Short-circuit Resistance Research Articles

In Situ Measurement of Dynamic Internal Short Circuit Resistance during Nail Penetration of Lithium-Ion Cells and its Implications on Cell Robustness and Abuse Tolerance

Here we report a method for in situ measurement of internal short circuit (ISC) resistance during nail penetration testing of lithium-ion cells. The method is demonstrated with dry cells, wet dummy cells, and working cells using a small nail and slow penetration speed. ISC current and ISC temperature are also measured during the tests. It is confirmed that the ISC resistance changes dramatically, by several orders of magnitude, during nail penetration. More importantly, it is found that the stable resistance after full penetration is much higher than the lowest dynamic resistance at earlier stages of nail penetration. Analysis based on such a stable ISC resistance would underestimate the risk of thermal runaway during nail penetration tests. It is also found that ISC in some cases may be mitigated due to melting or rupture of aluminum foil surrounding the nail, implying a mechanism that may be able to be used towards the design of more robust/abuse tolerant Li-ion cells. Lastly, it is found that nail penetration using a larger nail reduces ISC resistance during penetration of cells but the general behaviors of ISC resistance are similar to those during smaller nail penetration.

Parametric excitation analysis for system performance of piezoelectric energy harvesters

Capitalizing on the advantages of parametric excitation and real-life low frequency vibration, the steady-state response of piezoelectric energy harvesting system composing of a cantilever beam with a tip mass and horizontal excitation at the fixed end is investigated. By applying Hamilton's principle, the nonlinear partial differential equation of a cantilever bimorph piezoelectric energy harvesting system with an additional tip mass is derived and analyzed. The cantilever is modeled as an axially non-elongated Euler Bernoulli beam with geometric and damping nonlinearities. By using the Galerkin method, the nonlinear partial differential equation is reduced to an electromechanical coupling system that governs a cantilever piezoelectric energy harvesting system with a tip mass under parametric excitation. The first-order resonance response of this harvesting system is studied by using the method of multiple scales. The analytical response expressions and first-order amplitude functions for vertical displacement, output voltage and output power are derived and analyzed. The influence of different impedance and tip mass on the piezoelectric energy harvesting system performance is summarized and concluded. For the parametric excitation system, the load resistance shows significant influence on the initial threshold of the piezoelectric energy harvesters under parametric excitation. For a short circuit, the initial threshold increases with increasing load resistance. For an open circuit, the initial threshold decreases with increasing load resistance. For an open circuit resistance or a short circuit resistance under parametric excitation, an increase in the end block mass effectively reduces the resonance frequency of the piezoelectric energy harvesters. Consequently, the energy harvesting bandwidth is increased, and the harvesting effect of the piezoelectric energy harvesters is enhanced. In this paper, a more accurate and practical theoretical model is established to predict the electromechanical coupling behavior of cantilever piezoelectric energy harvester under parametric excitation. Using this approach, it is possible to complement parametric and direct excitation such that the advantages of parametric excitation can be achieved. It is also significant to improve the energy conversion efficiency of energy harvesting system.

A quantitative method for early-stage detection of the internal-short-circuit in Lithium-ion battery pack under float-charging conditions

Detecting the internal short circuit (ISC) of Lithium-ion batteries is critically important for preventing thermal runaway. Conventional approaches mainly focus on ISC detection for dynamic load profiles, while the commonly seen float-charging scenarios with a high risk of ISC are rarely considered. Technical challenges arise from not only the lack of models describing battery dynamics at fully charged conditions but also the computational burden caused by the high number of series-connected cells in a battery pack. To address these issues, we here propose a simple and accurate method to quantitatively identify the leakage current of the battery with ISC, by checking the behaviors of the battery equalization system. Battery-in-the-loop experiments are carried out to verify the proposed method under a wide range of the short circuit resistances (∼20 to ∼500Ω), using both LiFePO4 and Li(NiCoMn)0.33O2 batteries with different aging degrees. The typical errors of the identified leakage currents can be well-bounded within ±1 mA, including the cases of using both passive and active balancing. The proposed method is completely model-free and, therefore, can be transplanted into low-cost embedded systems to support broader applications.

Fault detection method for VSC-HVDC System with new energy sources considering fault current limiter

Voltage source converter based high voltage direct current transmission (VSC-HVDC) is widely adopted in the power system with new energy sources, but with the problem of fast rise of fault current. Magnetic saturation fault current limiter (MSFCL) suppresses the rise of short-circuit current by increasing the inductance value, which has the advantages of high speed and no additional control. However, the change of inductance will affect the detection of VSC-HVDC system fault, may leading to missed or incorrect removal of the fault and causing harm. Based on this problem, this paper proposes a fault detection method used in VSC-HVDC system considering MSFCL, which has fast detection speed, high sensitivity and reliability. The proposed method is robust to short-circuit resistance variation and can locate faults external and internal. Firstly, we establish the short-circuit equivalent circuit of VSC-HVDC system. Then we establish the differential equation and solve it, to obtain the analytical expression of voltage across MSFCL. Furthermore, we proposed a short circuit fault detection method based on the rise rate of the voltage across MSFCL. Finally we establish a four port VSC-HVDC system simulation model to prove that the method is effective. The results show that compared with traditional methods, the proposed method has faster detection speed. The proposed method is independent of the power flow direction, can identify the short circuit fault with large short-circuit resistance of 500 Ω and can discriminate the faulty branch from the healthy ones accurately.

Soft Short-circuit Fault Diagnosis of the Lithium-ion Battery Pack Based on an Improved EKF Algorithm

Background: Lithium-ion batteries are widely used in new energy vehicles and energy storage systems due to their superior performance. However, lithium batteries are prone to safety problems in the use process, so the fault diagnosis technology of lithium batteries has attracted more attention. Objective: This study aimed to ensure the safety of lithium batteries and accurately and timely diagnose the soft short circuit (soft SC) fault of lithium battery. Methods: Aiming at the energy storage lithium battery pack, this study proposed a soft short-circuit fault diagnosis method for the lithium-ion battery pack based on the improved Extended Kalman Filter (EKF) algorithm. First, the 1st-order RC equivalent circuit model of normal battery and soft SC fault battery was established, and model parameters were identified using Recursive Least Squares with Forgetting Factor (FFRLS). Then, using the improved EKF, the state of charge (SOC) of a single cell was estimated, and the difference between the calculated SOC and the estimated SOC by the coulomb counting method was used to detect soft SC faults and compared them with the reference data. Finally, the SC resistance value indicated the severity of the fault. Results: The proposed method could accurately diagnose the soft short circuit fault, and the error was found to be lower than the traditional EKF algorithm. The estimation error was about 0.4% for the battery with slight failure and about 1.5% for the battery with serious failure. Conclusion: The experimental results showed that the improved EKF algorithm could estimate the SOC difference more accurately, and the effect of soft SC fault diagnosis was better. At the same time, it could quantitatively identify the size of the short circuit resistance, which is very helpful for the subsequent management of the battery system.

An Early Soft Internal Short-Circuit Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft internal short-circuit faults in lithium-ion battery packs is critical to ensuring the safe and reliable operation of electric vehicles. This article proposes a fault diagnosis method that can achieve the detection and assessment of soft internal short-circuit faults for lithium-ion battery packs. Specifically, based on the incremental capacity curve, fault features are extracted from the data, making them easier to identify than small voltage differences. Then, the local outlier factor method is proposed to detect the early soft internal short-circuit fault by calculating the local outlier factor value of each cell within the battery pack. Furthermore, soft short-circuit simulations of a series-connected battery pack under different conditions and various short-circuit resistance values are conducted to generate an internal short-circuit fault data set. Finally, the validity of the proposed fault diagnosis method is verified using simulation and real-world vehicle data. The results show that the proposed method can effectively identify the short-circuit fault of the battery at the early stage, accurately locate the faulty cells in the battery pack, and describe the severity of the fault.

An analysis of errors in determining contact voltage due to the ignorance of phase-to-ground fault resistance

One of the ways to assess the danger of electric shock in electrical distribution networks is the analytical determination of the touch voltage. In the emergency mode in these networks (phase-to-ground fault), the value of contact voltage depends on phase-to-ground fault resistance. Taking into account that the short circuit of the overhead line wire forming the distribution network can be on vegetation, snow, ice, melt water, etc., taking the short circuit resistance to zero will cause significant errors. The article describes possible errors in the calculation of contact voltage in the distribution networks with voltages up to 1000 V in different neutral modes in case of the phase-to ground fault.

Fault data generation of lithium ion batteries based on digital twin: A case for internal short circuit

Battery accidents are emerging for various kinds of applications, such as commercial electronics, electric scooters, electric vehicle and energy storage stations. Effective early warning algorithm is essential for mitigating the damage caused by battery failure. An early warning algorithm needs to be trained by failure data. However, building battery failure data (such as internal short circuit) by experiment consumes so much time and cost that it cannot match the development requirement of new product. This work demonstrates the validity of using digital twin technique to generate battery failure data to train the online early warning algorithm. The digital twin is formed by a highly reliable thermal-electrical coupled battery model. The influential factors, e.g., load current, short circuit position, equivalent short resistance and environment temperature, which determine the performance of the early warning algorithm were discussed. The warning threshold that may judge the fault warning rate and response speed of the algorithm was also analyzed. The digital twin helps us find that there is a temperature difference between the sensor location and the failure (short circuit) point. The temperature difference enlarges under the cases that the failure point is far from the measurement point, thereby hindering the timely detection and alarm for fault. The temperature gap can be as high as 261.2 °C under the extreme case of 0.1 Ω short circuit resistance. Therefore, the mismatch of the failure point and the measurement will give us the illusion that the failure occurs suddenly, or the so-called sudden death. The thermal runaway boundary for battery module varies with the ambient temperature and the short resistance, helping the determination of the threshold of the detection algorithm for thermal runaway. At last, a guidance on how to develop fault diagnosis algorithm with the help of digital twin to judgement of fault diagnosis threshold is summarized, with clear discussion of the accuracy, cost and efficiency of the method. The proposed method paves a way for the application of the digital twin on the fast development of battery management algorithms.

Strategically switching metaheuristics for effective parameter estimation of electrochemical lithium-ion battery models

Electrochemical lithium-ion battery models have drawn considerable research attention as alternative experimental approaches to elucidate the internal mechanisms and physical properties of lithium-ion batteries. To obtain such useful models, accurate parameter estimation is an essential prerequisite, although it is challenging because of their complexity. Metaheuristics-based parameter estimation methods have been suggested individually in this regard. To achieve more accurate parameter estimation, this study provides a systematic guide for strategically switching to an appropriate metaheuristic. First, we identify objective functions that are beneficial to each metaheuristic. Thus, ten baseline functions suitable for specific metaheuristics are selected, and learned by a convolutional neural network (CNN). From the sample particles collected up to present, the designed CNN provides the most similar baseline function and then recommends the corresponding proper metaheuristic. To escape from local optima and deliver a better solution, particle resampling and metaheuristic suitability tests are also performed if the current metaheuristic does not significantly improve within the prescribed iteration. Compared with average results over five runs of each metaheuristic, experimental data demonstrate that the proposed strategically switched metaheuristics (SSM) reduces the difference between experimental and simulated voltage responses by more than half. Additionally, the SSM estimates the external short circuit resistance with a 3 % error using the internal short circuit (ISC) built-in model.

Research on Mechanical Properties of Winding Axial Bending Under Unbalance of Transformer Pad

To improve the study on the influence of transformer windings and pad support on transformer short-circuit resistance, the calculation method of wire bending elastic modulus is analyzed, and the sample test is carried out: The influences of the unbalance of the two ends of the wire between the number of wires wound and the span of adjacent pads on the force-deflection curve, the bending force at the selected deflection, and the elastic modulus of bending were analyzed. The results showed that: Increasing the number of wires and decreasing the height difference of adjacent pads can improve the bending force and bending elastic modulus of wires, the axial stability, and short-circuit resistance of transformer windings. When the height of the adjacent pad is not balanced, the axial force can increase the compression degree between the pad and the wire, and reduce the span of the wire. Under the action of force, the wire yields twice, which further reduces the bending force and elastic modulus of the wire. This research can provide test data support for improving the installation of transformer winding pads and theoretical evaluation of short-circuit tolerance.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries (LIBs). In this paper, we developed a Convolutional Neural Networks (CNN) based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions. Cycling tests of cells with an external short circuit (ESC) are produced to obtain the database and generate the training/ testing samples. The samples are sequences of voltage, current, charging capacity, charging energy, total charging capacity, total charging energy with a length of 120 s and frequency of 1 Hz, and their corresponding short circuit resistances. A big database with ∼ 6 × 105 samples are generated, covering various short circuit resistances (47 ∼ 470 Ω), current loading modes (Constant current-constant voltage (CC-CV) and drive cycle), and electrochemical states (cycle numbers from 1 to 300). Results show that the average relative absolute error of five random sample splits is 6.75 %±2.8 %. Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups: the optimized input sequence length (∼120 s), feature selection (at least one total capacity-related variable), and rational model design, using multiple layers with different kernel sizes. This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Power cable fired by transient arcing below the action value of relay protection: An analysis of a medium-voltage cable joint breakdown fault

Cable joint breakdown accidents have occurred frequently in recent years and have gradually become an important concern of the power maintenance department. The study of the fault development process of the cable joint is helpful to detect and diagnose in advance, so as to avoid the expansion of the accident. In this paper, a breakdown fault of 20 kV damp three-core cable joint was investigated. Firstly, the recorded fault waveform was analyzed, and the fault cable joint sample was disassembled. Then, the circuit simulation model was established to calculate the short-circuit resistance, which aimed to reveal the dynamic characteristics of the short-circuit resistance of the composite interface for the damp joint. Finally, the effect of moisture on the electric field distortion of the interface and the thermal ablation damage effect of the transient short-circuit current on the copper mesh were discussed by the finite element method. This paper reveals the phenomenon of transient grounding faults arising from moisture at the internal interface of cable joint, which may damage the grounding protection line of the joint and affect the sensitivity of the protective device. The method for analyzing the breakdown fault case of damp cable joint presented in this paper can provide reference and suggestions for the investigation and solution in similar cases.

Rapid synchronous state-of-health diagnosis of membrane electrode assemblies in fuel cell stacks

Consistency evaluation and degradation diagnosis of membrane electrode assemblies (MEAs) in fuel cell stacks are critical issues for fuel cell lifetime extension and commercialization. This study aims to develop a high-efficiency, high-precision, and synchronous state-of-health (SOH) diagnosis methodology for MEAs at the stack level. The micro-current excitation (MCE) method based on multiple excitations is used to evaluate the MEA inconsistency of a 22-cell commercial stack, involving hydrogen crossover, short-circuit resistance, electrochemical surface area, and double-layer capacitance. High precision is demonstrated by the consistency in the membrane-related parameters and the difference in the electrode-related parameters between the cathode and anode tests. Herein, an efficient single excitation and natural discharging (SEND) analytical method based on the excitation-discharging-combined equivalent circuit model is originally proposed. The accuracy of SEND can be demonstrated by comparing its results with the standard results of the MCE, which fit the baseline y = x with high linearity and identical evaluation of inconsistency. Moreover, the stability of the SEND is proved by its independence from the excitation current, which is reflected by the presence of tiny standard deviations. In addition, the bifurcation feature in the integrated net charge curves of the excitation and natural discharging processes proves the irreversibility of partial oxidation reactions in the high-potential region of excitation. The SEND significantly increases the testing efficiency using only a single excitation. It has promising prospects for the rapid consistency-based MEA screening, degradation evaluation, and recombination of MEAs.

Three-Dimensional Modeling for the Internal Shorting Caused Thermal Runaway Process in 20Ah Lithium-Ion Battery

Better understanding of how internal short circuit causes thermal runaway will benefit the engineering for safer lithium-ion batteries. In this study, three-dimensional (3D) numerical simulations of a 20Ah lithium battery under internal shorting condition are performed. The effects of internal short circuit area, resistance, penetration depth, convective heat transfer coefficient and internal short circuit position, on the thermal runaway are investigated with the simulations in this work. This study demonstrates that the average cell temperature is only weakly affected by the internal short circuit area, penetration depth, and position. On the other hand, the internal short circuit resistance and the convective heat transfer coefficient have large impacts on the thermal runaway propagation in the lithium-ion battery. A high convective heat transfer coefficient can effectively suppress the thermal runaway propagation. However, such a high convective heat transfer coefficient is hard to achieve at the cell surface.

Precise and fast safety risk classification of lithium-ion batteries based on machine learning methodology

How to establish a fast and accurate model with physics implications still remains an unsolved headache for the community. Herein, we establish a battery safety risk classification modeling framework based on a machine-learning algorithm that can accurately and rapidly classify the potential safety risk level. The model can identify defective cells, cells with internal short circuits (ISCs), and cells with possible thermal runaway (TR) using a small portion of one cycle data. About 3 × 105 training samples are generated, covering a wide spectrum of State of Charge (SOC), short circuit resistance, and charging/discharging rate (C-rate)) ranges based on established electrochemo-mechanical model, serving as training and prediction datasets for the machine learning (ML) algorithm. Classifiers in the ML model demonstrates satisfactory performance and robustness. We further prove that only 5 min is needed for the model to classify the cells with over 95% accuracy with a voltage error<0.1 mV if the ISC resistance is smaller than 102Ω. This work highlights the promising future of combining physics-based numerical modeling and data-driven algorithm to provide satisfactory battery safety identification with limited data produced in a short cycling time.

The change of the electrophysiological parameters using human intestinal tissues from ulcerative colitis and Crohn's disease

The purpose of this study was to investigate how disease state of the UC and CD patients affect tissue function determined from electrophysiology viewpoint the electrophysiological parameters on normal, ulcerative colitis (UC) and Crohn's disease (CD) patients. Potential differences (PD), short circuit current (Isc) and resistance (R) as electrophysiological parameters were determined using human large intestinal tissues.The measure of autoptical abnormality was quantified on an arbitrary scale of 0–2. A severe effect of ulcer and thickened mucosa by fibrosis was scored as Grade 2. The larger number of autopsy grade on both UC and CD tissues, the lower values of PD and R than those of normal tissues were observed, although Isc values were not statistically changed irrespective of autopsy grade. This electrophysiological observation of reduced PD indicated functional impairment of active ion transport via ion pumps. Additionally, the R values of CD tissues on each autopsy grade tended to be lower than those of UC tissues. These results suggest that the effect of inflammatory bowel disease on barrier function is different between UC and CD tissues. Therefore, the fibrosis on CD patients might affect the electrophysiological parameters than that of UC patients.

A comprehensive research on internal short circuits caused by copper particle contaminants on cathode in lithium-ion batteries

Manufacturing defects are one of the main causes of internal short circuits (ISCs) and then lead to thermal runaway accidents of batteries, which seriously threatens the safety of electric vehicles. Cu particle contaminants are one of the most common and dangerous manufacturing defects. Cu particles that may cause ISC by dissolving on the cathode and precipitating on the anode are smaller in size and less likely to be detected during manufacturing, compared with those that cause ISC by directly piercing the separator. However, there is little research on the mechanism and evaluation of ISC caused by cathode Cu particle contaminants. In this paper, Cu particles were intentionally placed on the cathode of lithium-ion batteries (LIBs). The mechanism and type of ISC induced by the Cu dissolution-precipitation process in LIBs were successfully characterized for the first time, and the evolution process was concluded. The ISC introduced by Cu particles was successfully simulated by a pseudo three-dimensional model. And the size boundary of Cu particles causing ISC was also studied. It was found that the implanted Cu particles will necessarily cause ISC in the batteries. The key to the ISC caused by cathode Cu particles is the precipitation of Cu along the pores of the separator from the anode side to the cathode side. The type of ISC caused by this defect is a cathode-anode short circuit, which only induces a soft ISC. The maximum temperature of the battery with this defect can only increase by approximately 2 °C through the calculation of the model under the set conditions of this study, which would not cause thermal runaway of the battery. In addition, even when the size is as small as 20 μm, the cathode Cu particle contaminants will still cause an ISC with short-circuit resistance of tens of kΩ magnitude.

Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis

Safe operation of large battery storage systems requires advanced fault diagnosis that is able to detect faults and provide an early warning in the event of a fault. Since Internal Short Circuits (ISC) are the most common abuse condition leading to thermal runaway, this study addresses the early detection of incipient soft ISCs at the stage when the fault is still uncritical and does not lead to significant heat generation. The differences in cell voltages as measured by conventional battery management systems prove to be indicative features for ISC diagnosis. However, due to poor balancing and parameter variations, the cell voltage differences exhibit nonlinear variations. This work addresses this challenge with a nonlinear data model based on Kernel Principal Component Analysis (KPCA). To enable an online application in a vehicle, the present work reduces the computational complexity of the method by an optimal choice of training data. An analysis of the contribution of each cell to the fault statistics enables identification of the faulty cell. Since early-stage ISCs can exhibit a wide range of short-circuit resistances, experimental validation is performed with resistances from 10Ω to 10kΩ, which are correctly detected and isolated by the optimized cross-cell monitoring in all cases.

Safety Assessment for External Short Circuit of Li-Ion Battery in ESS Application Based on Operation and Environment Factors

In recent years, the demand for medium and large secondary batteries in EV (electric vehicle) and ESS (energy storage systems) applications has been rapidly increasing worldwide, and accordingly, the market size is increasing exponentially. However, the recent fire accidents related to secondary batteries for EVs and ESS are having a negative impact on the battery market. Therefore, this paper implements an accident simulation device to perform an external short-circuit test, one of the typical safety tests for NMC-series prismatic and pouch-type batteries that are widely used among battery cells used in medium and large secondary batteries. The implemented accident simulation device for the external short-circuit test is composed of short-circuit resistance, measuring device, control device, etc., and is configured to analyze external short-circuit characteristics according to various test conditions. Based on this, an external short-circuit test according to the type, short-circuit resistance and SOC (states of charge) of the lithium-ion battery was performed to confirm the current and temperature characteristics according to each condition. As a result of performing an external short-circuit test for each protection device in the battery module and preprocessing temperature, it is certain that the module fuse operates over 120 times faster than the cell fuse based on the same SOC conditions, and the quantity of electric charge in the module fuse is over 110 times smaller than of the cell fuse in the case of a short-circuit fault. It is also found that the highest and lowest preprocessing temperatures are considered to be severe conditions. Based on the proposed mechanism of an external short circuit in a Li-ion battery and the test device for the external short circuit, it is confirmed that this paper can contribute to the safety assessment of Li-ion battery-based ESS.

(Digital Presentation) In Situ Measurement of Internal Short Circuit Resistance during Nail Penetration of Lithium-Ion Cells

Internal short circuit (ISC) is one of the main causes of lithium-ion cell thermal runaway in field failures. Nail penetration has been widely used to trigger ISC for evaluation of thermal runaway risks. Previous modeling studies1 , 2 revealed that ISC resistance greatly influences heat generation and thermal runaway risk, but there are few reports on experimental measurement of ISC resistance during nail penetration3. Furthermore, ISC resistance was assumed to be constant in these modeling and experimental studies. However, our recent experiments4 , 5 suggested that ISC resistance changes dramatically during nail penetration. Therefore, it is important to measure the dynamic ISC resistance in situ during nail penetration of lithium-ion cells. Such measurement will help better understand if and how an ISC triggers thermal runaway. The experimental data will also be useful for validation and improvement of numerical models on lithium-ion cell nail penetration.In this study we report a method for in situ measurement of dynamic ISC resistance during nail penetration. As shown schematically in Figure 1, a small test lithium-ion cell made of single unit of electrodes and four tabs are developed. Two tabs are connected to a large power supply lithium-ion cell through a low-resistance current sensor. The other two tabs are connected to a voltage sensor for measurement of test cell voltage. Then the test cell is penetrated using a small, slow and in situ sensing (3S) smart nail as reported in our earlier work4 , 5. A picture of such smart nail is shown in Figure 1. When an ISC is formed inside the test cell by the smart nail, external current will flow from the large power supply cell to the test cell. If the test cell is much smaller than the large power supply cell in dimension and capacity, it is assumed that the measured current is approximately equal to the ISC current and the measured test cell voltage (V2 in the schematic) approximately equal to the ISC voltage. Then the ISC resistance can be obtained through dividing the ISC voltage with the ISC current.Figure 2 and Figure 3 show preliminary results of this study based on dry test cells without electrolyte. The cells were penetrated using a smart nail with outer diameter of 0.76 mm at a speed of ~0.03 mm/s. At the tip of the nail, a thermocouple with diameter of 0.5 mm was embedded for in situ measurement of ISC temperature. The area of cathode in the test cells is 10 mm by 10 mm with a nominal capacity of 1.8 mAh. The large power supply cell has a nominal capacity of 3,000 mAh. Figure 2 shows results of penetration from Al foil to Cu foil while Figure 3 shows results of penetration from Cu foil to Al foil. It can be seen that ISC resistance changed dramatically during nail penetration in both cases. The lowest ISC resistance was several orders of magnitude lower than the stable resistance after full penetration. The lowest ISC resistance also caused rapid increase of ISC temperature, indicating higher risk of thermal runaway. By comparing Figure 2 and Figure 3, a stable resistance after full penetration was similarly seen in the two cases, but the dynamic resistance responses were very different. The dynamic resistance can be attributed to the different resistivity of test cell components as well as the change of contact resistance between the nail and those components. Moreover, the lowest ISC resistance during penetration from Al to Cu is tens of times smaller than that during penetration from Cu to Al. These results provide new insights into nail penetration processes. They also imply that future modeling studies on nail penetration should consider the dynamic change of ISC resistance to more accurately reflect the physical behaviors. More details about these dry test cell results and new results of working test cells with electrolyte will be presented in the conference. References W. Zhao, G. Luo and C.-Y. Wang, Journal of The Electrochemical Society, 162, A207 (2014).T. Yamanaka, Y. Takagishi, Y. Tozuka and T. Yamaue, Journal of Power Sources, 416, 132 (2019).M. Chen, Q. Ye, C. Shi, Q. Cheng, B. Qie, X. Liao, H. Zhai, Y. He and Y. Yang, Batteries & Supercaps, 2, 874 (2019).S. Huang, X. Du, M. Richter, J. Ford, G. M. Cavalheiro, Z. Du, R. T. White and G. Zhang, Journal of The Electrochemical Society, 167 (2020).S. Huang, Z. Du, Q. Zhou, K. Snyder, S. Liu and G. Zhang, Journal of The Electrochemical Society, 168 (2021). Figure 1