Battery Failure Research Articles

Molecular Synergistic Effects Mediate Efficient Interfacial Chemistry: Enabling Dendrite-Free Zinc Anode for Aqueous Zinc-Ion Batteries.

The primary cause of the accelerated battery failure in aqueous zinc-ion batteries (AZIBs) is the uncontrollable evolution of the zinc metal-electrolyte interface. In the present research on the development of multiadditives to ameliorate interfaces, it is challenging to elucidate the mechanisms of the various components. Additionally, the synergy among additive molecules is frequently disregarded, resulting in the combined efficacy of multiadditives that is unlikely to surpass the sum of each component. In this study, the "molecular synergistic effect" is employed, which is generated by two nonhomologous acid ester (NAE) additives in the double electrical layer microspace. Specifically, ethyl methyl carbonate (EMC) is more inclined to induce the oriented deposition of zinc metal by means of targeted adsorption with the zinc (002) crystal plane. Methyl acetate (MA) is more likely to enter the solvated shell of Zn2+ and will be profoundly reduced to produce SEI that is dominated by organic components under the "molecular synergistic effect" of EMC. Furthermore, MA persists in a spontaneous hydrolysis reaction, which serves to mitigate the pH increase caused by the hydrogen evolution reaction (HER) and further prevents the formation of byproducts. Consequently, the 1E1M electrolyte not only extends the cycle life of the zinc anode to 3140 cycles (1 mA h cm-2 and 1 mA cm-2) but also extends the life of the Zn//MnO2 full battery, with the capacity retention rate still at 89.9% after 700 cycles.

The analysis of the overall failure of practical Zn−Ni battery

Zn−Ni batteries are viewed as promising power sources for electric tools and electric vehicles (EVs) due to their benefits of high safety, high working voltage and attractive rate performance. However, the practical applications of the Zn−Ni batteries are hampered by poor cycling performance, posing significant challenges for their widespread utilization. To effectively address this pressing concern, a profound investigation of the intrinsic failure mechanisms underlying Zn−Ni batteries holds paramount importance. This paper carries out a thorough analysis of battery behavior to simulate industrial application scenarios using two different assembly methods of Zn−Ni batteries. One type is a battery composed of one Ni(OH)2 cathode and one Zn anode (abbreviated as OO). The other type is the battery assembled with one Ni(OH)2 cathode and two Zn anodes (abbreviated as OT). Ultimately, it is revealed that the principal factors of Zn anode failure in OO battery are the growth of zinc dendrites, passivation and uneven current distribution. In contrast, for OT battery, the cathode emerges as the failed electrode. Specifically, the rupture of spheres on the positive electrode surface and the detached powder from the substrate result in the decline in capacity and finally the failure of the battery. Two classical battery assembly methodologies were employed to investigate in-depth the failure mechanisms of Zn−Ni batteries, ultimately revealing that the reasons for battery failure mechanism differed, thus providing valuable and practical guidance for future research aimed at enhancing battery lifespan.

Helmholtz Plane Regulation Empowers PF6− Permselectivity Towards High Coulombic Efficiency Dual Ion Battery of 5.5 V

AbstractHigh‐voltage dual ion battery (DIB) is promising for stationary energy storage applications owing to its cost‐effectiveness, which has been a hot topic of research in rechargeable battery fields. However, it still suffers from rapid battery failure caused by the severe solvent co‐intercalation and electrolyte oxidation. To address these bottlenecks, herein a functional electrolyte additive hexafluoroglutaric anhydride (HFGA) is presented based on a Helmholtz plane regulation strategy. It is demonstrated that the HFGA can precisely enter into the Helmholtz plane and positively regulate anion solvation behaviors near the graphite electrode surface owing to its considerable H−F affinity with ethyl methyl carbonate (EMC), thus alleviating EMC‐related co‐intercalation and oxidation decomposition during DIB charging. Meanwhile, HFGA can copolymerize with the presence of PF5 at the Helmholtz plane to participate in forming a CF2‐rich CEI layer with excellent PF6− permselectivity, conducive to achieving PF6− de‐solvation and simultaneously suppressing electrolyte oxidation decomposition. By virtue of such beneficial effects, the graphite cathode enables a 5.5 V DIB with a prominent capacity retention of 92 % and a high average Coulombic efficiency exceeding 99 % within 2000 cycles at 5 C, demonstrating significantly enhanced electrochemical reversibility. The Helmholtz plane regulation strategy marks a milestone in advancing DIB technologies.

Helmholtz Plane Regulation Empowers PF6 - Permselectivity Towards High Coulombic Efficiency Dual Ion Battery of 5.5 V.

High-voltage dual ion battery (DIB) is promising for stationary energy storage applications owing to its cost-effectiveness, which has been a hot topic of research in rechargeable battery fields. However, it still suffers from rapid battery failure caused by the severe solvent co-intercalation and electrolyte oxidation. To address these bottlenecks, herein a functional electrolyte additive hexafluoroglutaric anhydride (HFGA) is presented based on a Helmholtz plane regulation strategy. It is demonstrated that the HFGA can precisely enter into the Helmholtz plane and positively regulate anion solvation behaviors near the graphite electrode surface owing to its considerable H-F affinity with ethyl methyl carbonate (EMC), thus alleviating EMC-related co-intercalation and oxidation decomposition during DIB charging. Meanwhile, HFGA can copolymerize with the presence of PF5 at the Helmholtz plane to participate in forming a CF2-rich CEI layer with excellent PF6 - permselectivity, conducive to achieving PF6 - de-solvation and simultaneously suppressing electrolyte oxidation decomposition. By virtue of such beneficial effects, the graphite cathode enables a 5.5 V DIB with a prominent capacity retention of 92 % and a high average Coulombic efficiency exceeding 99 % within 2000 cycles at 5 C, demonstrating significantly enhanced electrochemical reversibility. The Helmholtz plane regulation strategy marks a milestone in advancing DIB technologies.

Solid‐State Revolution: Assessing the Potential of Solid Polymer Electrolytes in Lithium‐Ion Batteries

AbstractLithium‐ion batteries (LIBs) are crucial for achieving sustainable energy goals due to their high energy density and long cycle life. They dominate markets like consumer electronics, electric vehicles, and stationary energy storage systems. However, current LIBs use liquid electrolytes, which are toxic, flammable, and their liquid state does not resist dendrite growth, causing battery capacity decline and failure. Additionally, the limited availability of lithium and other metals makes liquid‐based LIBs less sustainable. On the other hand, solid polymer electrolytes (SPEs) offer a safer alternative as they are non‐volatile and can resist dendrite growth. However, ion transport in solids is much more restricted than in liquids, while imperfect solid‐solid interfaces contribute to interfacial resistance leading to lower ionic conductivity and increasing Ohmic losses or requiring battery operation at elevated temperatures. Chemical and mechanical degradation of these interfaces can also result in battery capacity fade, and poorer cyclic performance compared to liquid electrolytes. Understanding the ionic transport mechanisms in SPEs is critical for designing and optimizing the nanostructure of polymers and polymer/electrode interfaces to overcome these limitations. In this review, the fundamental mechanisms of ion transport in SPEs will first be explored. Various state‐of‐the‐art approaches for addressing the key challenges in SPEs and their solutions are then discussed. Furthermore, the current status of SPEs is analyzed to determine their potential for replacing liquid electrolytes in the future.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Safety Aspects of Sodium-Ion Batteries: Prospective Analysis from First Generation Towards More Advanced Systems

After an introductory reminder of safety concerns pertaining to early rechargeable battery technologies, this review discusses current understandings and challenges of advanced sodium-ion batteries. Sodium-ion technology is now being marketed by industrial promoters who are advocating its workable capacity, as well as its use of readily accessible and cheaper key cell components. Often claimed to be safer than lithium-ion cells, currently only limited scientifically sound safety assessments of sodium-ion cells have been performed. However, the predicted sodium-ion development roadmap reveals that significant variants of sodium-ion batteries have entered or will potentially enter the market soon. With recent experiences of lithium-ion battery failures, sodium-ion battery safety management will constitute a key aspect of successful market penetration. As such, this review discusses the safety issues of sodium-ion batteries, presenting a twofold innovative perspective: (i) in terms of comparison with the parent lithium-ion technology making use of the same working principle and similar flammable non-aqueous solvent basis, and (ii) anticipating the arrival of innovative sub-chemistries at least partially inspired from successive generations of lithium-ion cells. The authors hope that the analysis provided will assist concerned stakeholders in the quest for safe marketing of sodium-ion batteries.

Thermal Runaway Diagnosis of Lithium-Ion Cells Using Data-Driven Method

Fault diagnosis is crucial to guarantee safe operation and extend the operating time while preventing the thermal runaway of the lithium-ion battery. This study presents a data-driven thermal runaway diagnosis framework where Bayesian optimization techniques are applied to optimize the hyperparameter of various machine learning techniques. We use different machine learning models such as support vector machine, naive Bayes, decision tree ensemble, and multi-layer perceptron to estimate a high likelihood of causes of thermal runaway by using the experimental measurements of open-source battery failure data. We analyze different evaluation metrics, including the prediction accuracy, confusion metrics, and receiver operating characteristic curves of different models. An experimental evaluation shows that the classification accuracy of the decision tree ensemble outperforms that of other models. Furthermore, the decision tree ensemble provides robust prediction accuracy even with the strictly limited dataset.

Remaining useful life prediction of high-capacity lithium-ion batteries based on incremental capacity analysis and Gaussian kernel function optimization

Remaining useful life (RUL) is a key indicator for assessing the health status of lithium (Li)-ion batteries, and realizing accurate and reliable RUL prediction is crucial for the proper operation of battery systems. As high-capacity Li batteries have more complex chemical properties, most of the current RUL prediction methods rely mainly on a priori knowledge to make judgments. As a result, prediction accuracy is not high. In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to achieve accurate RUL prediction for 280 Ah high-capacity Li batteries. Validation against the United States National Aeronautics and Space Administration’s four battery test datasets showed that the use of the HI-C dual GPR model resulted in a mean absolute percentage error and root mean square error of less than 0.02 and 0.04, respectively, for the four battery-rated capacity predictions. Additionally, this model achieved an absolute error of less than five battery failure turns. Compared with a single model, the HI-C dual GPR model not only had high accuracy but also solved the problem that the HI was not measurable in the actual battery operation, which made it more suitable for RUL prediction of Li batteries.

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.

Revealing the Failure Mechanisms of Lithium Metal Solid‐State Batteries with Solid Inorganic Electrolytes by In situ Electron Microscopy

Lithium metal solid‐state batteries (LMSSBs) are considered to be one of the ultimate choices for future energy storage systems because of their high theoretical energy density and enhanced safety. However, the development of LMSSBs has been seriously hindered by some practical issues, such as Li dendrite penetration in the solid‐state electrolytes (SSEs) and uncontrolled interphase growth at the Li/SSE interface, which can cause severe battery degradation, failure, and even safety hazards. To construct safe high‐performance LMSSBs, it is crucial to gain an in‐depth understanding of the failure mechanisms induced by these challenges, especially through direct visualization of the failure processes. In this review, the recent progress on the mechanistic study of LMSSBs by in situ electron microscopy is summarized. In situ transmission electron microscopy (TEM) and scanning electron microscopy (SEM) offer an opportunity to probe the battery failure mechanism by observing the associated physical and chemical processes at nano/atomic resolution. The failure causes of Li dendrites growth and interphase formation are classified and discussed, followed by the corresponding solutions to address these issues. Additionally, the emerging perspectives on future research directions in this field are also summarized.

Untangling dendrite growth dynamics in hybrid flow batteries

Despite advancements, dendrite growth at the anode continues to be a persistent roadblock in accelerating the widespread deployment of hybrid flow batteries as the next-generation energy storage solution, due to the significant impact of dendrites on cycling performance and the potential for battery failure. The ability to analyze energy storage systems at micro-to-macro levels offers unprecedented insights into their behavior and performance. Herein, we develop a multiscale model utilizing phase-field method to investigate dendrite formation, growth, and stripping under operational conditions. The Zn-I system is employed to unravel the intricacies of dendrite evolution and its mitigation through strategic utilization of critical battery parameters. Our findings not only uncover precise zinc morphologies but also provide valuable insights into battery performance toward developing a strategy for mitigating dendrite growth and enhancing battery efficiency at high current density. To our knowledge, this is the first work to comprehensively untangle electrodeposition dynamics at multiscale in the field of flow battery and related research. The findings revolutionize our understanding of deposition behavior, driving transformative advancements in hybrid flow battery design and development, with potential applicability to other battery systems.

2024 Tianmulake Advanced Anode Materials Innovation Forum and Sixth National Conference of Advanced Battery Failure Analysis and Testing Technology concluded successfully

2024 Tianmulake Advanced Anode Materials Innovation Forum and Sixth National Conference of Advanced Battery Failure Analysis and Testing Technology concluded successfully

Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data

Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts.

Visualization of stepwise electrode decomposition in a nail penetrated commercial lithium-ion cell using low-temperature synchrotron X-ray computed tomography

The transition towards zero carbon emissions in power generation hinges on the integration of efficient electrical energy storage systems, with lithium-ion batteries (LIBs) positioned as a pivotal technology. While generally safe, deviations in their operational guidelines due to manufacturing defects or misuse can lead to critical safety concerns, notably thermal runaway (TR) events. Internal short circuits (ISCs) are primary initiators of TR within LIBs. For abuse testing, ISCs are often triggered by nail penetration. This study explores the morphological changes and mechanisms underlying ISC-induced TR in LIBs using operando synchrotron X-ray computed tomography (SXCT) at subzero temperatures. A novel cryogenic setup was developed to control a stepwise temperature increase in the damaged sample while monitoring electrochemical characteristics and simultaneously enabling acquisition of high-resolution SXCT images. The findings reveal that conducting nail penetration at minus 80 °C prevents immediate TR, enabling detailed analysis of subsequent structural and electrochemical behavior during controlled thawing. Thus, the initiation of TR processes at localized ISC sites has been observed, evidenced by voltage fluctuations and morphological changes, such as cathode material cracking and decomposition. These results underscore the importance of temperature control in mitigating TR risks and provide critical insights into the internal dynamics of LIBs under abusive conditions. The developed cryogenic SXCT methodology offers a powerful tool for non-destructive, high-resolution investigation of battery failure mechanisms, contributing to the enhancement of LIB safety.

Tracing Root Causes of Electric Vehicle Fires

As electric vehicles (EVs) emerge as the backbone of modern transportation, the concurrent uptick in battery fire incidents presents a disconcerting challenge. To tackle this issue effectively, it is imperative to pierce beyond the superficial causes of lithium‐ion battery (LIB) failures—such as equipment malfunctions or physical damage—and to excavate the underlying triggers. This nuanced approach is pivotal to refining EV quality, diminishing fire incidents, and bolstering consumer trust. While issues that are readily apparent to consumers, like spontaneous battery degradation, vehicular collisions, or submersion, may seem like the primary culprits, they merely scratch the surface of a more complex problem. The true mechanisms behind these fires involve internal and external short circuits, yet these explanations fall short in providing actionable guidance for manufacturers or end‐users to enhance LIB safety. The heart of the issue is identifying the root causes that bridge failure triggers and resulting fires. A total of 20 root causes are identified, linking them to real‐world scenarios like overcharging causing internal shorts or wire harness issues leading to external shorts. With this insight, stakeholders can more effectively investigate and understand EV LIB fire incidents, bolstering EV safety and reliability, and promoting consumer confidence.

Electric Field Regulator Constructed by Magnetron Sputtering for Dendrite‐Free and Stable Zinc Metal Anode

AbstractRechargeable aqueous zinc‐ion batteries (AZIBs) are considered to be one of the most promising devices in the next generation of energy storage systems. However, the uncontrolled growth of Zn dendrites during electroplating leads to rapid battery failure, which hinders the wide application of AZIBs. In this work, an Fe metal interface (FMI) with electric field regulation is designed on the Zn metal anode using a magnetron sputtering technology. The FMI layer with nanosheet array not only uniforms the surface electric field, but also adjusts the surface Zn2+ ion distribution to inhibit 2D diffusion. The strong orientation relationships of the FMI layer enhance the reversibility of Zn plating/stripping, improving the structural stability of the interface layer. Consequently, FMI@Zn symmetric cell exhibits ultra‐stable lifespan for over 6000 h (Cumulative plated capacity, CPC = 15 Ah cm−2) with a low voltage hysteresis of 46.4 mV and high Coulombic efficiency of 99.8% at 5 mA cm−2. Even at the large current density of 40 mA cm−2, the CPC reaches 19.7 Ah cm−2. The proposed electric field regulation strategy reveals a promising prospect for designing highly stable Zn anode, which also applies to other metal anodes in energy storage systems.

Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model

The gradual decrease capacity serves as a pivotal health indicator, reflecting the condition of lithium-ion batteries. Accurate forecasting of capacity can ascertain the remaining lifespan of these batteries at any given cycle, which is crucial for managing batteries in electric vehicles. This paper proposes an Autoregression with Exogenous Variables (AREV) model, which continually updates itself through a sliding window, offering predictions of battery state of health and remaining useful life, which extends battery prognostics at a fixed operating condition to different operating conditions. In addition, unlike most models that require multiple battery data of the same type for training, the proposed model only requires the use of fragmented data of the target battery with length around 30-50 cycles for capacity prediction and determines battery life based on battery failure thresholds. The above two points enable this model to be updated online without the need for any offline training. Finally, four different types of battery dataset , with different chemical substances and different charge and discharge conditions (especially dataset that follows random walk discharging profile to stimulate the real power consumption process) , are applied to verify the effectiveness and robustness of proposed RUL prediction approach. It shows that the proposed model can accurately predicting future capacity values. Timely warning signals can be issued before the end of life of battery, thereby ensuring the safe driving of electric vehicles and timely battery replacement.

High-Speed Imaging for Investigating Battery Failure Mechanisms

High-Speed Imaging for Investigating Battery Failure Mechanisms

Theoretical approaches to study degradation in Li-ion battery cathodes: Crucial role of exchange and correlation

Abstract Li-ion batteries have become essential in energy storage, with demand rising steadily. Cathodes, crucial for determining capacity and voltage, face challenges like degradation in the form of thermal runaway and battery failure. Understanding these degradation phenomena is vital for developing mitigation strategies. Experimental techniques such as XAS, XPS, PES, UV–Vis, RIXS, NMR, and OEMS are commonly used, but theoretical modelling, particularly atomistic modelling with density-functional theory (DFT), provides key insights into the microscopic electronic behaviours causing degradation. While DFT offers a precise formulation, its approximations in the exchange-correlation functional and its ground-state, 0K limitations necessitate additional methods like ab initio molecular dynamics. Recently, many-body electronic structure methods have been used alongside DFT to better explain electron–electron interactions and temperature effects. This review emphasizes material-specific methods and the importance of electron–electron interactions, highlighting the role of many-body methods in addressing key issues in cathode degradation and future development in electron–phonon coupling methods. Graphical abstract