Rechargeable lithium-ion cell state of charge and defect detection by in-situ inside-out magnetic resonance imaging

Regulation of Interfacial Chemistry Enabling High-Power Dual-Ion Batteries at Low Temperatures

Interfacial chemistry plays a crucial role in determining the electrochemical properties of low-temperature rechargeable batteries. Although existing interface engineering has significantly improved the capacity of rechargeable batteries operating at low temperatures, challenges such as sharp voltage drops and poor high-rate discharge capabilities continue to limit their applications in extreme environments. In this study, an energy-level-adaptive design strategy for electrolytes to regulate interfacial chemistry in low-temperature Li||graphite dual-ion batteries (DIBs) is proposed. This strategy enables the construction of robust interphases with superior ion-transfer kinetics. On the graphite cathode, the design endues the cathode interface with solvent/anion-coupled interfacial chemistry, which yields an nitrogen/phosphor/sulfur/fluorin (N/P/S/F)-containing organic-rich interphase to boost anion-transfer kinetics and maintains excellent interfacial stability. On the Li metal anode, the anion-derived interfacial chemistry promotes the formation of an inorganic-dominant LiF-rich interphase, which effectively suppresses Li dendrite growth and improves the Li plating/stripping kinetics at low temperatures. Consequently, the DIBs can operate within a wide temperature range, spanning from -40 to 45 °C. At -40 °C, the DIB exhibits exceptional performance, delivering 97.4% of its room-temperature capacity at 1 C and displaying an extraordinarily high-rate discharge capability with 62.3% capacity retention at 10 C. This study demonstrates a feasible strategy for the development of high-power and low-temperature rechargeable batteries.

Three-dimensional electrochemical-magnetic-thermal coupling model for lithium-ion batteries and its application in battery health monitoring and fault diagnosis

Storage batteries with elevated energy density, superior safety and economic costs continues to escalate. Batteries can pose safety hazards due to internal short circuits, open circuits and other malfunctions during usage, hence real-time surveillance and error diagnosis of the battery's operational state is imperative. In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the spatial distribution pattern of the physical field such as magnetic field and temperature when the battery is operational is acquired. Furthermore, this manuscript also investigates the diagnostic methodology for defective batteries with internal short circuits and fissures, that is, the operational state of the battery is evaluated and diagnosed by the distribution of the magnetic field surrounding the battery. To substantiate the method's practical viability, the present study extends its examination to the 18650-battery pack. We obtained the magnetic field images of the normal operation of the battery pack and the failure state of some batteries and analyzed the relationship between the magnetic field distribution characteristics and the performance of the battery pack, providing a new method for the health monitoring and fault diagnosis of the battery pack. This non-contact method incurs no damage to the battery, concurrently exhibiting elevated sensitivity and extremely rapid response time. Meanwhile, it provides an effective means for non-destructive research on the batteries and can be applied to areas such as battery safety screening and non-destructive testing. This research not only helps to facilitate our understanding of the battery's operating mechanism, but also provides robust support for safe operation and optimal battery design.

Visualizing and Regulating Dynamic Evolution of Interfacial Electrolyte Configuration during De-solvation Process on Lithium-Metal Anode

Acting as a passive protective layer, solid-electrolyte interphase (SEI) plays a crucial role in maintaining the stability of the Li-metal anode. Derived from the reductive decomposition of electrolytes (e.g., anion and solvent), the SEI construction presents as an interfacial process accompanied by the dynamic de-solvation process during Li-metal plating. However, typical electrolyte engineering and related SEI modification strategies always ignore the dynamic evolution of electrolyte configuration at the Li/electrolyte interface, which essentially determines the SEI architecture. Herein, by employing advanced electrochemical in situ FT-IR and MRI technologies, we directly visualize the dynamic variations of solvation environments involving Li+-solvent/anion. Remarkably, a weakened Li+-solvent interaction and anion-lean interfacial electrolyte configuration have been synchronously revealed, which is difficult for the fabrication of anion-derived SEI layer. Moreover, as a simple electrochemical regulation strategy, pulse protocol was introduced to effectively restore the interfacial anion concentration, resulting in an enhanced LiF-rich SEI layer and improved Li-metal plating/stripping reversibility.

Diagnosis of Current Flow Patterns Inside Fault-Simulated Li-Ion Batteries via Non-Invasive, In Operando Magnetic Field Imaging

With the growing popularity of Li-ion batteries in large-scale applications, building a safer battery has become a common goal of the battery community. Although the small errors inside the cells trigger catastrophic failures, tracing them and distinguishing cell failure modes without knowledge of cell anatomy can be challenging using conventional methods. In this study, a real-time, non-invasive magnetic field imaging (MFI) analysis that can signal the battery current-induced magnetic field and visualize the current flow within Li-ion cells is developed. A high-speed, spatially resolved MFI scan is used to derive the current distribution pattern from cells with different tab positions at a current load. Current maps are collected to determine possible cell failures using fault-simulated batteries that intentionally possess manufacturing faults such as lead-tab connection failures, electrode misalignment, and stacking faults (electrode folding). A modified MFI analysis exploiting the magnetic field interference with the countercurrent-carrying plate enables the direct identification of defect spots where abnormal current flow occurs within the pouch cells.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

Zero- to Ultralow-Field Nuclear Magnetic Resonance Enhanced with Dissolution Dynamic Nuclear Polarization

Zero- to ultralow-field nuclear magnetic resonance is a modality of magnetic resonance experiment which does not require strong superconducting magnets. Contrary to conventional high-field nuclear magnetic resonance, it has the advantage of allowing high-resolution detection of nuclear magnetism through metal as well as within heterogeneous media. To achieve high sensitivity, it is common to couple zero-field nuclear magnetic resonance with hyperpolarization techniques. To date, the most common technique is parahydrogen-induced polarization, which is only compatible with a small number of compounds. In this article, we establish dissolution dynamic nuclear polarization as a versatile method to enhance signals in zero-field nuclear magnetic resonance experiments on sample mixtures of [¹³C]sodium formate, [1-¹³C]glycine, and [2-¹³C]sodium acetate, and our technique is immediately extendable to a broad range of molecules with >1 s relaxation times. We find signal enhancements of up to 11,000 compared with thermal prepolarization in a 2 T permanent magnet. To increase the signal in future experiments, we investigate the relaxation effects of the TEMPOL radicals used for the hyperpolarization process at zero- and ultralow-fields.

A Novel Online State of Health Estimation Method for Electric Vehicle Pouch Cells Using Magnetic Field Imaging and Convolution Neural Networks

Lithium-ion batteries (LiBs) are used as the main power source in electric vehicles (EVs). Despite their high energy density and commercial availability, LiBs chronically suffer from non-uniform cell ageing, leading to early capacity fade in the battery packs. In this paper, a non-invasive, online characterisation method based on deep learning models is proposed for cell-level SoH estimation. For an accurate measurement of the state of health (SoH), we need to characterize electrochemical capacity fade scenarios carefully. Then, with the help of real-time monitoring, the control systems can reduce the LiB’s degradation. The proposed method, which is based on convolutional neural networks (CNN), characterises the changes in current density distributions originating from the positive electrodes in different SoH states. For training and classification by the deep learning model, current density images (CDIs) were experimentally acquired in different ageing conditions. The results confirm the efficiency of the proposed approach in online SoH estimation and the prediction of the capacity fade scenarios.

Rational Design of Biaxial Tensile Strain for Boosting Electronic and Ionic Conductivities of Na2 MnSiO4 for Rechargeable Sodium-Ion Batteries

Using first-principles calculations, biaxial tensile (ϵ=2 and 4 %) and compressive (ϵ=-2 and -4 %) straining of Na2 MnSiO4 lattices resulted into radial distance cut offs of 1.65 and 2 Å, respectively, in the first and second nearest neighbors shell from the center. The Si-O and Mn-O bonds with prominent probability density peaks validated structural stability. Wide-band gap of 2.35 (ϵ=0 %) and 2.54 eV (ϵ=-4 %), and narrow bandgap of 2.24 eV (ϵ=+4 %) estimated with stronger coupling of p-d σ bond than that of the p-d π bond, mainly contributed from the oxygen p-state and manganese d-state. Na+ -ion diffusivity was found to be enhanced by three orders of magnitude as the applied biaxial strain changed from compressive to tensile. According to the findings, the rational design of biaxial strain would improve the ionic and electronic conductivity of Na2 MnSiO4 cathode materials for advanced rechargeable sodium-ion batteries.

Power Batteries Health Monitoring: A Magnetic Imaging Method Based on Magnetoelectric Sensors

With the popularity of electric vehicles, the ever-increasing demand for high-capacity batteries highlights the need for monitoring the health status of batteries. In this article, we proposed a magnetic imaging technique (MIT) to investigate the health status of power batteries nondestructively. This technique is based on a magnetic sensor array, which consists of a 16-channel high-performance magnetoelectric sensor, and the noise equivalent magnetic induction (NEB) of each channel reaches 3–5 pT/Hz1/2@10 Hz. The distribution of the magnetic field is imaged by scanning the magnetic field variation of different positions on the surface. Therefore, the areas of magnetic anomalies are identified by distinguishing different magnetic field abnormal results. and it may be possible to classify the battery failure, so as to put forward suggestions on the use of the battery. This magnetic imaging method expands the application field of this high-performance magnetoelectric sensor and contributes to the battery’s safety monitoring. Meanwhile, it may also act as an important role in other nondestructive testing fields.

In Situ Detection of Lithium-Ion Battery Pack Capacity Inconsistency Using Magnetic Field Scanning Imaging

One of the main obstacles for the reliability and safety of a lithium-ion battery pack is the difficulty in guaranteeing its capacity consistency at harsh operating conditions, while the key solution is accurate detection of cell capacity inconsistency within the battery pack without taking it apart for destructive testing. Here, an in situ and nondestructive technology is proposed for this purpose, by imaging the magnetic field of the battery pack during its operation, the minor current imbalance within the pack can be identified without strong interference of the magnetic susceptibility due to state of charge change, and the corresponding location can also be determined. The feasibility of employing this technique in the battery pack with multicells is also demonstrated, which is beneficial in practical systems for evaluating the battery pack reliability, with which mitigation and maintenance can be taken to extend its remaining useful life.

Solid-State NMR and MRI Spectroscopy for Li/Na Batteries: Materials, Interface, and In Situ Characterization

Enhancing the electrochemical performance of batteries, including the lifespan, energy, and power densities, is an everlasting quest for the rechargeable battery community. However, the dynamic and coupled (electro)chemical processes that occur in the electrode materials as well as at the electrode/electrolyte interfaces complicate the investigation of their working and decay mechanisms. Herein, the recent developments and applications of solid-state nuclear magnetic resonance (ssNMR) and magnetic resonance imaging (MRI) techniques in Li/Na batteries are reviewed. Several typical cases including the applications of NMR spectroscopy for the investigation of the pristine structure and the dynamic structural evolution of materials are first emphasized. The NMR applications in analyzing the solid electrolyte interfaces (SEI) on the electrode are further concluded, involving the identification of SEI components and investigation of ionic motion through the interfaces. Beyond, the new development of in situ NMR and MRI techniques are highlighted, including their advantages, challenges, applications and the design principle of in situ cell. In the end, a prospect about how to use ssNMR in battery research from the perspectives of materials, interface, and in situ NMR, aiming at obtaining deeper insight of batteries with the assistance of ssNMR is represented.

Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells

Low-grade waste heat is an abundant and underutilised energy source. In this context, thermo-electrochemical cells (i.e., systems able to harvest heat to generate electricity) are being intensively studied to deliver the promises of efficient and cost-effective energy harvesting and electricity generation. However, despite the advances in performance disclosed in recent years, understanding the internal processes occurring within these devices is challenging. In order to shed light on these mechanisms, here we report an operando magnetic resonance imaging approach that can provide quantitative spatial maps of the electrolyte temperature and redox ion concentrations in functioning thermo-electrochemical cells. Time-resolved images are obtained from liquid and gel electrolytes, allowing the observation of the effects of redox reactions and competing mass transfer processes such as thermophoresis and diffusion. We also correlate the physicochemical properties of the system with the device performance via simultaneous electrochemical measurements.

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review

Battery degradation is a fundamental concern in battery research, with the biggest challenge being to maintain performance and safety upon usage. From the microstructure of the materials to the design of the cell connectors in modules and their assembly in packs, it is impossible to achieve perfect reproducibility. Small manufacturing or environmental variations will compound big repercussions on pack performance and reliability. This review covers the origins of cell-to-cell variations and inhomogeneities on a multiscale level, their impact on electrochemical performance, as well as their characterization and tracking methods, ranging from the use of large-scale equipment to in operando studies.

AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)

Sheet metal-based products serve as a major portion of the furniture market and maintain higher quality standards by being competitive. During industrial processes, while converting a sheet metal to an end product, new defects are observed and thus need to be identified carefully. Recent studies have shown scratches, bumps, and pollution/dust are identified, but orange peel defects present overall a new challenge. So our model identifies scratches, bumps, and dust by using computer vision algorithms, whereas orange peel defect detection with deep learning have a better performance. The goal of this paper was to resolve artificial intelligence (AI) as an AI landing challenge faced in identifying various kinds of sheet metal-based product defects by ALDB-DL process automation. Therefore, our system model consists of multiple cameras from two different angles to capture the defects of the sheet metal-based drawer box. The aim of this paper was to solve multiple defects detection as design and implementation of Industrial process integration with AI by Automated Optical Inspection (AOI) for sheet metal-based drawer box defect detection, stated as AI Landing for sheet metal-based Drawer Box defect detection using Deep Learning (ALDB-DL). Therefore, the scope was given as achieving higher accuracy using multi-camera-based image feature extraction using computer vision and deep learning algorithm for defect classification in AOI. We used SHapley Additive exPlanations (SHAP) values for pre-processing, LeNet with a (1 × 1) convolution filter, and a Global Average Pooling (GAP) Convolutional Neural Network (CNN) algorithm to achieve the best results. It has applications for sheet metal-based product industries with improvised quality control for edge and surface detection. The results were competitive as the precision, recall, and area under the curve were 1.00, 0.99, and 0.98, respectively. Successively, the discussion section presents a detailed insight view about the industrial functioning with ALDB-DL experience sharing.

Lower than low: Perspectives on zero- to ultralow-field nuclear magnetic resonance

The less-traveled low road in nuclear magnetic resonance is discussed, honoring the contributions of Prof. Bernhard Blümich, aspiring towards reaching 'a new low.' A history of the subject and its current status are briefly reviewed, followed by an effort to prophesy possible directions for future developments.

Rapid Online Solid-State Battery Diagnostics with Optically Pumped Magnetometers

Solid-state battery technology is motivated by the desire to deliver flexible power storage in a safe and efficient manner. The increasingly widespread use of batteries from mass production facilities highlights the need for a rapid and sensitive diagnostic tool for identifying battery defects. We demonstrate the use of atomic magnetometry to measure the magnetic fields around miniature solid-state battery cells. These fields encode information about battery manufacturing defects, state of charge, and impurities, and they can provide important insights into battery aging processes. Compared with SQUID-based magnetometry, the availability of atomic magnetometers, however, highlights the possibility of constructing a low-cost, portable, and flexible implementation of battery quality control and characterization technology.

Mapping oscillating magnetic fields around rechargeable batteries

Power storage devices such as batteries are a crucial part of modern technology. The development and use of batteries has accelerated in the past decades, yet there are only a few techniques that allow gathering vital information from battery cells in a nonivasive fashion. A widely used technique to investigate batteries is electrical impedance spectroscopy (EIS), which provides information on how the impedance of a cell changes as a function of the frequency of applied alternating currents. Building on recent developments of inside-out MRI (ioMRI), a technique is presented here which produces spatially-resolved maps of the oscillating magnetic fields originating from the alternating electrical currents distributed within a cell. The technique works by using an MRI pulse sequence synchronized with a gated alternating current applied to the cell terminals. The approach is benchmarked with a current-carrying wire coil, and demonstrated with commercial and prototype lithium-ion cells. Marked changes in the fields are observed for different cell types.

Nuclear magnetic resonance spectroscopy of rechargeable pouch cell batteries: beating the skin depth by excitation and detection via the casing

Rechargeable batteries are notoriously difficult to examine nondestructively, and the obscurity of many failure modes provides a strong motivation for developing efficient and detailed diagnostic techniques that can provide information during realistic operating conditions. In-situ NMR spectroscopy has become a powerful technique for the study of electrochemical processes, but has mostly been limited to laboratory cells. One significant challenge to applying this method to commercial cells has been that the radiofrequency, required for NMR excitation and detection, cannot easily penetrate the battery casing due to the skin depth. This complication has limited such studies to special research cell designs or to 'inside-out' measurement approaches. This article demonstrates that it is possible to use the battery cell as a resonator in a tuned circuit, thereby allowing signals to be excited inside the cell, and for them to subsequently be detected via the resonant circuit. Employing this approach, ⁷Li NMR signals from the electrolyte, as well as from intercalated and plated metallic lithium in a multilayer (rolled) commercial pouch cell battery were obtained. Therefore, it is anticipated that critical nondestructive device characterization can be performed with this technique in realistic and even commercial cell designs.

Sensitive magnetometry reveals inhomogeneities in charge storage and weak transient internal currents in Li-ion cells

The ever-increasing demand for high-capacity rechargeable batteries highlights the need for sensitive and accurate diagnostic technology for determining the state of a cell, for identifying and localizing defects, and for sensing capacity loss mechanisms. Here, we leverage atomic magnetometry to map the weak induced magnetic fields around Li-ion battery cells in a magnetically shielded environment. The ability to rapidly measure cells nondestructively allows testing even commercial cells in their actual operating conditions, as a function of state of charge. These measurements provide maps of the magnetic susceptibility of the cell, which follow trends characteristic for the battery materials under study upon discharge. In particular, hot spots of charge storage are identified. In addition, the measurements reveal the capability to measure transient internal current effects, at a level of μA, which are shown to be dependent upon the state of charge. These effects highlight noncontact battery characterization opportunities. The diagnostic power of this technique could be used for the assessment of cells in research, quality control, or during operation, and could help uncover details of charge storage and failure processes in cells.

Application of Magnetic Resonance Techniques to the In Situ Characterization of Li-Ion Batteries: A Review

In situ magnetic resonance (MR) techniques, such as nuclear MR and MR imaging, have recently gained significant attention in the battery community because of their ability to provide real-time quantitative information regarding material chemistry, ion distribution, mass transport, and microstructure formation inside an operating electrochemical cell. MR techniques are non-invasive and non-destructive, and they can be applied to both liquid and solid (crystalline, disordered, or amorphous) samples. Additionally, MR equipment is available at most universities and research and development centers, making MR techniques easily accessible for scientists worldwide. In this review, we will discuss recent research results in the field of in situ MR for the characterization of Li-ion batteries with a particular focus on experimental setups, such as pulse sequence programming and cell design, for overcoming the complications associated with the heterogeneous nature of energy storage devices. A comprehensive approach combining proper hardware and software will allow researchers to collect reliable high-quality data meeting industrial standards.

In-situ MRI velocimetry of the magnetohydrodynamic effect in electrochemical cells

Electrochemical reactions have become increasingly important in a large number of processes and applications. The use of NMR (Nuclear Magnetic Resonance) techniques to follow in situ electrochemistry processes has been gaining increasing attention from the scientific community because they allow the identification and quantification of the products and reagents, whereas electrochemistry measurements alone are not able to do so. However, when an electrochemical reaction is performed in situ the reaction rate can be increased by action of the Lorentz force, which is equal to the cross product between the current density and the magnetic field applied. This phenomenon is called the magnetohydrodynamic (MHD) effect. Although this process is beneficial because it accelerates the reaction, it needs to be well understood and taken into account during the in situ electrochemical measurements. The MHD effect is based on increased mass transfer, which is shown by in situ MRI velocimetry here. Images had to be acquired in a rapid manner since current was not pulsed. Significant velocities in a plane parallel to the electrodes alongside with complex flow patterns were detected.

Diagnosing current distributions in batteries with magnetic resonance imaging

Batteries and their defects are notoriously difficult to analyze non-destructively, and consequently, many defects and failures remain little noticed and characterized until they cause grave damage. The measurement of the current density distributions inside a battery could reveal information about deviations from ideal cell behavior, and could thus provide early signs of deterioration or failures. Here, we describe methodology for fast nondestructive assessment and visualization of the effects of current distributions inside Li-ion pouch cells. The technique, based on magnetic resonance imaging (MRI), allows measuring magnetic field maps during charging/discharging. Marked changes in the distributions are observed as a function of the state of charge, and also upon sustaining damage. In particular, it is shown that nonlinearities and asymmetries of current distributions could be mapped at different charge states. Furthermore, hotspots of current flow are also shown to correlate with hotspots in charge storage. This technique could potentially be of great utility in diagnosing the health of cells and their behavior under different charging or environmental conditions.

In situ and operando magnetic resonance imaging of electrochemical cells: A perspective

Nuclear Magnetic Resonance (NMR) spectroscopy and Magnetic Resonance Imaging (MRI) of electrochemical devices have become powerful tools for the in situ investigation of electrochemical processes. The techniques often take advantage of NMR's nondestructive/noninvasive properties, its sensitivity to frequency shifts, internal interactions, and transport processes, as well as its ability to measure liquid phases and disordered materials. Here, we provide a perspective on recent work on in situ MRI of electrochemical devices, batteries and relevant model systems, and discuss their applications and promises in assessing device performance, and electrochemical processes in cells.

Applications of magnetic resonance imaging in chemical engineering

While there are many techniques to study phenomena that occur in chemical engineering applications, magnetic resonance imaging (MRI) receives increasing scientific interest. Its non-invasive nature and wealth of parameters with the ability to generate functional images and contrast favors the use of MRI for many purposes, in particular investigations of dynamic phenomena, since it is very sensitive to motion. Recent progress in flow-MRI has led to shorter acquisition times and enabled studies of transient phenomena. Reactive systems can easily be imaged if NMR parameters such as relaxation change along the reaction coordinate. Moreover, materials and devices can be examined, such as batteries by mapping the magnetic field around them.

Distortion-free inside-out imaging for rapid diagnostics of rechargeable Li-ion cells

Safety risks associated with modern high energy-dense rechargeable cells highlight the need for advanced battery screening technologies. A common rechargeable cell exposed to a uniform magnetic field creates a characteristic field perturbation due to the inherent magnetism of electrochemical materials. The perturbation pattern depends on the design, state of charge, accumulated mechanical defects, and manufacturing flaws of the device. The quantification of the induced magnetic field with MRI provides a basis for noninvasive battery diagnostics. MRI distortions and rapid signal decay are the main challenges associated with strongly magnetic components present in most commercial cells. These can be avoided by using Single-Point Ramped Imaging with T ₁ enhancement (SPRITE). The method is immune to image artifacts arising from strong background gradients and eddy currents. Due to its superior image quality, SPRITE is highly sensitive to defects and the state of charge distribution in commercial Li-ion cells.

Electrochromic Effect of Indium Tin Oxide in Lithium Iron Phosphate Battery Cathodes for State-of-Charge Determination

In this article, we discuss the origin of an optical effect in lithium iron phosphate (LFP) battery cathodes, which depends on the electrical charge transferred into the battery. Utilizing indium tin oxide (ITO) as an electrode additive, we were able to observe a change in reflectivity of the cathode during charging and discharging with lithiation and delithiation being clearly visible in the form of lithiation fronts. Further investigations using in situ video microscopy and in situ Raman spectroscopy on test cells with an optical window indicate that ITO additionally acts as an electrochromic marker within the LFP cathode. This enhances the optical effect due to local potentials around the lithiation fronts, which enables the voltage-dependent reflectivity of the ITO to be visible in the LFP cathode. Structural analysis with scanning electron microscopy and X-ray crystallography is presented as well. The observed effect allows for novel battery research methods and for a possible commercial application as a sensor for state-of-charge estimation.

Microscopic Behavior of Active Materials Inside a TCNQ-Based Lithium-Ion Rechargeable Battery by in Situ 2D ESR Measurements

Real-time spectroscopic measurements in rechargeable batteries are important to understand the electrochemistry of the batteries at the molecular level and improve relevant functionalities. We have applied in situ two-dimensional (2D) electron spin resonance (ESR) spectroscopy to a well-known organic lithium-ion battery, which is composed of 7,7,8,8-tetracyanoquinodimethane (TCNQ) as the cathode-active material and a lithium metal anode electrode. The TCNQ rechargeable battery is suitable for investigating electrochemistry in the battery in terms of behavior of electron spin at microscopic levels on both the cathode and anode electrodes. We have discussed two-stage oxidation/reduction reactions of TCNQ, Li deposited/stripped process and their resulting dendritic and/or mossy microstructures, clearly elucidating the cause of the cell capacity degradation upon the charge-discharge cycles. The observed in situ ESR spectra showed that the degradation of the cell capacity was due to the elution of the active molecules, which caused the increase of ion conductivity by the substitution of the electrolyte solution for the adsorbed active materials on the conductive carbon surface. To discriminate paramagnetic species during the charge-discharge process, the generalized 2D correlation spectroscopy has been applied to characterize time-dependent in situ ESR spectra. The correlation analysis with in situ ESR helps us identify the paramagnetic species occurring in the battery cell in a straightforward manner.

EPR Imaging of Metallic Lithium and its Application to Dendrite Localisation in Battery Separators

Conduction Electron Paramagnetic Resonance Imaging (CEPRI) is presented as a sensitive technique for mapping metallic lithium species. The method is demonstrated using different samples that are either thick or thin compared to the microwave skin depth. As a thin sample, microstructured metallic lithium deposits in a lithium-ion battery (LIB) separator were analysed, illustrating the capabilities of CEPRI by obtaining a high-resolution image with an image resolution in the micrometre range. Limitations and intricacies of the method due to non-linear effects caused by the skin effect are discussed based on images of surface patterns on thick metallic lithium samples. The lineshape of the EPR spectrum is introduced as a proxy to determine the suitability of CEPRI for the quantitative visualisation of metallic lithium deposits. The results suggest that CEPRI is particularly suited to analyse the spatial distribution of microstructured Li that forms during charging and discharging of LIB cells, including the localization of the point of failure in the case of an internal cell short circuit caused by dendrites.

A study of MR signal reception from a model for a battery cell

Number of NMR/MRI studies on batteries is rapidly increasing in the past decade. As the test batteries designed for the studies contain metal parts such as electrodes and lead wires as well as other conductive parts (electrolyte), which all present obstacles for good MR signal reception, understanding of the role of battery design and of battery interactions with magnetic field is of a key importance for a successful performance of the experiments. For the study, five different samples mimicking a real battery cell were made. All the samples had two parallel copper electrodes separated by a gel layer, however, they differed in electrode thickness, gel conductivity and separation between the electrodes. The samples were inserted in an MRI magnet in different orientations with respect to magnetic fields B₀ and B₁ and scanned with the spin-echo and single point imaging methods in 2D and 3D (spin-echo only). The performed experiments confirmed that the main reason for poor MR signal reception from a test battery are RF-induced eddy currents. These were found stronger with the sample with the smaller distance between the electrodes. The effect of RF-induced eddy currents was efficiently suppressed when the sample was oriented with the electrodes parallel to the B₁ field. However, in the orientation there were still susceptibility effects that caused a signal voiding in a narrow region near the electrodes. The susceptibility effects were found lower with the sample with thin electrodes and the non-conductive gel. The results of the study can help optimizing test battery and capacitor designs for NMR/MRI experiments.