Search Results (361)

Electric vehicles are becoming more common daily because countries are moving towards net-zero emissions. Different generations of NMC battery cells are used for EV applications. This work investigates the degradation behavior of high-energy 75 Ah prismatic NMC631 lithium-ion cells using a combined incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) framework under different conditions. Cells are cycled at an identical C-rates and depths of discharge (DoD), and at different temperatures to systematically evaluate the impact of temperature on electrochemical aging. ICA results revealed that cells cycled at low temperatures maintain stable peaks and a high SoH (>90%) after completing 1600 full equivalent cycles (FECs). EIS analysis confirms the distinct impedance evolution patterns. Degradation mode analysis is performed using the ICA, and EIS highlights the combined evolution of conductivity loss, loss of lithium inventory, and loss of active material. It also highlights different degradation path trajectories under identical operating conditions stem from the progressive amplification of internal cell heterogeneities during aging. The results demonstrate that combining ICA and EIS provides complementary insights into degradation evolution and enables clear differentiation between gradual aging and sudden failure pathways in high-energy NMC cells. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

In battery systems, external mechanical compression is commonly applied to pouch/prismatic cells to improve their electrical performance and mechanical integrity. However, cell clamping can hinder system heat rejection by introducing an additional thermal insulation layer. A novel battery clamping scheme was designed with reduced contact area to explore the system thermal behaviour under different cooling regimes. Experimental data obtained from battery characterisation and performance tests is analysed with a thermal-coupled equivalent circuit model to quantify changes in cell impedance and system thermal properties. By reducing the clamping area by 70%, the temperature rise of the cell was decreased by 0.5 °C in comparison to the reference condition of a cell with no clamping during a 1C discharge under natural convection. Under immersion cooling using BOT2100 dielectric liquid, the thermal benefit was amplified, resulting in temperature reductions of 0.9 °C at 1C and 4 °C at 3C. The principal conclusion of this work is that reshaping the clamping plate has the potential to reduce ohmic heating by lowering battery internal resistance, which outweighs the additional thermal resistance introduced by partial surface coverage. This novel experimental approach demonstrates the potential to improve battery thermal management through geometry-optimised cell clamping, particularly for high-power applications, and further directs the community towards cell clamping solution designed to optimise both thermal and mechanical cell performance. Full article

Immersion cooling has been widely investigated in battery thermal management due to its high cooling efficiency; however, the influence of coolant properties on the thermal behavior and temperature uniformity of large-capacity energy storage battery modules remains unclear. In this study, a three-dimensional numerical model is developed to investigate the thermal performance of an immersion-cooled battery module consisting of 52 prismatic cells. The cooling performance of silicone oil (SO), synthetic hydrocarbon (SH), and two synthetic esters (SE) with different viscosities is systematically compared under various discharge rates and volumetric flow rates. The battery thermal model was validated through single-cell experiments under natural air convection conditions. The research results indicate that at a 0.5C discharge rate, the 30 cSt SE achieves a reduction in maximum battery pack temperature of 6.3% and 7.0% compared to SO and SH, respectively. Furthermore, the maximum temperature difference is significantly reduced by 22.9% and 25.4% under the same conditions. Due to differences in the inherent properties and flow heat transfer characteristics of the coolant, at a volumetric flow rate of 12 L/min, the 30 cSt SE resulted in a 15.8% reduction in module temperature difference compared to the 20 cSt SE. To further evaluate the internal thermal balance of the battery module, two thermal uniformity indicators were introduced to quantify the consistency of the highest temperature of individual cells and the internal temperature difference. Considering both the temperature performance and thermal uniformity at the module level, from a heat dissipation performance perspective, the 30 cSt SE demonstrates significant potential for thermal management of large-scale prismatic battery packs. Full article

The operational safety and endurance of unmanned aerial vehicles (UAVs) are strongly affected by lithium-ion battery degradation under extreme thermal environments. However, conventional physics-based models often rely on simplified assumptions, whereas purely data-driven methods usually lack physical interpretability and robust generalization. To address these limitations, this study proposes a Physics-Informed Deep Neural Network (PIDNN) for predicting UAV battery degradation under complex environmental conditions. The proposed framework integrates thermodynamic and fluid dynamic principles with deep neural networks by incorporating physical constraints derived from heat generation, heat conduction, and convective heat transfer into the loss function. This design enables the model to capture nonlinear degradation patterns while maintaining consistency with fundamental physical laws. Comprehensive simulation-based experiments were conducted under high-temperature (45 °C), low-temperature (−20 °C), and room-temperature (25 °C) conditions, together with varying discharge rates, humidity levels, wind speeds, and multi-factor coupled scenarios. The results show that the proposed PIDNN consistently outperforms conventional physics-based models and several representative data-driven methods, including SVM, LSTM, and GAN-based approaches. It achieves lower prediction errors across all evaluated conditions, as reflected by reduced mean absolute error and root mean square error. By providing physically consistent predictions of capacity fade, internal resistance growth, and remaining useful life, the proposed framework supports degradation-aware monitoring and early warning for intelligent battery management systems. These findings provide a robust methodological basis for improving the reliability, safety, and service life of UAV power systems operating in complex climatic environments. Full article

The demand for high-energy-density and fast-charging solid-state lithium metal batteries (SSLMBs) often subjects practical devices to internal thermal loads, making high-temperature operation a common operational condition rather than an isolated scenario. To address the interfacial degradation and dendrite growth accelerated by such thermomechanical stresses, we developed a composite gel electrolyte (CGE) by incorporating an optimal concentration of active Li_6.4La₃Zr_1.4Ta_0.6O₁₂ (LLZTO) into a fluoropolymer network. The abundant Lewis acidic sites on the LLZTO surfaces promote competitive solvation decoupling by interacting with anions, thereby modulating the primary solvation sheath of Li⁺. This localized modulation lowers the lithium-ion migration activation energy to 0.248 eV and facilitates a dual-interfacial passivation mechanism. Specifically, a rigid, inorganic-rich solid electrolyte interphase (SEI) forms to suppress morphological instability at the lithium anode, while an organic-dominated cathode electrolyte interphase (CEI) enhances the oxidative stability up to 4.3 V. As a result, symmetric cells demonstrate stable electrodeposition for over 450 h at 80 °C and 0.5 mA cm⁻². Furthermore, NCM811/Li full cells utilizing this CGEs exhibit significantly improved thermal resilience and cycling stability. Full article

Thermal runaway accidents in lithium batteries necessitate effective thermal management. This study proposes a liquid cooling plate with internal spiral-array fins and investigates its performance under electrochemically coupled temperature-dependent heat generation conditions. A pseudo-two-dimensional (P2D) electrochemical model simulates battery discharge at 0.5C–2C rates to obtain heat generation characteristics, which serve as inputs for a fluid–solid coupled heat transfer model. The effects of spiral fin parameters—pitch (S) and height (h)—are systematically analyzed. Three main contributions are presented: spiral fins induce secondary flow that disrupts thermal boundary layer development and enhances fluid mixing, with smaller pitch extending the flow path and increasing radial velocity; a performance evaluation criterion (PEC)-based analysis identifies the optimal parameter range that balances heat transfer enhancement and pressure drop penalty; and increasing the fin height raises the finned area proportion and swirl intensity, suppressing bypass flow and strengthening heat transfer, with effects more pronounced at higher discharge rates. Key quantitative findings show that at 2C discharge, the optimized configuration (S = 3 mm, h = 0.5 mm) achieves a comprehensive performance index of 2.19 and reduces the maximum temperature by 25.32% compared to smooth channels. This work integrates electrochemical and thermal models to provide a new approach for optimizing spiral fin microchannels tailored to lithium battery operation. Full article

This study focuses on the instantaneous effects of fast charging technologies, in terms of the daily operation of mobile devices, and specifically on the trade-off between fast charge and discharge efficiency. A controlled experimental layout is used, containing three smart devices, iPhone 17 Pro, iPad 11 Air and MacBook Pro, and four variations in chargers. The research monitored important values like the voltage, current, power and thermal behavior of the selected devices. These comparative results showed that high-speed charging at 67 Watts causes peak temperatures in the battery to be 41.5 °C, which is significantly higher compared to charging under standard protocols of 20 W, with values of 33.1 °C. This thermal stress forces the battery outside of its optimum operating window and consequently increases the internal resistance of the battery which results in a reduction of about 5% of the subsequent discharge runtime. Although fast charging offers a rapid energy replenishment, the thermal penalty incurred by the fast charging process reduces the battery’s short-term utility, suggesting that standard charging is the best option to maximize the single-cycle duration. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

This work demonstrates the potential of fiber optic sensors for measuring thermal effects in lithium-ion batteries, using a fiber optic measurement method of Optical Frequency Domain Reflectometry (OFDR). The innovative application of fiber sensors allows for spatially resolved temperature measurement, particularly emphasizing the importance of monitoring not just the exterior but also the internal conditions within battery cells. Utilizing inert glass fibers as sensors, which exhibit minimal sensitivity to electric fields, opens up new pathways for their implementation in a wide range of applications, such as battery monitoring. The sensors used in this work provide real-time information along the entire length of the fiber. It is shown that using the herein presented novel sensors in a temperature range of 0–$80°\mathrm{C}$ reveals a linear, high-sensitivity thermal measurement characteristic with a local resolution of a few centimeters. Furthermore, this study presents preliminary findings on the potential application of fiber optic sensors in lithium-ion battery (LIB) cells, demonstrating that the steps required for battery integration do not impose any restrictive effects on thermal measurements. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

Temperature and humidity monitoring in grain-carton warehousing is essential for quality assurance, yet fixed wiring is difficult under frequent stacking and battery-powered tags require routine maintenance. This study proposes a distributed passive monitoring sensing system that combines high-efficiency light energy harvesting with low-power long-range wide-area network (LoRa) communication. The key novelty is a carton-oriented separated architecture: an external photovoltaic harvester is wired to internal sensing/communication modules, mitigating stack-induced shading and enabling reliable operation for sensors embedded inside densely stacked cartons; an occlusion-tolerant multi-tag reporting strategy is further adopted. The tag integrates (i) an energy management module based on the bq25570 with a monocrystalline light cell and energy storage for low-light/intermittent illumination, (ii) a LoRa transceiver optimized for long-range and occlusion-tolerant data delivery, and (iii) a temperature–humidity sensing module for reliable microenvironment measurements. A hardware layout with an external photovoltaic panel and internal core modules mitigates carton-induced shading, while low-power scheduling and a lightweight protocol ensure robust sensing and transmission. Experiments show that the energy management module achieves > 60% charging efficiency at a 1.3 V input. After penetrating three layers of grain cartons, the LoRa link maintains a stable range of 500–800 m with ≤1% packet loss under concurrent multi-tag transmission. The measurement errors are within ±1 °C and ±3% relative humidity (RH) in the experimental setup. The proposed system eliminates fixed bus wiring and routine battery replacement, offering a scalable solution that enables maintenance-free monitoring in densely stacked warehousing environments. Full article

This research focuses on the passive behavior changes of 3 Ah pouch LiFePO₄ (LFP) batteries during low-temperature storage, a point often neglected in previous studies. This experiment examines the low-temperature non-operational endurance of fully charged batteries (FCB) at 25 °C, −10 °C, and −35 °C. Battery performance reliability under these conditions is evaluated through capacity retention and internal resistance (IR) analysis. Microstructural changes on the surfaces of thawed battery electrodes are acquired using scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. After seven freeze–thaw cycles, the maximum usable capacity is marginally affected. Notably, a pronounced increase in polarization resistance (R_p) has been observed, particularly at −10 °C conditions, with an increase of about 40.57 mΩ. Microstructural analyses reveal that low-temperature storage significantly led to cracking of the electrolyte layer and of the particles in the anode material. Subsequently, at room temperature (RT, 25 °C), external short circuit (ESC) tests were performed on thawed batteries. At 50C, the peak temperatures recorded at the center of the FCB₋₁₀, FCB₂₅, and FCB₋₃₅ batteries are 104.35 °C, 94.67 °C, and 90.56 °C, respectively. The batteries exhibit rupture at approximately 47 s, 60 s, and 70 s during the ESC process. The results show that battery FCB₋₃₅ exhibits a slower temperature rise and delayed physical damage during ESC. Full article

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article

The widespread adoption of electric mobility has accelerated decarbonization in transportation applications, increasing the reliance on lithium-ion batteries (Li-IBs) in electric vehicles (EVs) and energy storage systems. To analyze battery risk under different combinations of ambient temperature, discharge C-rate, and state-of-charge (SoC) windows, this study experimentally investigates power fade (PF) and capacity fade (CF) as degradation-based risk indicators. In addition to experimental observations, degradation conditions reported in previous studies are considered to identify reliable and unreliable operating zones. Several variables, including operating temperature, current rate, and SoC, influence the short- and long-term performance of Li-IBs in EV applications and should be evaluated from a safety perspective. Under combined thermal and electrical operating conditions, battery degradation progresses, associated with reductions in usable energy and power, increased internal heat generation, and increased safety risks. Due to the nonlinear behavior of Li-IBs, conventional risk models may not always fully represent battery performance; therefore, qualitative analysis and risk assessment are employed. Aging is monitored using discharge capacity, discharge energy, power rating, internal resistance, and open-circuit voltage within the proposed framework. The experimental results show that operational risk increases under high discharge C-rates combined with low ambient temperature. Discharging at 0.2 C at 25 °C with an SoC of 80% is identified as a critical operating scenario within the investigated conditions, as it results in both CF and PF. In contrast, Li-IB safety is not significantly affected under CF conditions at 4 C and 3 C at 10 °C at the same SoC level, nor under PF conditions at 0.2 C at 10 °C with SoC levels of 80% and 50%. The multi-indicator risk assessment combines individual indicators to compare operating conditions in terms of associated safety risk. Finally, the results confirm that relying on a single performance indicator tends to underestimate degradation, while a combined multi-indicator approach provides a better representation of Li-IB performance over battery lifetime. Full article