Experimental Study on Distributed Measurement of Internal Pressure in Lithium-Ion Batteries Using Thin-Film Sensors

Abstract

With the rapid development of electric vehicles, the safety and reliability of lithium-ion batteries (LIBs), as their core energy storage units, have become increasingly prominent. The variation in internal battery pressure is closely related to critical issues such as thermal runaway, mechanical deformation, and lifespan degradation. The non-uniform distribution of internal pressure may trigger localized hot spots or even thermal runaway, posing significant threats to vehicle safety. However, traditional external monitoring methods struggle to accurately reflect internal pressure data, and single-point external pressure measurements fail to capture the true internal state of the battery, particularly within battery modules. This limitation hinders efficient battery management. Addressing the application needs of electric vehicle power batteries, this study integrates thin-film pressure sensors into LIBs through the integrated functional electrode (IFE), enabling distributed in situ monitoring of internal pressure during long-term cycling. Compared to non-implanted benchmark batteries, this design does not compromise electrochemical performance. By analyzing the pressure distribution and evolution data during long-term cycling, the study reveals the dynamic patterns of internal pressure changes in LIBs, offering new solutions for safety warnings and performance optimization of electric vehicle power batteries. This research provides an innovative approach for the internal state monitoring of power batteries, significantly enhancing the safety and reliability of electric vehicle battery systems.

1. Introduction

Lithium-ion batteries (LIBs), as high-efficiency and portable energy storage units, are characterized by their superior specific capacity, extended cycle life, and environmental compatibility [1,2,3,4,5] and have been widely deployed in consumer electronics, electric vehicles (EVs), and grid-scale energy storage systems [6,7,8,9]. Among these applications, LIB performance directly governs the operational cost and safety of EVs, while the rapid advancement of EV technology imposes increasingly stringent requirements on battery capabilities [3]. The global surge in demand for clean-energy transportation has triggered exponential growth in the EV market. This trend not only accelerates the technological iteration of LIBs but also elevates the criticality of enhancing energy density and safety metrics. Structural and material innovations in LIB design—such as advanced electrode architectures and electrolyte formulations—offer pathways to improve capacity and intrinsic safety [10,11]. However, such optimizations mitigate the probability of failure events rather than eliminating safety incidents under extreme operating conditions, leaving absolute operational safety unguaranteed. Enhancing the monitoring capabilities of battery management systems (BMSs), refining thermal management strategies, and optimizing thermal runaway prediction algorithms can provide optimal operational protocols for onboard batteries [12]. Current BMS implementations predominantly rely on electrical parameter monitoring (e.g., voltage, current). However, these implementations employ limited measurement modalities that lack spatially resolved, real-time thermomechanical state tracking. Pressure dynamics, however, serve as critical indicators of internal battery states, directly reflecting thermal stability and mechanical integrity, thereby playing a pivotal role in BMS functionality. Consequently, integrating pressure monitoring into BMS architectures is imperative for comprehensive battery management.

The internal pressure within the battery primarily arises from the constrained expansion of the battery itself, due to the confinement imposed by its casing and external structural supports. Both the intrinsic expansion behavior of the battery and the mechanical constraints provided by external supports exert significant influences on the spatial and temporal evolution of internal pressure distribution. The expansion mechanisms are intrinsically linked to electrochemical processes, aging-induced material degradation, and phase transformations within the cell. Nafrh et al. [13] systematically investigated pressure accumulation in LCO/graphite lithium-ion pouch cells under varying C rates and thermal conditions, identifying critical contributing factors such as solid electrolyte interphase (SEI) fracture, electrolyte decomposition, and lithium plating phenomena. Liu et al. [14] investigated the impact of battery type and cycling mileage on the long-term stress–state of health (SOH) relationship, revealing that the global stress evolution within batteries arises from the combined effects of linear film-growth mechanisms and nonlinear stress-relaxation processes. This mechanistic understanding enables a more precise characterization of LIBs’ health status. Advancement in pressure metrology resolution directly enhances SOH estimation accuracy, which is paramount for optimizing battery performance, extending service life, and ensuring operational safety. Improved SOH prediction models significantly enhance battery system reliability and economic viability [15,16,17]. Concurrently, temperature fluctuations induce volumetric changes through thermal expansion effects, further complicating pressure dynamics [18]. Aiello et al. [19] developed a novel testing platform to decouple thermal–mechanical interactions during electrical operation, underscoring the critical need for co-monitoring of thermal and mechanical states in LIBs. Pressure variations alter the compression state of porous electrodes, profoundly influencing lithium-ion transport kinetics and associated electrochemical processes, thereby impacting overall cell capacity [20]. Mechanical constraint strategies significantly affect LIB longevity and performance metrics. Wüsch et al. [21] conducted comparative cycling aging tests on automotive pouch cells with diverse constraint systems, demonstrating substantial variations in material degradation patterns across different mechanical loading configurations. Innovative approaches employing hybrid fixtures achieved a <10% expansion rate after 300 cycles under controlled external pressures (110–248 kPa), matching performance benchmarks of state-of-the-art lithium-ion technologies [22].

Current research predominantly focuses on correlating pressure variations with battery states during cycling and evaluating pressure-dependent performance degradation in LIBs. However, methodological constraints persist, as most pressure measurement techniques—including load cells, confined vessel pressure sensors, or indirect single-point monitoring—suffer from limited spatial resolution, insufficient sensitivity, and compromised accuracy [14,23,24,25]. Furthermore, the measurement of mechanical pressure on batteries through external constraints is influenced by the material dimensions and modulus of the battery casing, which cannot directly reflect the internal pressure state. Reliance on single-point pressure measurement data fails to accurately represent the true internal conditions of batteries, particularly for individual cells within battery modules. This limitation hinders effective battery management strategies. Recognizing these limitations, several teams have pioneered in situ pressure measurement approaches. Zhu et al. [26] employed a Rigaku D/max-2600pc X-ray diffractometer (40 kV, 40 mA, Cu Kα) for operando pressure monitoring, while Wu et al. [27] integrated 10 strain gauges along the mid-section of 18650 cell to measure circumferential strain. Zhu et al. [28] further utilized thin-film strain sensors to track internal hoop strain evolution in 18650 cell during cycling. The application of strain gauges in detecting internal stress variations is relatively common. However, they provide limited information and face challenges in the widespread adoption of commercial batteries [29]. The acquisition of battery internal information extends beyond pressure monitoring, with temperature detection serving as another primary approach. Mutyala et al. [30] developed a thermocouple integrated into lithium-ion pouch batteries, enabling effective real-time temperature monitoring. However, similar to strain gauge measurements, thermocouple-based temperature detection faces limitations in multi-point sensing and provides only limited information. Currently, the application of miniaturized high-precision fiber optic sensors and thin-film sensors for internal battery monitoring has progressively become a research focus [31,32,33,34]. These sensors enable simultaneous monitoring of both internal pressure and temperature. Zhu et al. demonstrated that thin-film sensors could measure real-time temperature variations under different current rates [7], while Mei et al. [35] achieved in situ continuous monitoring and precise decoding of internal temperature and pressure in 18650 cell during thermal runaway scenarios using fiber optic sensors. Nevertheless, intrusive sensor integration risks compromising hermeticity, accelerating lithium plating, and exposing sensors to electrolyte corrosion, which collectively undermine measurement stability and longevity. Current prominent battery internal sensing technologies additionally encompass gas sensing and ultrasonic spectroscopy. Gas sensing enables monitoring of internal gas generation [36], while ultrasonic spectroscopy facilitates in situ early detection of lithium plating [37]. However, their substantial costs and considerable size constrain device portability, posing significant challenges for vehicular power battery applications. In contrast, thin-film sensors demonstrate superior adaptability to complex operational conditions in vehicle-mounted batteries while meeting requirements for portability and cost-effectiveness. Chen et al. [20] demonstrated the feasibility of in situ flexible thin-film pressure sensors for jelly roll pressure mapping in large-format LIBs. However, the thin-film sensors utilized in this study exhibit a miniature footprint, occupying merely 0.36% of the battery surface area, which fundamentally restricts their capacity to achieve spatially distributed measurements. This geometric constraint inherently compromises the acquisition of comprehensive mechanical data across the battery system. These challenges underscore the critical need for non-destructive, distributed internal pressure monitoring technologies to enhance the safety and reliability of electric vehicle battery systems.

In this study, we present a novel methodology for the non-destructive integration of sensing elements within LIBs, utilizing thin-film pressure sensors embedded into a LiNi_0.5Co_0.2Mn_0.3O₂ (NCM523) battery via an IFE design [38]. This approach enables distributed monitoring of internal pressure. The ternary lithium battery and the non-implanted benchmark battery underwent over 400 synchronized test cycles. Comparative analysis of key electrochemical performance parameters demonstrates that the design scheme does not compromise the battery’s electrochemical functionality. The results demonstrate that the IFE-integrated sensor architecture induces no measurable degradation in electrochemical performance. More critically, this study investigates and analyzes the spatial distribution and temporal evolution of internal pressure within lithium-ion batteries during prolonged cycling, revealing dynamic patterns of pressure variation. These findings provide critical insights into understanding battery failure mechanisms and advancing safety performance optimization strategies.

2. Materials and Methods

The lithium-ion batteries employed in this study were NCM (nickel–cobalt–manganese) ternary batteries manufactured by Zhuzhou Cubenergy New Energy Technology Co., Ltd. (Zhuzhou, China). The cathode material consisted of LiNi_0.5Co_0.2Mn_0.3O₂, while the anode utilized graphite, with a polypropylene film serving as the separator. The flexible thin-film pressure sensors, fabricated by Dongguan Moxian Technology Co., Ltd. (Dongguan, China), measured 45 mm × 58 mm with a full-scale accuracy of 5% (Figure 1a). The sensor incorporated 9 independent sensing units arranged in a 3 × 3 matrix, enabling distributed pressure measurements across the battery. Given the significant influence of fixture design on battery performance and cycle longevity [21], as well as the critical relationship between internal pressure measurements and fixture-applied constraints, a specialized battery fixture was designed for this investigation (Figure 1b). The fixture’s main structure was constructed from 304 stainless steel, selected for its superior machinability and mechanical properties to ensure assembly precision. Peripheral springs and a central rotary compression mechanism were integrated, with the battery securely positioned between two steel plates to guarantee uniform force distribution and unrestricted expansion during testing.

The IFE primarily consisted of two single-sided coated electrodes. During assembly, the thin-film pressure sensor was fixed between the two electrodes of the IFE, and the entire assembly was integrated into the current collector of the pouch cell. The IFE configuration minimized interference with battery performance while enabling non-destructive pressure monitoring. Sensor wires were routed through the end opposite to the battery tabs. Following cell assembly, the current collector and tabs were joined via ultrasonic welding, after which the cell was encapsulated with aluminum–plastic laminate film. All components were dried in an 85 °C vacuum oven before electrolyte injection (5 g, composition: ethylene carbonate (EC), ethyl methyl carbonate (EMC), dimethyl carbonate (DMC), and 1 M LiPF6). To determine the positioning and implantation status of thin-film sensors within the battery, X-ray computed tomography (CT) was utilized for non-destructive testing (Figure 1d). The CT analysis confirmed the successful integration of the thin-film sensors, revealing their precise alignment and intact morphology within the battery’s internal architecture (Figure 1e). The benchmark battery followed identical fabrication procedures, excluding sensor integration. As illustrated in Figure 1f, the pouch cells were secured in fixtures with a preload applied via a central rotary spindle, followed by formation at 0.05 C. Post-formation, cells underwent secondary degassing before final sealing and reinstallation in the fixture under identical conditions.

Throughout the cycling test, the sensor-embedded pouch cells underwent charge–discharge cycles at a 1 C rate (1 C = 1210 mA) under 25 °C ambient conditions. The charging process was performed in constant current mode until the battery voltage reached 4.2 V, followed by constant voltage maintenance until the current decreased to 0.05 C. After charging, the battery was rested for 30 min and then discharged at a 1 C rate to a cutoff voltage of 3.0 V. Subsequently, an additional 30 min rest period was implemented, thereby completing one full charge–discharge cycle. Following formation, capacity calibration was conducted via 200 mA constant current discharge. Electrochemical impedance spectroscopy (EIS) measurements were implemented at both 100% and 0% state of charge (SOC), with the first-cycle discharge capacity serving as nominal capacity. A 2 h relaxation period preceded each EIS measurement to stabilize the electrochemical potential. EIS parameters included a frequency range of 10 kHz to 0.01 Hz with 10 mV sinusoidal amplitude. Subsequent EIS evaluations at both SOC states were performed every 100 cycles using a Biologic VMP3 potentiometer. The benchmark cell underwent identical cycling protocols and capacity calibration without fixture constraints, maintaining equivalent charge–discharge procedures to the sensor-embedded cell.

3. Results and Discussion

3.1. Cycling Test and Electrochemical Test Results of the Sensor-Embedded Battery

Throughout 400 charge–discharge cycle tests, both the benchmark cell and the sensor-embedded cell were subjected to cycling at a 1 C rate. The charge/discharge curves of the lithium-ion battery embedded with a thin-film sensor during cycling tests are presented in Figure 1a,b. Over 400 cycles, the battery exhibited slight electrochemical polarization and reasonable capacity decay, both of which represent typical changes inherent to normal battery cycling processes. No significant abnormalities were observed throughout the testing period. During the cycling tests, the battery discharge capacity exhibited an overall linear decay trend (Figure 2c). The sensor-embedded cell initially displayed a slightly lower capacity than the benchmark cell, but its residual capacity gradually exceeded that of the benchmark counterpart as cycling progressed. Although minor discrepancies in initial capacity between the benchmark and sensor-embedded cells were observed due to manual fabrication processes, their electrochemical performance remained unaffected. Slight capacity fluctuations observed throughout the cycling process represent a reasonable phenomenon in experimental testing. After 400 charge/discharge cycles, the benchmark cell exhibited a capacity decay of 89 mAh with a capacity retention rate of 92.11%, whereas the sensor-embedded cell demonstrated a reduced decay of 30 mAh and a higher capacity retention rate of 97.26%. The overall capacity degradation followed a stable and gradual trajectory, maintaining reasonable capacity retention within acceptable ranges even after prolonged cycling tests. It is noteworthy that the sensor-embedded cell exhibited a slight capacity increase of 0.072% compared to its initial capacity at the 100th cycle. Following the 100th cycle, the capacity demonstrated gradual growth until the 153rd cycle, accumulating a 1.7 mAh increment (0.15% growth). This phenomenon might be attributed to the fixture clamping during testing, which effectively controlled external and intrinsic pressures affecting battery expansion, thereby enhancing the performance reproducibility of lithium-metal pouch cells [22]. Concurrently, the initial SEI formed during early cycles continued to evolve in composition and structure through subsequent cycling [39]. A suboptimal SEI might have developed during initial cycling stages, which stabilized after several cycles to improve capacity retention. Furthermore, studies indicate that a greater depth of discharge during early cycling phases could induce structural delamination in graphite layers, facilitating lithium-ion diffusion and consequently increasing capacity [40]. The enhanced capacity retention observed in mechanically constrained test cells, compared to unclamped benchmark units, substantiates the critical role of controlled external pressure in mitigating electrode stack expansion and self-generated internal stresses. This mechanical stabilization mechanism improves lithium-ion transport reproducibility, thereby optimizing capacity retention—a finding corroborated by prior studies on pressure-dependent electrochemical performance [22,25,41]. The coulombic efficiency of the sensor-embedded cell remained nearly identical to that of the benchmark cell, both maintained at approximately 99.9% (Figure 2d), indicating no significant side reactions induced by sensor integration. Figure 2e,f present the calculated incremental capacity (IC) curves for the benchmark and sensor-embedded cell at different cycling stages. During the initial 400 cycles, the primary IC peak intensity gradually declined in both cells, though the sensor-embedded cell exhibited less pronounced attenuation compared to the benchmark. This attenuated decline suggests minimal loss of active material (LAM) in the sensor-integrated cell during cycling, reflecting the positive effects of appropriate external pressure.

The impedance characteristics of LIBs exhibit significant dependence on both SOH and SOC, with impedance spectra encapsulating critical electrochemical information regarding internal degradation mechanisms [42]. The impedance curves at 100% and 0% SOC for the battery embedded with sensors at the 1st, 200th, and 400th cycles were extracted and compared, as shown in Figure 3b. It is evident that the semicircle diameter progressively increased with cycle number. Additionally, the equivalent circuit model corresponding to the impedance behavior is illustrated in Figure 3a. The equivalent circuit model (ECM) applied for impedance deconvolution comprises the following elements: L (inductance arising from current collector interactions), R₀ (ohmic resistance), a parallel R₁//CPE₁ network (representing high-frequency lithium-ion migration through the SEI), a parallel R₂//CPE₂ configuration (characterizing charge-transfer kinetics), and W (Warburg impedance associated with solid-state diffusion) [43]. The quantitative fitting results of EIS parameters (

Table 1. Fitting results for EIS resistance data.

	1st-0%	200th-0%	400th-0%	1st-100%	200th-100%	400th-100%
R0	0.06599	0.07236	0.07026	0.07180	0.06927	0.06225
R1	0.02555	0.04573	0.05130	0.02476	0.02972	0.04420
R2	0.16825	0.10364	0.10721	0.01537	0.03068	0.03673

) demonstrate robust agreement with experimental data, with all fitted parameters exhibiting errors below 10%. Notably, the ohmic resistance (R₀) at both 0% and 100% SOC remained stable across cycling, while moderate increases in SEI resistance (R₁) and charge-transfer resistance (R₂) at 100% SOC align with expected aging behavior. A marginally elevated R₂ at 0% SOC post-formation may tentatively correlate with sensor integration, though within acceptable statistical bounds. Crucially, these systematic validations confirm that the embedded thin-film sensor neither accelerates degradation nor introduces anomalous impedance behavior during the cycling test, thereby substantiating its compatibility with long-term battery operation.

3.2. Distributed Pressure Change Monitoring Inside the Battery

The exceptional cycling stability of the battery demonstrates that the embedded thin-film sensor enables in situ pressure monitoring without perturbing the electrochemical integrity of the pouch cell, thereby validating the feasibility of this internal sensing strategy for onboard battery management applications. By extracting and summing pressure data from nine measurement units of the thin-film pressure sensor during battery charge/discharge cycles, it was observed that the total internal pressure measured by the sensor closely followed variations in battery voltage, with both parameters exhibiting highly similar trends (Figure 4a). This aligns with findings reported in prior studies [29,44], indirectly reflecting the reliability of measurements obtained from the embedded thin-film sensor.

The cyclic pressure variations in LIBs originate primarily from reversible electrode swelling driven by lithium-ion intercalation/deintercalation dynamics. During charging, lithium-ion extraction from the cathode (e.g., layered oxides) and subsequent insertion into the anode (typically graphite) induce periodic lattice expansion at the electrode level—graphite interlayer spacing increases by ~10% upon lithiation, while cathode materials exhibit comparatively smaller but cumulative volumetric changes that may induce structural fatigue and microcracking over the cycling test. The pressure measurement segments collected throughout the battery cycling process are presented in Figure 4b. It can be observed that the incremental pressure and its maximum value exhibit an increasing trend with cycling progression. This upward pressure trend is attributed to irreversible internal battery expansion, which correlates with degradation mechanisms such as lithium plating, gas generation from electrolyte decomposition, and the formation/growth of the SEI.

The implanted sensors demonstrated reliable and stable performance, as previously mentioned. Figure 5 presents representative pressure data from the entire battery cycling test, reflecting the evolutionary patterns of pressure. The contour map illustrates the pressure distribution at peak total pressure instances during cycling, while the two-dimensional pressure distribution plot, constructed based on the nine measurement units of the thin-film pressure sensor, shows the third row corresponding to the tab-adjacent region. Throughout the cycling test process, the internal total pressure exhibited a continuous increase with cycle progression. During the 400th cycle, the peak pressure reached 69.0 N, representing a 24.7 N increase (56.7% growth) compared to the post-formation peak of 44.3 N. Notably, the minimum total pressure at the 400th cycle (47.9 N) already exceeded the post-formation peak value, indicating significant irreversible battery expansion.

Comparative analysis of sectional pressures at peak instances across five cycles revealed a hierarchical distribution: maximum pressure in the central region, followed by the tab-adjacent area, with minimal pressure in distal regions. This heterogeneity is primarily attributed to multiple coupled mechanisms: (1) During charging, simultaneous electrode expansion in the central region creates stress superposition and pressure concentration, exacerbated by inferior heat dissipation and consequent thermal expansion compared to peripheral areas. (2) Longer current paths and higher electrical resistance in the central region elevate local Joule heating, further amplifying pressure. (3) The stress concentration near tabs originates from geometric discontinuities at electrode–tab junctions and intensified current density during operation, which enhances ohmic heating and material expansion in these localized areas. These findings collectively highlight the pronounced spatial heterogeneity and cycling-dependent escalation of internal pressures. The observed pressure evolution patterns hold critical implications for LIB safety management, particularly regarding mechanical degradation pathways. Therefore, the evolution and distribution of internal pressure play a critical role in LIB safety concerns, and the strategy developed in this study—integrating thin-film pressure sensors into batteries via IEF—successfully addresses this challenge. The embedded pressure sensing proves particularly advantageous for modular battery assembly, especially in battery packs. Each cell within the pack contains buffer materials between adjacent units, where external sensors would disrupt the spatial distribution configuration. This integrated pressure sensing technology effectively resolves this challenging layout issue while maintaining structural integrity.

Based on the periodic pressure variation characteristics of batteries, this study selected three key parameters—initial pressure, peak pressure increment, and average pressure rise rate per cycle—to characterize mechanical stress evolution. During a single cycle, the stabilized pressure after discharge rest was defined as the initial pressure for the subsequent cycle. The pressure increment was calculated as the difference between the cycle’s peak total pressure and its initial pressure, while the average pressure rise rate was derived by dividing the peak increment by the time required to reach it. As shown in Figure 6a, these parameters exhibited nonlinear correlations with cycling progression. At the beginning of the cycle, the initial pressure of the cell did not rise significantly, fluctuating around 30 N, which indicates that the growth of irreversible expansion of the cell was not obvious. This may be attributed to the incomplete formation of the SEI layer in the battery during initial cycling, leading to an instability in the electrode–electrolyte interfacial reaction. Repeated rupture and remodeling of the SEI may cause local volume changes and thus initial pressure fluctuations [45]. At the same time, after applying a suitable external pressure, the contact between lithium, electrolyte, and SEI within the negative electrode is closer during the early cycle, which mitigates the process of early irreversible swelling [46]. Beyond 200 cycles, the initial pressure increased markedly at an average rate of 0.81 N per 10 cycles. The gradual development of porous lithium morphologies and loose internal structures in batteries after 200 cycles contributes to enhanced irreversible expansion [46]. Both the peak pressure increment and its average growth rate demonstrated an ascending trend in the first 200 cycles, followed by a decline and stabilization thereafter, displaying parallel evolutionary patterns.

An analysis of the pressure increment distribution at peak instances (Figure 6b) revealed universal increases across all regions, with the central area maintaining a consistent increment of ~4 N, exceeding peripheral zones. The tab-adjacent region exhibited marginally higher increments than the non-tab side, aligning with the pressure distribution characteristics. A comparative analysis with the pressure distribution in Figure 5 reveals that the total pressure near battery tabs exceeds that in inter-tab regions, while the pressure increase magnitude in these areas is smaller than in regions distal to the tabs. This observation indicates that tabs significantly influence battery pressure distribution, potentially inducing stress concentration. These findings indicate that pressure increment distribution mirrors the spatial heterogeneity of pressure distribution throughout cycling, providing novel insights for battery design and SOH model development. The implementation of internal pressure monitoring in batteries facilitates experimental correlation studies between pressure and temperature distributions, thereby assisting in identifying thermal runaway critical conditions and designing more effective thermal management strategies. This approach demonstrates both scientific significance and engineering value for advancing battery safety technologies.

4. Conclusions

The proposed strategy integrating thin-film pressure sensors into LIBs via an IEF enables distributed internal pressure monitoring throughout the cycling test. After 400 cycles at a 1 C rate, the battery exhibited a capacity retention rate of 97.26%, marginally exceeding that of the benchmark cell. EIS tests revealed a negligible increase in ohmic resistance (R₀) at both 0% and 100% SOC during cycling, while moderate growth in SEI film resistance (R₁) and charge transfer resistance (R₂) at 100% SOC aligned with typical aging behavior, confirming minimal electrochemical interference from the sensor integration. This validates the non-destructive implementation of the monitoring system in pouch cells. Furthermore, sensor-derived pressure evolution analysis demonstrated cumulative increases in both reversible and irreversible internal pressures. By the 400th cycle, the peak pressure reached 69.0 N, representing a 56.7% increase (24.7 N) from the initial 44.3 N post-formation. Spatial analysis of internal pressure distribution reveals pronounced heterogeneity, with the central battery region exhibiting higher pressure magnitudes than tab-proximal zones, while non-tab areas demonstrate minimal pressure values. Significant pressure fluctuations and marked spatial inconsistencies in pressure evolution were observed throughout the electrochemical cycling process.

The integration of sensor-acquired monitoring data with a BMS enables real-time transmission via demodulators for analytical processing. BMSs can leverage pressure data as adaptive decision-making criteria to optimize thermal management, adjust charging protocols, and issue thermal runaway warnings. Concurrently, pressure data acquisition enhances BMS capabilities in SOH estimation. When computational limitations arise in a BMS, battery data can be uploaded to cloud servers to utilize advanced computational resources for precise algorithmic analysis of current, voltage, and pressure parameters, thereby safeguarding battery health and safety. The interconnectivity between embedded sensors and a BMS, along with practical methodologies for pressure data utilization, warrants further investigation. In large-scale power systems such as off-grid or hybrid energy configurations, lithium-ion batteries face heightened demands for long-term stable operation under complex working conditions [47]. Advanced internal state monitoring provides sensitive and intuitive feedback on battery operational status, facilitating early detection of latent risks during prolonged service and enhancing management efficacy—critical factors for ensuring reliability and autonomy in such systems. Distributed internal pressure monitoring thus serves as a pivotal data source for battery safety assurance, lifespan management, and performance optimization. These applications contribute to advancing battery technologies toward safer and more efficient paradigms.