Detection and Analysis of Abnormal High-Current Discharge of Cylindrical Lithium-Ion Battery Based on Acoustic Characteristics Research (2024)

1.1. Current Research Status

In recent years, some scholars have applied acoustic detection methods to batteries due to the characteristics of less damage, convenient use, strong adaptability, and high detection. Common acoustic detection techniques include ultrasonic testing (UT) and AE research. UT is renowned for its proficiency in non-destructive testing (NDT), utilizing ultrasonic waves to inspect internal defects in diverse fields such as materials science, medicine, and structural engineering [6,7]. In the realm of battery technology, Sood et al. [8] pioneered the adaptation of UT for LIBs, demonstrating its effectiveness in identifying structural changes that reflect the battery’s health status. Their research indicated that UT could detect variances within the battery structure, thereby offering a method to monitor the health and integrity of batteries in a non-invasive manner.

Acoustic measurement is an innovative method for assessing electric currents that is particularly effective in detecting arcing faults within EV batteries, and it offers distinct advantages over traditional methods like Hall Effect sensors, shunt resistors, and Rogowski coils. Unlike Hall Effect sensors, which detect magnetic fields and can be susceptible to magnetic interference, acoustic sensors detect sound waves generated by electrical discharges, offering a unique advantage in environments with high electromagnetic noise [9]. Shunt resistors, while cost-effective and direct in measuring current through voltage drop, inherently generate heat and can suffer from power loss; these are issues not encountered with acoustic methods, which are non-contact and non-invasive [10]. Rogowski coils, suitable for high-frequency AC currents, require complex integration and calibration to accurately measure current, whereas acoustic sensors can directly identify rapid changes in current associated with fault conditions without the need for electrical contact or complex circuit integration [11]. However, acoustic measurement’s effectiveness can be limited by ambient noise and requires sophisticated signal processing algorithms to distinguish relevant acoustic signals from background noise, which is less of a concern with more direct electrical measurement techniques. Moreover, the rapid response of acoustic methods to high-current scenarios provides crucial advantages in operational settings, enabling real-time monitoring and immediate detection of structural changes within batteries, a capability that is vital for the safety and efficiency of electric vehicle operations.

The advancement of battery technology for electric vehicles and energy storage systems has necessitated the development of sophisticated diagnostic methods to ensure safety and efficiency. Among these, acoustic detection methods, such as UT and AE, have emerged as promising non-invasive techniques due to their minimal damage risk, convenience, adaptability, and high detection capabilities.

Expanding on this foundation, subsequent studies have focused on the specificity of UT in relation to the battery’s state of charge (SOC). Hsieh et al. [12] applied UT to various commercial batteries, observing changes in the time of flight (TOF) and sound intensity as the SOC increased. These acoustic parameters have proven to be reliable indicators of battery status, enhancing the understanding of how acoustic properties correlate with battery chemistry and charge levels. Moreover, Chang et al. [13] advanced UT application by introducing a non-contact, air-coupled method that established a linear relationship between signal amplitude and SOC, significantly improving the accuracy of SOC predictions. These innovations underscore the versatility of UT in battery diagnostics and its potential to evolve into a standard tool for battery management systems.

Studies by Sun have examined the use of ultrasonic nondestructive diagnosis at multiple frequencies to identify the optimal conditions for assessing the health of lithium-ion batteries, indicating significant progress in understanding the complex interactions within battery cells [14,15]. Gold et al. have further developed the application of ultrasonic transmission to probe lithium-ion batteries’ state of charge, offering a novel approach that enhances the precision of battery monitoring systems [16,17]. Additionally, Wu et al. focused on the health monitoring of lithium-ion batteries using ultrasonic sensing techniques, emphasizing the need for improved access to the battery cells for effective diagnostics [18,19]. These advancements suggest that ultrasonic testing could play a critical role in future battery management systems, particularly in ensuring the longevity and reliability of batteries in high-demand applications such as electric vehicles.

AE detection distinguishes itself by capturing stress waves generated from structural or compositional changes within the battery without the need for external stimulation. This technique has been instrumental in identifying the health status of lithium-ion batteries, where the amplitude and pattern of AE signals correlate with battery aging and degradation processes. For instance, Zhang et al. [20] demonstrated that continuous and pulsed AE signals could effectively characterize lithium-ion battery health, with signal amplitude (SA) variations indicating the degree of degradation. Further investigations into AE have focused on detecting the formation and evolution of the solid electrolyte interphase (SEI) [21,22,23], as well as changes in electrode morphology due to electrochemical processes [24,25]. These studies illustrate AE’s capability to monitor intricate battery behaviors, offering insights into failure mechanisms and the effects of aging [26,27]. Specifically, in short-circuit detection, acoustic measurements can characterize cell anomalies across various SOCs, complementing conventional electrode assessments [28,29]. Additionally, for aging-related failures, these methods offer a swift and reliable means to detect defect accumulation, outperforming standard inspection techniques [16,30].

Acoustic detection technologies present innovative solutions to the limitations of current BMS by offering additional non-invasive means of monitoring lithium-ion batteries’ internal states. These methods complement traditional voltage, current, and temperature measurements, promising to enhance the accuracy and robustness of BMS through the provision of detailed acoustic data on battery operation and health. Despite their potential, the deployment of UT and AE faces challenges, including high costs and the need for adaptation to various production environments. Future research is directed towards developing cost-effective sensor technologies and refining algorithms for data analysis to overcome these barriers. Ultimately, the continued evolution of acoustic diagnostic methods is expected to significantly contribute to the development of safer and more reliable battery systems, marking a crucial advancement in automotive battery diagnostics.

Ultrasonic testing offers unique insights into the internal health and structural integrity of batteries, which is essential for enhancing safety and performance in applications like electric vehicles. For cylindrical cells, ultrasonic testing helps in evaluating the mechanical and structural properties during the operational stresses and aging processes [31]. Prismatic cells benefit from detailed structural assessments that can detect deformations and failures, which are crucial for maintaining the operational reliability of battery packs in automotive applications [32]. Pouch cells, which are susceptible to swelling and mechanical stresses, are monitored using ultrasonic techniques to ensure the stability of the electrode structures and to prevent catastrophic failures [33]. Furthermore, ongoing research continues to focus on enhancing the precision of ultrasonic diagnostics to better understand the degradation mechanisms and to improve the predictive maintenance strategies for lithium-ion batteries [34].

AE techniques are finding increasingly practical applications across various battery types, including cylindrical, prismatic, and pouch configurations. For cylindrical batteries, AE has been used to analyze the acoustic characteristics under conditions such as external short circuits, providing insights into potential battery safety and health issues [30]. In studies on prismatic batteries, AE monitoring has proven effective in exploring mechanical properties and failure mechanisms, which are crucial for predicting battery life and preventing catastrophic failures [7]. For pouch cells, AE is instrumental in cyclic aging monitoring, where the emissions are linked with the battery’s declining capacity, offering a non-destructive tool for assessing battery health over its operational lifespan [35]. These studies demonstrate the broad application potential of acoustic detection technologies across different battery configurations, especially highlighting their unique advantages in battery health monitoring and fault diagnosis.

1.2. Motivation and Original Contribution

The operation of electric vehicle (EV) batteries under varying road conditions necessitates their frequent engagement in high-current charging and discharging activities. Such conditions impose significant stress on the batteries, leading to degradation of performance, capacity, and lifespan, thereby increasing the likelihood of thermal runaway events. The motivation for this study arises from the critical need to enhance battery monitoring capabilities, particularly under high-current discharge scenarios that traditional electrode voltage and current measurements fail to accurately capture. Current literature primarily focuses on conventional diagnostic methods, leaving a significant gap in the understanding and application of AE for battery state detection under stress conditions. This research seeks to address this gap by exploring the utilization of AE to detect subtle changes within the battery’s internal structure that traditional methods might overlook. Theoretical considerations suggest that the microstructural alterations in the anode and cathode during high-current discharges, which potentially compromise the battery’s charge-discharge cycle performance and safety, can be effectively captured through acoustic signals.

The introduction of a novel methodological framework for the collection, analysis, and interpretation of AE data aims to establish a more reliable mapping between acoustic signals and the battery’s discharge state. Through controlled experiments conducted under both low (0.5 C) and high (3 C) discharge currents, the study identifies AE acoustic characteristic parameters that accurately reflect the battery’s condition. Such methodological advancements offer a new avenue for real-time battery monitoring, presenting a significant contribution to the field. The practical implications of these findings are far-reaching. Integrating AE-based diagnostics into BMS can potentially achieve more sophisticated, real-time monitoring of battery health. This integration promises not only to enhance vehicle safety and battery reliability but also to extend the battery’s operational lifespan.

Furthermore, the study sets the stage for future investigations into additional acoustic parameters, their applicability to different battery types and operational conditions, and the synergistic integration of AE with other diagnostic technologies. Ultimately, the research provides a foundational step towards the development of more advanced, acoustic-based diagnostics for EV batteries, addressing the urgent need for improved safety and performance monitoring in the rapidly evolving electric vehicle sector.

1.3. Configuration of This Paper

The structure of this paper is organized as follows: Section 2 describes the experimental setup; Section 3 introduces the primary methods employed in this study; Section 4 presents the results and analysis of the acoustic response of batteries subjected to different discharge currents, along with pertinent discussions. The paper concludes with a summary of the findings in the Section 5. Additionally, a Nomenclature section is included to define all term abbreviations used throughout the study.

4.1. Analysis of Acoustic Signal Characteristics in the Time Domain

The analysis of acoustic signal characteristics under different discharge currents reveals critical insights into battery behavior, particularly highlighting the acoustic diagnostics’ sensitivity to discharge rates and their potential in monitoring battery health.

Observing the acoustic signal fluctuations in Figure 4, it is noted that under a 3 C discharge, there are more pronounced variations compared to a 0.5 C discharge. This distinction not only validates the acoustic method’s ability to detect structural changes within the battery but also emphasizes its capacity to differentiate between operational states based on the discharge intensity. A key finding from the time domain analysis is the discernible delayed response of the acoustic signal under both 3 C and 0.5 C discharge conditions. This delay, established through statistical analysis, demonstrates a significant correlation with discharge rates, illustrating how the acoustic signal’s behavior is influenced by current density variations. Despite slight differences within the same discharge current cycle, the overall delay length suggests a level of dependency on the discharge current, reinforcing the idea that acoustic diagnostics can provide meaningful insights into the battery’s structural rearrangement during discharge.

From the overall observation in Figure 5, the correlation analysis between current and AE under 3 C discharge conditions reveals distinct acoustic signal behaviors across different discharge cycles. In the continuous 0.5 C charge to 3 C discharge cycle, for the three replay segments of cycle 1, cycle 2, and cycle 3, a short time delay Δt1 is observed before the acoustic signal response intensifies post-discharge initiation. Interestingly, the rise in acoustic signal intensity is not uniform across cycles. In the 3 C discharge segment, cycles 1 and 3 exhibit an increase in acoustic signal intensity, while cycle 2 shows a noticeable drop, suggesting complex internal battery structure responses to high-current discharge influenced by factors such as the battery’s state of health and internal structural differences at the cycle’s start.

When the current changes, this study selects the variation range of the battery acoustic amplitude signal (ΔAE) as the characteristic parameter, which better reflects the acoustic response of the structure while considering the different directions of the acoustic signal variation. The calculated ΔAE changes of cycle 1, cycle 2, and cycle 3 are 0.72, 0.45, and 0.69, respectively, with a fluctuation range of about 14~20% of the respective acoustic signals. This range demonstrates the sensitivity of acoustic diagnostics to internal structural changes during high-current discharge, showing a clear degree of recognition and correlation with current changes.

In terms of the acoustic response delay compared to the start of current change, i.e., the change in Δt1, the three cycle segments all exhibit faster acoustic response signals after 3 C discharge begins, indicating a significant structural change. The robust acoustic signal is swiftly transmitted to the battery surface, resulting in a relatively short Δt1 value. The calculated parameter changes for Δt1 of cycle 1, cycle 2, and cycle 3 are 5.8 s, 7 s, and 3.3 s, respectively. After the discharge concludes, large-scale structural changes inside the battery cease, but minor structural repairs and rearrangements continue, contributing to ongoing acoustic feedback. The Δt2 parameters for cycle 1, cycle 2, and cycle 3 are calculated to be 53 s, 71 s, and 47 s, respectively.

A definitive correlation exists between current changes and acoustic variations across different discharge cycles, underscoring the potential of acoustic diagnostics as a critical tool for future battery anomaly state monitoring. The parameters such as ΔAE, Δt1, and Δt2 not only reflect the immediate response to electrical inputs but also demonstrate a consistent relationship with the magnitude and timing of current fluctuations, emphasizing the applicability of acoustic diagnostics in real-time battery monitoring and anomaly detection.

The analysis of acoustic signal characteristics during the 0.5 C discharge process, as depicted in Figure 6, provides insights into the correlation between current discharge patterns and acoustic emissions. The methodology for correlating current and AE signals involves both temporal analysis and statistical correlation techniques, offering a robust framework for identifying significant patterns in acoustic signal behavior in relation to current discharge activities. This examination elucidates the extended observation window for acoustic signals due to the longer duration of the 0.5 C discharge process, which incorporates two phases of equivalent 0.5 C charge and discharge, thereby introducing interference in observing acoustic discharge characteristics.

In cycle 1, Figure 6a, the resting period with zero current between charge and discharge cycles marks a distinct point of analysis. The signal change trend exhibits a clear upward trend after a period (Δt1) from the start of the 0.5 C discharge, indicating more pronounced acoustic signal fluctuations; this upward trend ceases after a period (Δt2) as the current returns to zero, underscoring the acoustic signal’s delayed response to the discharge process. The range of amplitude variation during this entire process, denoted as ΔAE, is significant, enhancing the discriminability of acoustic signals during different operational states.

In cycle 2, Figure 6b, the acoustic characteristics reflected by the charge and discharge at 0.5 C show slight differences, with minimal amplitude change during the discharge process, highlighting the challenges in distinguishing between charge and discharge phases based solely on acoustic data under low current conditions. This observation underscores the need for advanced analytical techniques to enhance signal discrimination and accuracy in battery diagnostics.

In Figure 6c, cycle 3 reveals an evident rise in the acoustic signal following the 0.5 C discharge, with the rising peak after the discharge end serving as a marker for the acoustic response to the discharge process. This phase demonstrates a significant amplitude change, illustrating the acoustic signal’s sensitivity to discharge activities and its potential as a diagnostic tool for battery health monitoring.

In this study, error bars and box plots are utilized in Figure 7 to visually represent the variability and central tendencies of acoustic emission data under different discharge conditions. Error bars were computed using confidence intervals based on the standard error of the mean for each dataset, reflecting the precision and reliability of the measurements under varied operational stresses. Similarly, box plots were constructed to display the range of data distribution and central tendency, providing a comprehensive overview of the acoustic response variability under high and low discharge conditions. This approach not only highlights the dynamic and sometimes unpredictable behavior of the battery under stress but also ensures that our analysis adheres to rigorous statistical standards, offering clear insights into the operational safety and performance of the batteries. This combined method helps illustrate the extent of variation in parameters such as ΔAE, Δt1, and Δt2, reflecting the differing structural responses and stress levels of the battery at various discharge rates and offering a more comprehensive understanding of battery behavior under different operational conditions.

In Figure 7a, error bars for ΔAE at 3 C discharge show significant variations, indicating a wide range of acoustic responses and suggesting varying stress levels at each cycle due to rapid load changes. Longer error bars imply inconsistent behaviors, reflecting dynamic responses to high operational stress. Conversely, at 0.5 C discharge, error bars are shorter, suggesting more consistent acoustic emissions and stable structural integrity under lower stress, leading to uniform responses. The box plot for 3 C might display a higher median and broader interquartile range, highlighting greater fluctuations in acoustic emissions and possibly impacting battery longevity and safety due to high stress. In contrast, the box plot at 0.5 C shows a lower median and narrower range, indicating less variability and stress, promoting controlled battery behavior under these conditions.

In Figure 7b, error bars at 3 C discharge for Δt1 are notably short, suggesting a rapid response to high discharge demands and consistent, quick reactions across cycles, underlining the effectiveness of acoustic diagnostics in capturing fast structural responses under stress. At 0.5 C discharge, Δt1 values are higher and show greater inconsistency, as reflected by longer and more varied error bars, highlighting slower and more variable responses under lower stress conditions. The box plot for 3 C would likely show a tightly grouped distribution, indicating uniformity in rapid responses, while the box plot for 0.5 C would display a wider spread, reflecting inconsistent response times and less efficient structural reactions at lower discharge rates.

In Figure 7c, error bars for Δt2 at 3 C discharge indicate a moderate duration of acoustic signals, showing some variability but not excessively, suggesting that while the battery structure undergoes stress, it does not persist too long. At 0.5 C discharge, shorter Δt2 values with smaller error bars indicate a quicker cessation of acoustic signals, implying faster recovery or less impactful structural changes. The box plot for 3 C discharge might show moderate variability in signal duration, with outliers possibly indicating prolonged stress episodes. Conversely, a tighter box plot at 0.5 C would reflect more consistent and shorter durations, aligning with quicker recoveries and less severe operational stresses.

The results reveal a heightened sensitivity of acoustic diagnostics to high discharge rates. Signals at 3 C discharge exhibited more pronounced fluctuations and variations than those at 0.5 C, demonstrating the method’s ability to detect and quantify the stress and structural changes occurring within the battery. This makes acoustic diagnostics a non-invasive, crucial tool for assessing battery health and ensuring operational safety. Furthermore, the analysis shows that acoustic signals under high discharge conditions have a delayed response to discharge activities, which highlights the method’s capability of capturing the nuances of stress relaxation and microstructural rearrangements within the battery. This delay, more noticeable under high discharge conditions, provides insights beyond what traditional electrical measurement techniques can offer. Additionally, the acoustic signal amplitude (ΔAE) under 3 C discharge conditions showed, on average, 1.35 times greater variation compared to 0.5 C, underscoring the method’s effectiveness in monitoring structural changes. The duration of these signals (Δt2) was also, on average, 1.3 times longer during 3 C discharges, indicating more substantial internal structural changes due to the higher current impact. These findings collectively emphasize the diagnostic precision of acoustic methods, proving special value for non-invasive assessments and advancing battery management systems.

4.2. Analysis of Acoustic Signal Characteristics in the Frequency Domain

The frequency domain analysis offers a nuanced perspective on the battery’s acoustic characteristics under varying discharge currents, complementing the insights gained from the time-domain analysis. This analysis delves into the structural dynamics and the effects of operational stresses on the battery, as evidenced by acoustic emissions.

For the 0.5 C discharge, observed in Figure 8, the notable amplitude of the center frequency underscores the acoustic signal’s stability and the mild impact of lower discharge rates on the battery’s structure. This clear and discernible frequency domain characteristic suggests that the structural changes are minimal, with a consistent impact from the discharge process, making it a reliable indicator of the battery’s operational health.

Conversely, the 3 C discharge cycles depicted in Figure 9 show a significant reduction in the center frequency’s amplitude. This indicates a substantial impact of high-current discharge on the battery structure despite the shorter duration of the discharge process. The emergence of multi-peak or peak-shift phenomena suggests complex structural dynamics at play, with the battery exhibiting varied responses to the intense discharge conditions. These findings highlight the acoustic signal’s sensitivity to discharge intensity and its diagnostic potential in identifying structural impacts and changes within the battery.

By comparing the acoustic responses to 0.5 C and 3 C discharge currents through frequency domain analysis, several key insights are uncovered that underscore the pivotal role of this approach in acoustic diagnostics. First and foremost, the sensitivity of acoustic diagnostics to discharge rates is evident, as lower currents like 0.5 C discharge lead to higher amplitude values of the center frequency, indicating a more focused and consistent acoustic signal response. This contrasts sharply with the 3 C discharge, where significant structural impacts manifest through decreased amplitude and the emergence of multi-peak phenomena, pointing to the differential effects of discharge rates on battery structure. Moreover, the diagnostic capabilities of frequency domain analysis become apparent, offering clear and pronounced amplitude information for low-current discharges, which aids in evaluating long-term battery operation status. In contrast, the distinct multi-peak characteristics observed during high-current discharges act as crucial indicators for assessing immediate structural impacts. Integrating insights from both time and frequency domain analyses facilitates a comprehensive understanding of battery behavior under varying discharge conditions. This holistic approach not only bolsters our capacity to monitor and diagnose battery health with precision but also highlights the indispensable value of acoustic diagnostics as a key tool for advancing battery technology and ensuring operational safety and reliability.