Soft Short Circuit Research Articles

Unveiling Cu Nanoparticles Formed During Li Deposition in Anode-Free Batteries.

Developing high-energy-density Li metal batteries is essential for sustainable progress, necessitating in-depth studies of complex battery reactions. The presence of metallic Cu impurities detrimentally impacts battery performance, leading to issues such as self-discharging and internal soft short-circuit. Nevertheless, their formation mechanism and structural characteristics have not been revealed clearly. Here the formation of single-crystalline Cu nanoparticles during the Li deposition process in anode-free cells was identified by transmission electron microscopy. Through investigation of the chemical state of Cu before and after Li deposition, the formation of Cu NPs was attributed to the reduction of the oxide layers formed on the surface of Cu current collectors. Additionally, it was observed that Cu nanoparticles can be formed inside of deposited Li metal. This work reveals the formation pathway and microstructural characteristics of Cu nanoparticles appearing during Li deposition, underscoring the importance of nanoscale investigations into the underlying battery reactions.

(Best Presentation Award - 3rd Place) Evaluating the Safety Limits of Soft Shorting in Li-Ion Batteries

This study aims to redefine the safety limitations of lithium dendrite soft shorting in lithium-ion batteries using isothermal microcalorimetry and thermal reduction tests. Lithium dendrites are generally assumed to pose a safety risk by causing fatal short circuits, which can lead to thermal runaway and catastrophic battery failure. This assumption has led to massive amounts of time and capital on dendrite suppression and elimination research.Thermal reduction tests on delithiated lithium cobalt oxide were conducted to determine the absolute temperature at which thermal runaway occurs in one of the most volatile conventional systems. Isothermal microcalorimetry was conducted on the same system to measure the heat generated by the soft short circuit. Our results showed that the absolute temperature generated by the lithium dendrite soft short circuit was significantly less than the onset of thermal runaway via a thermal reduction route. These findings challenge the general assumption that lithium dendrites will directly lead to thermal runaway events in lithium-ion batteries.Our study suggests that while lithium dendritic shorting may pose a safety risk in lithium-ion batteries, it will not directly lead to a catastrophic thermal event. Further research is needed to explore the potential implications of deactivating the metallic lithium in the system via this soft shorting pathway.

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.

Quantitative diagnosis of the soft short circuit for LiFePO4 battery packs between voltage plateaus

An accurate diagnostic method on the soft short circuit (SSC) for the Li-ion batteries before it evolves to a critical safety issue is recognized as one of the most important functions in battery management systems. Because the SOC (state of charge)-OCV (open circuit voltage) curve of Lithium Iron Phosphate (LiFePO4 or LFP) batteries is flat, there are few diagnostic algorithms that focus on LFP. Therefore, this paper proposes a quantitative SSC diagnosis method for LFP battery packs within a narrow voltage window. The proposed method firstly find the median voltage between the two voltage plateaus during the constant current charging for all the cells in the battery pack. It takes the cell with the highest voltage as the reference cell, and calculates the time differences and electric quantity differences (EQDs) when the other batteries reach the median voltage during every charge. The leakage current and SSC resistance of the SSC cell can be calculated from the increase in EQD after each charge. A series of experiments with external resistors show that this method can identify the SSC resistances of 510 Ω and is suitable for various applications, especially when they are seldom fully charged or discharged.

A Soft Short-Circuit Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

The early detection of soft short-circuit (SC) faults in lithium-ion battery packs is critical to enhance electric vehicle safety and prevent catastrophic hazards. This article proposes a battery fault diagnosis method that achieves joint soft SC fault detection and estimation. Specifically, based on an augmented state-space battery model, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> nonlinear observer is constructed to estimate state of charge (SOC) and soft SC current in the presence of model parameter variations. Then, the asymptotic stability of the estimation error system under the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance is formally proved and a tractable observer design criterion is derived. Furthermore, a diagnosis algorithm is developed to detect soft SC faults via checking the difference between the estimated SOC from the observer and the calculated SOC from Coulomb counting. Once a soft SC fault is detected, the algorithm also allows the soft SC resistance to be calculated from the estimated soft SC current. Finally, soft SC experiments of a series-connected battery pack under different working conditions and various SC resistance values are conducted to verify the effectiveness of the proposed method.

Influence of flexible gelatinous coating layer on electrochemical performance and fatigue failure of lithium battery separator

Recently, the battery security issue receives widespread attention. Investigating the compression performance and failure mode of the battery separator, and then simulating the internal soft short-circuit of the battery due to separator rupture becomes the current focus. In this work, commercial polyethylene separators are coated with a Al2O3 composite organic silicone - acrylic acid gel layer and the electrochemical performance are characterized. The coated battery separator (PEAE-5) exhibits the lowest charge transfer resistance and increases the electrochemical stability window to 5.3 V, since the silicone exhibits oxidation resistance and the acrylic provides flexibility in polar electrolyte solvents. Subsequently, the battery separator is punched in cycles to simulate fatigue failure of the separator under external compression. The results indicate that the separator (PEAE-5) coated with a 5% solid content of organic silicone - acrylic acid emulsion exhibits the best combined physical-mechanical and electrochemical properties, maintaining nearly 80% of the original cycling performance after 50 cycles of compression. The separator coated with the composite organic silicone - acrylic acid gel layer suffers the least from cycle fatigue. The existence of the flexible gelatinous coating layer affords an efficient method to protect the battery separator from destruction.

Fault diagnosis of external soft-short circuit for series connected lithium-ion battery pack based on modified dual extended Kalman filter

Battery fault diagnosis is intractable to guarantee the safety and reliability in an advanced battery management system, especially the diagnosis of external soft-short circuit is still an open and challenging issue. Herein, a novel dual-Kalman filter diagnostic method is developed through deeply analyze the characteristics of the external soft-short circuit fault in the series-connected lithium-ion battery pack. First, a modified extended Kalman filter is applied to obtain the open-circuit voltage online, where the relationship between the curve and state of charge is constructed previously that time-saving not to update the relationship in real-time. Second, the SOC difference between the “median cell” and “minimum cell” is obtained by interpolation. Then, the external soft-short circuit current and resistance are calculated by linearly fitting the difference curve before the inflection point. Where after the point, the SOC difference does not always increase with time even if the short-circuit resistance is fixed owing to the nonlinear characteristics of the battery. The experiments are performed that on one hand is to clarify the fault characteristics and on the other is to validate the effectiveness of the diagnostic algorithm. The proposed diagnostic method gives an excellent performance for detecting the fault of the external soft-short circuit.

Multi-scale short circuit resistance estimation method for series connected battery strings

Short circuit (SC) fault in battery systems is considered as one of the most severe problems, which may result in thermal runaway and fire. This paper tries to utilize the multi-scale technology to estimate the short circuit resistance to give a quantitative analysis of short circuit fault. With the value of the short circuit resistance, it is able to determine to light a warning or stop using the battery immediately. To solve this problem, the multi-scale short circuit resistance estimation method is proposed. Not only the hard short circuit with small resistance but also the soft short circuit with large resistance can be estimated accurately. Additionally, to reduce the computation complexity, only two battery cells in the battery string are needed for the estimation. The experimental test platform is established and different short circuit resistance is applied to the battery string. The results show that the fast estimation of hard short circuit resistance can be realized. Moreover, the soft short circuit resistance is able to be estimated accurately. The hard short circuit resistance can be estimated in 3 s and the estimation error standard deviation for the soft one is less than 4%.

Safety performance and failure prediction model of cylindrical lithium-ion battery

In this paper, the safety performance model of cylindrical lithium-ion batteries, which is based on a second-order oscillation feature that is subjected to mechanical abuse is proposed via a discrete Fourier transformation of experimental data. Combined with the safety performance model and Crushable-Foam material model, the simulation results show that the average predictive error of the force-displacement and failure displacement is less than 8.8% and 4.0%, respectively. A battery fracture angle model is proposed, the fracture angle is only related to the load property, and the state-of-charge has minimal influence under the same load conditions. Furthermore, the failure prediction model of the soft short-circuit and the failure warning verification system are proposed. The experimental results show that the failure prediction model can warn the soft short-circuit in time with a certain safety margin. With further research, the safety performance model and the failure prediction model, which is based on mechanical penetration displacement, can be combined with other failure signals to provide more reliable information about battery safety. The failure warning device has the advantages of low cost and simple structure.

On-board soft short circuit fault diagnosis of lithium-ion battery packs for electric vehicles using extended Kalman filter

The safety of lithium-ion batteries in electric vehicles (EVs) is attracting more attention. To ensure battery safety, it is necessary for early detection of soft short circuit (SC) which may evolve into severe SC faults, leading to fire or thermal runaway. This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter (EKF) for on-board applications in EVs. In the proposed method, the EKF is used to estimate the state of charge (SOC) of the faulty cell by adjusting a gain matrix based on real-time measured voltages. The SOC difference between the estimated SOC and the calculated SOC by coulomb counting for the faulty cell is employed to detect soft SC faults, and the soft SC resistance values are further identified to indicate the degree of fault severity. Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values. The experimental data are acquired to validate the proposed soft SC fault diagnosis method. The results show that the proposed method is effective and robust in detecting a soft SC fault quickly and estimate soft SC resistance accurately.

Dynamic mechanical behavior of prismatic lithium‐ion battery upon impact

Dynamic impact safety of lithium-ion batteries (LIBs) is a hot subject. The mechanical-electrical behavior of LIBs under dynamic loading was studied in this study. Drop-weight tests of two types of indenter, namely, round and flat heads, were conducted. Strain rate and state of charge (SOC) effects on the mechanical properties of LIBs under different indenters were fully discussed. The interaction between mechanical performance and electrical behavior was studied. Experiments show that the structural stiffness of batteries increases with strain rate increase but exhibits little effect from SOC. Different indenters have a significant influence on the mechanical behavior of the prismatic LIBs. Under the same impact rate and SOC, the peak load of a flat head is considerably larger than that of a round head. The battery exhibits a hard short-circuit under the impact of a round head and a soft short-circuit under the impact of a flat head. This result shows that the larger the contact area between the indenter and the battery is, the larger the impact load under the same drop-weight and impact rate will be, although the impact safety of the battery does not decrease. The results provide useful insights into the basic understanding of the electromechanical coupling integrity of LIBs.

State of charge-dependent failure prediction model for cylindrical lithium-ion batteries under mechanical abuse

Based on the previous research, the parameters of the equivalent mechanical model are associated with state of charge, thus, the model can simulate the mechanical behavior of cylindrical lithium-ion batteries under continuous state of charge with simulation software. The comparison results show that the average fitting error of the equivalent mechanical model under different state-of-charge is less than 3.91% and the simulation error for failure displacement is less than 4%. Apart from that, the equivalent spring stiffness of the equivalent mechanical model will soften under mechanical abuse. Based on this, the first order derivative of the load force to displacement (df/dx) of the equivalent mechanical model is carried out. The results show that the displacement of df/dx extreme value can better reflect the mechanical failure and soft short circuit of the battery, the average prediction error is 10.91% and 5.46% which always in advance of the actual failure. So, the failure criteria of lithium-ion batteries related to state of charge are proposed. In the future, these criteria can be used as the reference basis for judging the failure of lithium-ion batteries based on mechanical penetration and the simple form of the prediction criteria can be easily programmed into the controller.