Jun Peng

Internal gas pressure provides insight into the evolving internal state of batteries, yet its behavior in sodium-ion batteries (SIBs) under abuse conditions remains poorly understood. Here, an extrinsic fiber Fabry–Perot sensing system was developed for operando monitoring of internal gas pressure in commercial cylindrical SIBs. Normal constant-current–constant-voltage cycling produced reproducible and predominantly reversible pressure variations, whereas elevated temperature preserved the main stage-dependent features but altered their magnitudes. During external heating, pronounced pressure accumulation preceded marked terminal-voltage deterioration and remained informative after voltage collapse. Under external short circuit, increasing state of charge enhanced the peak pressure increment and prolonged the pressure-rise stage, while repeated short circuits produced a cumulative pressure offset despite similar transient increments. High-voltage charging further revealed a transition from nonaccumulative to sustained post-charge pressure buildup between 4.1 and 4.2 V. Operando gas-pressure monitoring therefore provides a new dimension for probing the internal-state evolution of SIBs under safety-critical conditions, supporting abuse-state identification and the development of pressure-informed strategies for thermal-runaway warning and safer cell design.

In this work, an extrinsic fiber Fabry–Perot (F-P) pressure sensor was designed and fabricated for real-time monitoring of internal gas pressure inside 26700 cylindrical sodium-ion batteries. The internal pressure mainly originates from gas generation due to electrolyte decomposition and side reactions during battery charging-discharging operation. A prototype pressure sensor was developed and calibrated through controlled pressure tests to ensure measurement accuracy and stability. An experimental platform integrating the sensor with the sodium-ion battery was then constructed. Internal pressure variations were recorded during charge–discharge cycles under different ambient temperatures. The results reveal the dynamic evolution of internal pressure during electrochemical processes and highlight the significant influence of temperature on gas generation behavior. This study demonstrates the feasibility of using extrinsic fiber F-P sensors for in-situ pressure monitoring in sodium-ion batteries, providing valuable insights into battery safety and degradation mechanisms.

An accurate estimation of the state of health (SOH) of Li-ion batteries is critical for the efficient and safe operation of battery-powered systems. Traditional methods for SOH estimation, such as Coulomb counting, often struggle with sensitivity to measurement noise and time-consuming tests. This study addresses this issue by combining incremental capacity (IC) analysis and a novel neural network, Kolmogorov–Arnold Networks (KANs). Fifteen features were extracted from IC curves and a 2RC equivalent circuit model was used to identify the internal resistance of batteries. Recursive least squares were used to identify the parameters of the equivalent circuit model. IC features and internal resistance were considered as input variables to establish the SOH estimation model. Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. The performance of the five models was compared and analyzed. The results demonstrated that the hybrid models integrated with the KAN performed better than the conventional models, and the LSTM-KAN model had higher estimation accuracy than that of the other models. The model achieved a mean absolute error of less than 0.412% in SOH prediction in the test and validation dataset. The proposed model does not require complete charge and discharge data, which provides a promising tool for the accurate monitoring and fast detection of battery SOH.

Purpose: This study aims to address the issue of high-precision measurement of AC electric field. An electro-optical sensor with high sensitivity is proposed for this purpose. Design/methodology/approach: The proposed sensor combines electromagnetic induction and fiber Bragg grating (FBG) sensing techniques. It is composed of a sensing probe, a piece or stack of piezoelectric ceramics (PZT) and an FBG. A signal processing circuit is designed to rectify and amplify the induced voltage. The processed signal is applied to the PZT and the deformation of PZT is detected by FBG. Theoretical calculation and simulation are conducted to verify the working principle of the probe. The sensor prototype is fabricated and its performance is tested. Findings: The results of this study show that the sensor has good linearity and repeatability. The sensor sensitivity is 0.061 pm/Vm⁻¹ in the range from 250 to 17,500 V/m, enabling a measurement resolution of electric field strength of 16.3 V/m. The PZT stack is used to enhance the sensor sensitivity and the resolution can be improved up to 3.15 V/m. Originality/value: A flexure hinge lever mechanism is used to amplify the deformation of PZT for further enhancement of sensitivity. The results show that the proposed sensor has high sensitivity and can be used for the accurate measurement of an electric field. The proposed sensor could have potential use for electric field measurement in the power industry.

Lithium-ion (Li-ion) battery becomes a promising energy storage element in the power grid. Temperature monitoring is a key tool to guarantee the safe service and durable life of Li-ion batteries. In this study, an optical fiber sensor was proposed for the temperature monitoring of Li-ion batteries. The proposed sensor consists of a metal ring and a fiber Bragg grating (FBG), which is gloved on the external electrode of the cell. This installation is not only easy to assemble but also no interference to the circuit connection. The results of calibration test on proposed FBG sensors show good linearity (0.99) and high sensitivity (12 pm/°C). A comparison test illustrates the response of proposed FBG sensors has a good agreement with PT100 sensors. The FBG sensor can be fabricated as sensor arrays to monitor the temperature of multiple cells during charge/discharge cycles, simultaneously. The experimental results show that the FBG sensors have good repeatability during several cycles. FBG sensor arrays can be used to achieve distributed temperature measurement for multiple cells. It provides a solution to monitor the thermal behavior of each cell in battery packs and an available tool to improve the thermal management and safety of Li-ion batteries.