancement of battery thermal management systems based on immersion cooling using FC-40 as a dielectric. Full article

In this paper, Distribution of Relaxation Time (DRT) analysis is presented as a powerful tool for understanding the aging mechanisms in lithium-ion batteries, with a focus on its application to estimating the State of Health (SOH). A novel parameter, the characteristic relaxation time, derived from DRT analysis, is introduced to enhance SOH estimation. By analyzing the ratio of the central relaxation time (τ) between the charge transfer and diffusion peaks, the battery status can be determined without the need for historical data. Experimental data from lithium-ion batteries, including 18650 cells and LR2032 coin cells, were examined until the end of their life. Nyquist and DRT plots across various frequency ranges revealed consistent aging trends, particularly in the charge transfer and diffusion processes. These processes appeared as shifting and merging peaks in the DRT plots, signifying progressive degradation. A polynomial equation fitted to the τ ratio graph achieved a high accuracy (Adj. R² = 0.9994), enabling reliable battery lifespan prediction. Validation with a Samsung Galaxy S9+ battery demonstrated that the method could estimate its remaining life, predicting a total lifespan of approximately 2100 cycles (compared to 1000 cycles already completed). These results confirm that SOH estimation is feasible without prior data and highlight the potential of DRT analysis for accurate and quantitative prediction of battery longevity. Full article

We report on the effect state of charge (SoC), cell format, and chemistry have on the volume and composition (H₂, CO₂, CO, CH₄, C₂H₄, C₂H₆, C₃H₆, and C₃H₈) of cell failure gas from Li-ion cells. Nickel manganese cobalt oxide (NMC) 21700 cells with a 5 Ah capacity were externally heated to failure at a 5–100% SoC under an inert atmosphere. This showed that the volume of gas increased with cell SoC (1.8 L at 5% SoC vs. 8.3 L at 100% SoC). The effect of the cell chemistry format and abuse method was also investigated using 18650, pouch, and prismatic cells (2.3–50 Ah) with Ni-based or lithium cobalt oxide (LCO) cathodes or lithium titanium oxide (LTO) anodes. The results showed that at higher SoCs, larger quantities of gas were generated; however, there was no correlation between the cell SoC and the composition of gases produced. Tests on the other cells found that the Ni-based cell generated 1.29–1.89 L/Ah of gas. The main constituents of this were H₂, CO, and CO₂; however, all other hydrocarbons were identified in varying quantities. The LTO cells generated lower volumes of gas, 0.8 L/Ah compared to Ni-based cells, and the gas was found to contain lower H₂ concentrations but higher concentrations of CO₂. The LCO cell was found to generate a gas volume of 1.2 L/Ah. This forms the final of four papers which cover a total of 213 tests on 29 cell types with six different chemistries, all tested using a single robust testing method. Full article

The increasing adoption of Light Electric Vehicles (LEVs) in urban areas, driven by the micromobility wave, raises significant safety concerns, particularly regarding battery fire incidents. This research investigates the electromechanical performance of aged 18650 lithium-ion batteries (LIBs) from LEVs under mechanical impact conditions. For this study, a battery module from a used e-scooter was disassembled, and its constituent cells were reconfigured into compact modules for testing. To characterize their initial condition, the cells underwent cycling tests to evaluate their state of health (SOH). Although a slight majority of the cells retained an SOH greater than 80%, a notable increase in their internal resistance (IR) was also observed, indicating degradation due to aging. The mechanical impact tests were conducted in adherence to the UL 2271:2018 standard, employing a semi-sinusoidal acceleration pulse. During these tests, linear kinematics were analyzed using videogrammetry, while key electrical and thermal parameters were monitored. Additionally, strain gauges were installed on the central cells to measure stress and deformation. The results from the mechanical shock tests revealed characteristic acceleration and velocity patterns. These findings clarify the electromechanical behavior of aged LIBs under impact, providing critical data to enhance the safety and reliability of these vehicles. 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