Search Results (190)

Nowadays, thermal runaway accidents involving lithium batteries in new energy vehicles and energy storage power stations occur frequently, with battery deformation pressure as the core precursor signal. Traditional battery protection schemes suffer from limitations, including wired connections, limited real-time remote monitoring, and insufficient sensing accuracy, rendering them unable to meet the safety monitoring needs of large-scale battery modules. Therefore, a high-precision pressure-sensing battery protection system based on the Internet of Things has been developed. This paper selects a MEMS high-precision pressure sensor with an accuracy of ±0.1 kPa to design an IoT sensing node based on the STM32L431 and LoRa/Wi-Fi 6, integrating pressure sensing and wireless communication. It proposes a sliding-average filtering and wavelet denoising algorithm, as well as a temperature-compensation calibration model, to optimize sensing accuracy. Additionally, it constructs a hierarchical early warning model based on pressure thresholds. The experiment demonstrates that the sensor achieves a detection accuracy of 99.2%, a response delay of less than 50 ms, a transmission packet loss rate of less than 0.5%, an end-to-end delay of less than 200 ms, and an early warning accuracy rate of 99.2% under battery overcharge/overtemperature conditions. The innovation of this study lies in the first integration of high-precision pressure sensing and IoT communication for battery protection. A low-power IoT sensing node tailored for battery aging scenarios has been designed, validating the novel application value of IoT sensing in the safety monitoring of new energy equipment. This system fills a gap in IoT pressure-sensing technology for battery protection, enabling practical applications and serving as a reference for implementing integrated sensing and communication technology. Full article

The inherent limitations of conventional polyolefin separators, particularly their poor thermal stability and insufficient mechanical strength, pose significant safety risks for lithium-ion batteries (LIBs) by increasing susceptibility to thermal runaway. In this study, we developed a novel multilayer separator through sequential coating of a commercial polyethylene (PE) substrate with aluminum oxide (Al₂O₃), para-aramid (PA), and polyethylene wax microspheres (PEWMs) using a scalable micro-gravure process, denoted as SAPEAS, signifying a PE-based asymmetric structure separator with enhanced thermal shutdown and dimensional stability. The SAPEAS separator exhibits an early thermal shutdown capability at 105 °C, maintains structural integrity with negligible shrinkage at 180 °C, and demonstrates comprehensive performance enhancements, including enhanced mechanical strength (tensile strength: 212.3 MPa; puncture strength: 0.64 kgf), excellent electrolyte wettability (contact angle: 12.8°), a high Li⁺ transference number (0.71), superior ionic conductivity (0.462 mS cm⁻¹), outperforming that of commercial PE separators. In practical LFP|Gr pouch cells with ampere-hour (Ah) level capacity, the SAPEAS separator enables exceptional cycling stability with 97.9% energy retention after 1000 cycles, while significantly improving overcharge tolerance compared to PE. This work provides an effective strategy for simultaneously improving the safety and electrochemical performance of LIBs. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

As a cushion for the volatility of renewable energy, energy storage systems can achieve peak shaving and valley filling, thereby improving the operational efficiency and economic performance of the power grid. In addition, energy storage systems can absorb renewable energy production, thereby enhancing the safety and reliability of the electrical power system. Nowadays, energy storage systems are facing severe problems such as explosions that are caused by overcharging and discharging. The main reason for the overcharging and discharging of energy storage systems is the inconsistency in the state of the electric core in the charging and discharging process, which not only affects the safety of the electric core, but also influences the overall charging and discharging capacity of the energy storage system. To address this inconsistency of energy storage cores, this paper proposes an energy storage consistency monitoring method under the framework of clustering-classification, which adopts the Belief Peaks Evidential Clustering and Evidential K-Nearest Neighbors classification algorithm. This paper proposes a BPEC-EKNN-based method for battery inconsistency detection and localization. The proposed approach first constructs battery performance evaluation coefficients to characterize inter-cell behavioral differences, and then integrates an enhanced k-nearest neighbor strategy to identify abnormal cells. It also identifies and locates inconsistent battery cells by analyzing the magnitude of the confidence level m (Ω), without relying on predefined thresholds. Also, time-series data as opposed to the evaluation of voltage data at a singular point is engaged to realize the detection and localization of energy storage core consistency anomalies under the consideration of time-series data. The proposed algorithm is capable of identifying inconsistencies among energy storage batteries, with the parameter m (Ω) serving as an indicator of the likelihood of inconsistency. Experimental results on battery pack datasets demonstrate that the proposed method achieves higher detection accuracy and robustness compared with representative statistical threshold-based methods and machine learning approaches, and it can more accurately identify inconsistent battery cells. By applying perturbation analysis to real-time operational data, the algorithm proposed in this paper can detect inconsistencies in battery cells reliably. Full article

This paper presents a self-contained startup charging circuit designed for energy-harvesting batteryless IoT devices. The proposed circuit consists of a current-biasing block, a current mirror, a reference voltage generator, and a comparator circuit. The current-biasing circuit drives the current mirror, which supplies the charging current to the energy storage element. Simultaneously, the reference voltage generator—also biased by the current source—produces a stable DC reference voltage. When the energy storage device (e.g., a supercapacitor) lacks sufficient charge, the comparator enables the charging path by activating the current-biasing and mirror circuits. Once adequate energy is stored, the comparator disables these circuits to prevent overcharging. This self-contained solution is intended to autonomously initialize and manage the cold-start charging process in energy-harvesting systems without relying on external controllers. This paper highlights the circuit architecture and validated performance, demonstrating a charging current of up to 27 mA, a reference voltage of 700 mV, and an operating range from 0.9 V to 1.8 V across a temperature range of −40 °C to 85 °C. Full article

Cross-seasonal and cross-regional operations make it inevitable for low-temperature cycling of lithium–ion batteries, which accelerates battery aging and induces large inconsistency between batteries in the battery pack. This causes slight overcharging. However, the influence of long low-temperature cycling on the following slight overcharging aging and aging mechanism under multi aging path is not studied clearly. This affects the function of the battery management system (BMS), including state of health (SOH) prediction, state of charge estimation, etc. This work takes 18,650-type batteries as the study objects. Battery aging at low temperature (−10 °C) and slight overcharging (4.4 V) aging after low-temperature cycling are studied in this work. Hybrid pulse power characteristic, incremental capacity analysis, scanning electron microscope, and X-ray diffraction are used to reveal the aging mechanisms. The results indicate that a negative electrode degradation affects the cycle life of batteries more compared to a positive electrode, and the primary aging mechanisms are “dead lithium” and electrolyte decomposition. Compared to low-temperature cycling, slight overcharging is the lower stress factor. Cycling at low stress factor suppresses aging of battery cycled at high stress factor. When the SOH of battery is near 90%, lithium plating growing at low temperature is consumed after slight overcharging cycling. The generated products suppress further lithium plating. When the SOH is near 80%, although lithium plating is consumed, it also grows continuously. Slight overcharging causes more transition metal dissolution and graphite exfoliation. When SOH is near 90%, thermal management strategies should operate to control operation temperature of battery to avoid further low-temperature cycling. The results in this work are important to battery design and battery management system development. Full article

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation. However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs. To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance. The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection. Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy. The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation. An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor. The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 °C. It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%. An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected. Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%. The system accurately tracked charging/discharging patterns, validating its effectiveness. This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution. Full article

The increasing prevalence of lithium-ion batteries in energy storage and electric transportation has led to a rise in overcharge-induced thermal runaway (TR) incidents. Particularly, the TR of Lithium Iron Phosphate (LFP) batteries demonstrates distinct evolutionary stages and multimodal hazard signals. This study investigated the TR process of LFP batteries under various charging rates through five sets of gradient C-rate experiments, collecting multimodal data (temperature, voltage, gas, sound, and deformation). Drawing on the collected data, this study proposes a three-stage evolution model that systematically identifies key characteristic signals and tracks their progression pattern through each stage of TR. Subsequently, fusion-based models (for both single- and multi-rate scenarios) and a time-series-based LSTM model were developed to evaluate their classification accuracy and feature importance in the classification of TR stages. Results indicate that the fusion-based models offer greater generalization, while the LSTM model excels at modeling time-dependent dynamics. These models demonstrate complementary strengths, providing a comprehensive toolkit for risk assessment. Furthermore, for the severe TR stage, this study proposes an innovative three-dimensional dynamic emergency decision matrix comprising a toxicity index (TI), flammability index (FI), and visibility (V) to provide quantitative guidance for rescue operations in the post-accident phase. Ultimately, this study establishes a comprehensive, closed-loop framework for LFP battery safety, extending from multimodal signal acquisition and intelligent early warning to quantified emergency response. This framework provides both a robust theoretical basis and practical tools for managing TR risk throughout the entire battery lifecycle. Full article

Marine and island power systems usually incorporate various forms of energy supply, which poses challenges to the coordinated control of the system under diverse, irregular, and complex load operation modes. To improve the stability and self-sufficiency of island-isolated microgrids with high penetration of renewable energy, this study proposes a coordinated control strategy for an island microgrid with PV, HGT, and HESS, combining primary power allocation via low-pass filtering with a fuzzy logic-based secondary correction. The fuzzy controller dynamically adjusts power distribution based on the states of charge of the battery and supercapacitor, following a set of predefined rules. A comprehensive system model is developed in Matlab R2023b, integrating PV generation, an electrolyzer, HGT and a battery–supercapacitor HESS. Simulation results across four operational cases demonstrate that the proposed strategy reduces DC bus voltage fluctuations to a maximum of 4.71% (compared to 5.63% without correction), with stability improvements between 0.96% and 1.55%. The HESS avoids overcharging and over-discharging by initiating priority charging at low SOC levels, thereby extending service life. This work provides a scalable control framework for enhancing the resilience of marine and island microgrids with high renewable energy penetration. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

Lithium iron phosphate (LFP) batteries have become a popular choice for energy storage, electrified mobility, and plants. All lithium-based batteries produce flammable vent gas as a result of failure through thermal runaway. LFP cells produce less gas by volume than nickel-based cells, but the composition of this gas most often contains less carbon dioxide and more hydrogen. However, when LFP cells fail, they generate lower temperatures, so the vent gas is rarely ignited. Therefore, the hazard presented by a LFP cell in thermal runaway is less of a direct battery fire hazard but more of a flammable gas source hazard. This research identified the constituents and components of the vent gas for different sized LFP prismatic cells when overcharged to failure. This data was used to calculate the maximum homogenous concentration of gas that would be released into a 1.73 m³ test rig and the percentage of the lower explosive limit (LEL). Overcharge experiments were conducted using the same type of cells in the test rig in the presence of remote ignition sources. Ignition and deflagration of the vent gas were possible at concentrations below the theoretical LEL of the vent gas if it was homogeneously mixed. Full article

In this study, cells with three different cell chemistries Na₃V₂(PO₄)₂F₃ (NVPF), LiNi_0.6Mn_0.2Co_0.2O₂ (NMC) and LiFePO₄ (LFP) are analyzed in exactly the same setup to compare the hazardous vent gases and their thermal behavior during thermal runaway (TR). Additionally, the influence of different triggers on the failure behavior of NVPF cells is elucidated. The innovative perspective is providing a direct comparison of the three cell chemistries, the influence of the trigger method on the vent gas composition and the thermal behavior. Of the three cell chemistries, LFP releases the least amount of vent gas at 0.02 mol/Ah (41% H₂, 27% CO₂, 8% CO), followed by NVPF at 0.05 mol/Ah (42% CO₂, 17% electrolyte solvent, 15% H₂ and 10% CO) and NMC at 0.07 mol/Ah (36% CO, 24% CO₂, 19% H₂). The maximum vent gas temperature increases from NVPF (265 °C) to LFP (446 °C) and NMC (1050 °C). As for the triggers, overcharge has the highest vent gas production of the NVPF cells at 0.07 mol/Ah. The results offer valuable insight into storage system design and expand the assessment of battery cells. Full article

The energy management strategy of Hybrid Electric Vehicles (HEVs) plays a key role in improving fuel economy and reducing battery energy consumption. This paper proposes a Deep Reinforcement Learning-based energy management strategy optimized by the experience pool (P-HER-DDPG), aimed at improving the fuel efficiency of HEVs while accelerating the training speed. The method integrates the mechanisms of Prioritized Experience Replay (PER) and Hindsight Experience Replay (HER) to address the reward sparsity and slow convergence issues faced by the traditional Deep Deterministic Policy Gradient (DDPG) algorithm when handling continuous action spaces. Under various standard driving cycles, the P-HER-DDPG strategy outperforms the traditional DDPG strategy, achieving an average fuel economy improvement of 5.85%, with a maximum increase of 8.69%. Compared to the DQN strategy, it achieves an average improvement of 12.84%. In terms of training convergence, the P-HER-DDPG strategy converges in 140 episodes, 17.65% faster than DDPG and 24.32% faster than DQN. Additionally, the strategy demonstrates more stable State of Charge (SOC) control, effectively mitigating the risks of battery overcharging and deep discharging. Simulation results show that P-HER-DDPG can enhance fuel economy and training efficiency, offering an extended solution in the field of energy management strategies. Full article

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

This paper reports on the failure of cells with lithium iron phosphate (LFP) chemistry tested under a range of conditions to understand their effect on the volume and composition of gas generated. Cells of the following formats, 26,650, pouch, and prismatic, and capacities ranging from 3 to 230 Ah, were subjected to external heat, overcharge, and nail penetration tests. Gas volume was calculated, and the following gases analysed: H₂, CO₂, CO, CH₄, C₂H₄, C₂H₆, C₃H₆, and C₃H₈. Cells that failed via external heating under inert conditions (N₂ or Ar atmosphere) at 100% state of charge (SoC) typically generated 0.7 L/Ah of gas; overcharged cells, 0.11–0.68 L/Ah; and nail penetration between 0.3 and 0.5 L/Ah. In general, for all test configurations, regardless of atmosphere, the total gas volume contained a 40% concentration of H₂, 15% of CO₂, and the remaining gas consisted of varying concentrations of CO and flammable hydrocarbons. This demonstrates that despite differences in gas volume, the failure gas composition of LFP cells remains similar. Full article