A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis (original) (raw)

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Open AccessEditor’s ChoiceReview

by

Ximena Carolina Acaro Chacón

,

Stefano Laureti

,

Marco Ricci

and

Gregorio Cappuccino

*

Department of Informatics, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Arcavacata, Italy

*

Author to whom correspondence should be addressed.

Submission received: 8 September 2023 /Revised: 28 October 2023 /Accepted: 1 November 2023 /Published: 3 November 2023

Abstract

:

Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of health accurately, as well as estimating a range of relevant parameters in order to ensure their safe and efficient use. In this framework, non-destructive inspection methods play a fundamental role in assessing the condition of lithium-ion batteries, allowing for their thorough examination without causing any damage. This aspect is particularly crucial when batteries are exploited in critical applications and when evaluating the potential second life usage of the cells. This review explores various non-destructive methods for evaluating lithium batteries, i.e., electrochemical impedance spectroscopy, infrared thermography, X-ray computed tomography and ultrasonic testing, considers and compares several aspects such as sensitivity, flexibility, accuracy, complexity, industrial applicability, and cost. Hence, this work aims at providing academic and industrial professionals with a tool for choosing the most appropriate methodology for a given application.

1. Introduction

Batteries have revolutionized industries in several ways, radically changing electronics by enabling portability and mobility, i.e., making medical devices, GPS systems, and remote sensor technologies more accessible and versatile. Aside from consumer applications, batteries play a fundamental role in the energy storage and electric vehicle (EVs) industries. In these applications, secondary batteries can contribute significantly to decarbonization as they can be used for smoothing the fluctuations of renewables, and for the development of more and more accessible and high-performing transport media at zero carbon emissions.

Although other technologies exist for energy storage applications [1], Lithium-ion batteries (LIBs) have become the predominant technology thanks to a good trade-off between fast-charging capability and higher cycle life and energy density compared with other commercially available mature technologies [2,3,4,5,6].

Despite their numerous advantages, the safety and durability of LIBs must be considered carefully. The first challenge is the development of fast and accurate detection technologies for defects that emerge during production, such as surface defects and defects in the electrode plates that might affect the safety and performance of the batteries. Surface defects are mainly caused by damage to raw materials or accidental bumps. Defects in the electrode plates play a detrimental role in battery capacity and service life, often resulting in internal battery malfunction. Potential short circuit and pole piece defect detection must be monitored and identified as well [7].

During LIBs ordinary operation, safety concerns are related to the possibility of overheating and, in extreme cases, to the risk of fire or explosion [8]. Hence, it is crucial to implement proper safety measures in the design, manufacturing, and in the second life of LIBs, through a proper design of thermal management systems or short circuit protection [9,10]. Regarding their durability, LIBs can face degradation over time due to repeated charge–discharge cycles. This might affect their charge retention capacity and their lifespan. Hence, ongoing research is being conducted to improve these aspects through advancements in materials, electrode design, and battery management systems. LIB development focuses on improving their efficiency by using environmentally friendly materials [11].

During the life cycle, batteries can fail due to various factors such as manufacturing errors, abuse conditions, or degradation. Tests have been developed to simulate the mechanical and thermal abuse loads that batteries might encounter during their use [12,13]. New methods are currently being employed in the EV industry that allow for constant monitoring of battery conditions.

From the above discussion it turns out that the faithful estimation of LIBs’ global state of health (SOH) is crucial for guaranteeing effective battery management and a safe and reliable operation. Moreover, SOH should be estimated without damaging or triggering future failures of the battery, hence the need for non-destructive testing (NDT) techniques [14,15,16].

NDT refers to a range of methods for evaluating and localizing anomalies such as imperfections, corrosion, deformation, discontinuities, external and internal cracks, etc., during the production and life cycles of LIBs, without compromising the original part, in accordance with the applicable standards [17,18,19]. In recent years, significant steps have been made in the development of accurate, non-invasive, and reliable methods for the estimation of battery performance. One of the diagnostic tools to assess a battery’s state and determine if it is operating optimally or if it requires maintenance or replacement is based on the evaluation of micro-health parameters [20].

The NDT of LIBs can be classified into several categories. The commonly accepted taxonomy is based on their underlying physical principle of measurement, e.g., electromagnetic waves, thermal waves, mechanical waves, etc.

In this article we will discuss the primary NDT techniques employed for LIB monitoring and evaluation, these being electrochemical impedance spectroscopy (EIS), infrared thermography (IRT), X-ray computed tomography (XCT), and ultrasonic testing (UT). EIS can provide important information on the LIBs’ electrical properties and can be combined with imaging or data-driven methods to gather a comprehensive view on a battery’s SOH. IRT results in a visual representation of the temperature distribution, allowing for quick qualitative analysis and identification of anomalies or defects. UT has become more popular in recent years for the evaluation of SOC and SOH in LIBs and can provide accurate thickness measurements and characterization of material properties.

The objective of this research is twofold. First, the aim is to review the existing NDT methods and measurement schemes for examining batteries, providing the reader with a view on the basic experimental setup for each technique. Secondly, this work aims at aiding in choosing the most suitable measurements techniques, considering recent findings and the required methodology, as well as common issues faced. The sensitivity, the estimation parameters, and the complexity of the experimental setups are some of the aspects examined in the following analysis.

2. Electromechanical Impedance Spectroscopy (EIS)

EIS is based on the measurement of the battery impedance over a range of frequency values. Impedance is a measure of the opposition of a battery over the electrical current flow as a function of the input frequency, i.e., it is the AC counterpart of the resistance in a DC circuit. EIS is used to estimate several battery parameters that are related to the SOC, such as internal resistance, capacity, and time constant [21,22]. The remarkable aspect of EIS is the ability to provide insight into both the intrinsic characteristics and the surface properties of a system, leveraging parallels to circuit elements.

Figure 1 shows a basic experimental setup for the EIS test, which makes use of an impedance analyzer connected to the electrodes and excites them via an AC voltage/current input in a range of frequency values. In general, a four-electrode cell is used, but in practical applications on commercial cells, a two-electrode configuration is the only one that can be employed, although this leads to a less-precise control of the potential across the electrochemical interface of the cell [23,24].

According to the systems theory, the LIB is here considered to be a black box and the response to AC potential or current signals is retrieved over a range of frequencies. The value of the impedance is computed from the mentioned system’s output over a set of frequency values, thus resulting in a spectroscopy method. Practically speaking, this means that the diffusion coefficients, kinetic parameters, and electrolyte resistance of the LIB can be retrieved using a single measurement. The impedance is calculated according to Equations (1) and (2). Equation (3) shows how the imaginary (reactance) and real (resistance) parts of the impedance can be used to compute the phase angle, from which meaningful parameters can be inferred. The values of both the resistance and the reactance can also be plotted against each other, i.e., the so-called Nyquist plot, and this can provide further information about the LIB.

In Equations (1)–(3), V t stands for the potential as a function of time t, I t is the current, V ^ the amplitude of voltage, I ^ the amplitude of current, ∅ is the phase shift, Z 0 is the magnitude of impedance, and Z′ and Z″ the reactance and resistance, respectively.

As a final remark, note also that galvanostatic EIS is frequently applied to LIBs as well [25].

Z = V t I 1 = V ^ sin ( w t ) I ^ sin ( w t + ∅ ) = Z 0 sin ( w t ) sin ( w t + ∅ )

(1)

Z 0 = ( Z ′ ) 2 + Z ″ 2

(2)

It must be stressed that EIS data can be used to infer and/or select suitable equivalent circuit models (ECMs) to switch between the mentioned black box approach with a large number of free parameters to smaller, more interpretable, yet controllable, ones. The choice of a given ECM model to describe the behavior of LIBs is not an easy task and it depends on several factors, such as the dynamic range, working conditions, and the battery type (LiPo, LiFePO4, etc.), as they have different electrochemical characteristics and impedance behaviors, performance at low and high frequencies, the electrochemical components such as electrodes, electrolytes, and separators, the specific application, and eventually, the accuracy to be reached [26,27]. In the framework of battery research and development, highly accurate modelling is sought, while in other processes such as control applications, a simplified representation may be sufficient. To establish accurate circuit models, a range of proper electrical components, e.g., capacitors, resistors, inductors, and diodes, representing the overall system should be used. In Figure 2, we present an example provided by [28] of ECM that can be used to model a LIB, which is based on the most elementary half-cell system. In the shown model, EIS is used to retrieve the following parameters:

-

Rb corresponds to the internal resistance of the bulk materials. When a battery is cycled, the electrolyte is gradually depleted, and microcracks may form within the electrode materials. A decrease in SOH is typically associated with an increase in Rb;

-

RSEI and CPE are the resistance and capacitance of the solid electrolyte interphase layer;

-

W is the Warburg impedance and it is related to the diffusion of ions;

-

Rct is the transfer resistance, related to the electrochemical reaction kinetics, which change based on the surface coating, phase transition, band gap structure, and particle sizes. Rct is found to be correlated with SOC changes.

Furthermore, the Nyquist plots depicted in Figure 2, taken from the same work examined above, can exhibit abrupt variations in their trends due to temperature changes. For instance, higher temperatures might result in more pronounced semicircles or shifts in impedance values, so that these can be related to alterations in the battery’s electrochemical behavior.

It is important to note that there is no ECM that can be adapted to all types of batteries, though it can be customized depending on the characteristics and specific applications [29,30,31]. As a matter of fact, according to the properties of the electrochemical cell, a customized circuit model can be created by incorporating or excluding electrical components from an existing one. This is just a snapshot, but it gives an insight into the importance of choosing appropriate ECMs and the difficulties related to such a decision. Once an ECM has been selected and an EIS conducted, it is possible to evaluate fundamental properties that are crucial for understanding and optimizing the performance of batteries through data modelling and parameter analysis.

3. Infrared Thermography (IRT)

IRT is extensively used for quality control and process monitoring in a plethora of industrial applications [41]. Infrared cameras rely on the principle of heat transfer through radiation, and they contain a focal plane array composed of elements capable of capturing the infrared spectrum emitted by the surfaces of the objects. The impinging radiation is converted into digital data, which are then displayed as an image, and are visualized within the visible spectrum in false color [42,43,44]. Some cameras are calibrated using radiometric references to accurately record and display measurements in specific units. These cameras are endowed with various sensor types and pixel resolutions in order to capture specific infrared wavebands at the needed level of spatial detail.

For the analysis of LIBs, active thermography systems are increasingly used, in particular the pulsed IRT by exploiting flash lamps. In pulsed IRT, the excitation source is the flashlight. The surface of the battery is exposed to a brief, intense heat pulse produced by the flashlight, see Figure 3. If the LIB’s internal structure is flawless and relatively homogeneous, the heat diffuses at the same speed throughout a section of it, thus resulting in a homogenous distribution of the LIB surface temperature. A flaw such as a delamination, a vacancy, or the inclusion of a foreign body, affects the heat diffusion locally, resulting in temporal variations or discrepancies of the surface temperature that can be captured by the IR camera. A computer, equipped with real-time image signal processing and analysis, grabs a time sequence of thermal signals (one for each pixel of the camera sensor), revealing the propagation of thermal energy from the surface to the interior of the target and vice versa.

On the other hand, in the passive IRT approach, the heat source is the battery itself. Heat losses within a battery occur from multiple sources, including the entropy change resulting from electrochemical reactions and the Joule’s effect, or ohmic heating, caused by current flow across internal resistances and overpotential, see Equation (4). In certain electrochemical combinations, additional electrical energy losses occur, leading to heat generation, for e.g., when attempting to overcharge a fully charged cell.

The first term in Equation (4), represents the heat generation attributed to the reversible entropy change due to the electrochemical reactions within the cell. The second term, accounts for the heat generation resulting from irreversible effects such as ohmic heating and other factors within the cell [45]. Based on these terms and on the discussion above, if the thermal performance of battery pack is not taken into consideration, the rising temperature can cause severe damages to it.

q = − I T d E d T + I ( E − V )

(4)

In Equation (4), q is the heat generation, I the current, T the temperature, the term d E d T is the temperature coefficient, E the open-circuit potential, and V the cell potential.

4. X-ray Computer Tomography (XCT)

XCT is used to inspect the internal structure and to gain insight into the mechanical stability limits of the battery components. XCT is an imaging technique that makes use of X-rays to create detailed 3D images of an object’s internal structure. It works by taking multiple X-ray images from different angles and leveraging advanced algorithms to reconstruct a 3D image of the object. This technique is widely used in medical imaging to diagnose diseases, injuries, and in other fields such as engineering and materials science to inspect the internal structure of objects without provoking any damage [59,60].

XCT with a resolution on the nanoscale (nano-CT) is extremely desirable for the purpose of characterizing the inner interface [61], i.e., when used to create 3D representations of individual electrodes or the entire cell, and it can output 2D projection images of objects from multiple angles of incidence [62]. It enables the non-destructive inspection of the cell, providing abundant structural information at the micrometer or sub-micrometer levels. This technique can reveal the presence of cracks, voids, and other defects that may affect the performance and safety of the battery. XCT can also be used to study the distribution of active materials in the battery and to monitor changes in its internal structure during charge and discharge cycles. In a recent study, automated registration based on normalized mutual information was applied to align data derived from ultrasonic and radiographic inspections of thin, lithium metal pouch-cell batteries. The quality of the registration was quantified in terms of computational resources and spatial accuracy. In this case, the radiographic data resolution was much higher than the ultrasonic data, and the registration technique was able to align the two datasets accurately. This demonstrates the potential of XCT and other NDT methods to investigate oversized objects such as complete vehicles [63], providing an analysis related to the presence of defects on different automotive components. In addition, it is shown that XCT can provide info about the quality and assembly of components, as well as joining techniques such as welded, adhesive bonded, and sealed connections.

In the electrical vehicle industry, XCT can be used to analyze the internal state of the magnet wires that are needed to meet rigorous usage requirements including heat-resistance property, excellent workability, and insulation properties [64].

In the field of battery research, it is used to study the internal structure of LIBs [65]. Recently, researchers have developed a phosphate solid-state LIB prototype, in which the volume changes during charge and discharge in the materials are minimal according to the theory [3,66]. To understand and optimize the solid-state battery’s systems, researchers studied organic–inorganic composite electrolytes and sintered ceramic electrolytes to gain information on the mechanical stability limits [67,68] using XCT. Figure 4 depicts a basic scheme of the method for XCT.

5. Ultrasonic Testing (UT)

In recent years, UT has also been applied to the field of LIBs evaluation. The use of ultrasound in LIBs is mainly focused on detecting defects and on the real-time monitoring the state of the battery, i.e., its SOC and SOH [85,86]. It is based on the emission and reception of acoustic waves at a frequency range above the audible one, which propagates through the battery material and is reflected back from at the interfaces between components such as electrodes and electrolytes.

The pulse-echo and through-transmission methods are two commonly used ultrasound methods for LIBs. In pulse-echo, a single ultrasonic transducer is placed on the surface of the battery and the ultrasonic waves are transmitted through its inner volume, see Figure 5a. The waves are then reflected back to the transducer when hitting any discontinuities, i.e., any considerable change in the density and speed of sound occurring at the interfaces between different layers of the battery. By analyzing the reflected waves, it is possible to detect and locate defects in the electrodes, in the separator, and in other internal components. The method is also useful for detecting defects in the packaging such as leaks or cracks. The through-transmission method makes use of two transducers, one on each side of the battery. A transducer is employed to send ultrasonic waves through the battery, while the other one acts as the receiver, see Figure 5b. By analyzing the transmitted waves, it is possible to detect defects in the battery. It can detect shallower defects compared with the pulse-echo, such as shallow microcracks and voids.

Although more complex expressions exist depending on the model of wave propagation and the type of elastic constant employed, Equation (5) expresses the ultrasonic longitudinal wave velocity for a homogeneous elastic material, given its Young’s modulus (E) and Poisson’s ratio (σ) [87].

where:

ρ is the material density;

C i j is the material elastic constant.

The acoustic time-of-flight (ToF) and the signal amplitude are two parameters used in ultrasonic testing of LIBs [87]. The ToF measures how long it takes for an ultrasonic signal in pulse-echo mode to return to the receiver. A change in the ultrasonic velocity and/or thickness is thus represented by the ToF. On the other hand, the input acoustic energy, gain, transducer positioning, and contact pressure between the transducer and the LIB are only a few of the variables that may affect signal amplitude, and this should all be taken into account when using it as a proxy. Furthermore, a physical shift in the location of a fault, discontinuity, or interface towards or away from the transducer owing to material expansion or contraction could cause an amplitude peak variation over a specific ToF range, a signal delay, or an extended ToF value [87].

A change in the physical characteristics of the materials at the interface that causes an increase/decrease in the acoustic impedance mismatch at the interface could be the reason for a change in the peak amplitude. For a given material with a fixed Vp the distance L between the emitter and the receiver increases with increasing ToF values, see Equation (6) [88,89], with ρ being the material density.

In addition, powerful ultrasounds can be used to trigger various acoustic emission (AE) events during the charge and discharge cycles. The elastic waves are found to be related to the battery’s condition and are detected by the AE transducer [84,85]. This makes it possible to record more data across a wider frequency range than that of the impinging ultrasonic wave for a more efficient SOH estimation. According to the findings, the root mean square (RMS) of the AE signal can serve as a proxy for SOH, and the frequency range between 270 and 300 kHz can be used to estimate it accurately during discharging [90].

6. Discussion

The accurate estimation of various parameters, such as charge/discharge characteristics, structural changes, heat generation, etc., are crucial for ensuring the safe and controlled usage of LIBs across diverse applications. In this framework, NDT methods have proven to be invaluable tools for assessing LIBs in both research and industrial contexts. Their versatility, sensitivity, cost-effectiveness, in operando evaluation capabilities, and accuracy enable the acquisition of detailed information about battery condition and performance, without causing any damage. These methods contribute significantly to enhancing battery safety, reliability, efficiency, and real-time monitoring, all of which are vital for advancing energy storage technologies. Thus, the main goal of this review paper is to serve as a foundational resource for professionals seeking to apply these techniques in real-world applications by conducting an accurate literature review based on the estimation parameters and instruments used in four NDT different techniques, i.e., electromechanical impedance spectroscopy (EIS), infrared thermography (IRT), X-ray computer tomography (XCT), and ultrasonic testing (UT). Note that this set of techniques has been chosen as they are widely adopted in both research and industrial battery-related applications.

As demonstrated, choosing the most suitable NDT method for evaluating LIBs is a complex and pivotal decision in both research and industrial applications. The choice of an appropriate method depends on the specific application and the characteristics of the LIB under analysis, including material type, defect size, and desired level of accuracy. The following characteristics have been analyzed in this review:

• Versatility and Effectiveness: the explored NDT methods provide researchers and engineers with a wide range of tools to evaluate LIBs, each offering unique advantages in terms of the information they can provide.
• Cost: it is essential to note that the implementation of an NDT can involve a significant initial investment in equipment and training. Methods such as XCT and IR have relatively high initial costs.
• In Operando Evaluation: the capability to evaluate batteries during their operation is a crucial advantage of NDT technologies. This allows real-time measurements and monitoring of battery performance to be used under real-world conditions.
• Measurement Accuracy: some methods provide more accurate measurements in specific aspects, such as leak detection or structural integrity assessment, while others may be better suited for assessing the state of charge or overall battery health. This aspect has been discussed in detail in the manuscript.

A visual comparison of the abovementioned set of characteristics is depicted in Figure 6. As a general guideline, EIS is considered the most suitable method for its balance between accuracy and equipment costs, and it can provide a range of meaningful parameters depending on the selection of the underlying physical model:

Although sensitive to external factors like environmental temperature or non-uniform heating, IRT is preferred when thermal anomalies should be detected. For inferring the presence of internal structural changes or defects, XCT represents the optimal choice, albeit requiring expensive equipment and specialized personnel. It is primarily used in R&D applications rather than in industrial production for in-line monitoring. UT methods represent a versatile alternative, although the accuracy of results may be limited by environmental factors such as temperature and humidity, and the need for couplants.

7. Conclusions and Future Directions

This work provides a review of recent contributions related to NDT for evaluating LIBs. Today’s NDT methods can detect signs of deformation, leaks, swelling, corrosion, and other visible and non-visible signs of deterioration. These methods can also inspect battery connections, terminals, and internal material damage for potential manufacturing issues. The specific selection of an NDT method may depend on the type of defects to be identified and the production or monitoring environment in which the inspection takes place.

Choosing the appropriate method depends on the application and the type of information required from the battery, such as state of charge (SOC), internal or external defects, state of health (SOH), accessibility, heat generation, and real-time measurements. Future developments in NDT methods, both within the field and in other industrial contexts, may mutually drive advancements in LIBs monitoring.

In a broader context, the future of NDT is undoubtedly going to be shaped by technological advancements, the integration of AI, and a focus on improving the accuracy and efficiency of existing methods. Researchers can contribute to this field by working on AI algorithms, sensor technology, advanced imaging techniques, quantitative NDT, and sustainability initiatives as well.

Another promising advancement is the adoption of advanced data analysis. With the increasing use of sensors and data collection tools, substantial data are generated during NDT inspections. Extracting valuable insights from large data for making more informed decisions about the materials and structures to be tested is a pivotal future direction.

As a conclusion, environmental impact and sustainability are playing an increasingly important role in the choice and further development of NDT processes. Strong research efforts are advisable in exploring ways to reduce waste, energy consumption, and the environmental footprint of NDT processes.

Author Contributions

Conceptualization, X.C.A.C. and G.C.; methodology, X.C.A.C., S.L. and G.C.; validation, X.C.A.C., S.L., M.R. and G.C.; formal analysis, X.C.A.C., S.L., M.R. and G.C.; investigation, X.C.A.C., S.L. and G.C.; data curation, X.C.A.C. and S.L.; writing—original draft preparation, X.C.A.C.; writing—review and editing, X.C.A.C., S.L., M.R. and G.C.; supervision, M.R. and G.C.; project administration, X.C.A.C. and G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Next Generation EU–Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)–project Tech4You–Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and for Research nor the European Commission can be considered responsible for them.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Typical representation of electrochemical impedance spectroscopy (EIS) measurements of a LIB presented in a test setup.

Figure 1. Typical representation of electrochemical impedance spectroscopy (EIS) measurements of a LIB presented in a test setup.

Figure 2. Nyquist plot for a ECM of a LIB half-cell system. Reprinted from [28]—Copyright © 2023 by The Korean Electrochemical Society—CC BY-NC 4.0.

Figure 2. Nyquist plot for a ECM of a LIB half-cell system. Reprinted from [28]—Copyright © 2023 by The Korean Electrochemical Society—CC BY-NC 4.0.

Figure 3. Basic test setup of infrared thermography in reflection mode.

Figure 3. Basic test setup of infrared thermography in reflection mode.

Figure 4. A sketch of a general XCT setup.

Figure 4. A sketch of a general XCT setup.

Figure 5. Ultrasonic testing (a) pulse-echo; (b) through-transmission. Tx and Rx stand for transmitter and receiver transducer, respectively.

Figure 5. Ultrasonic testing (a) pulse-echo; (b) through-transmission. Tx and Rx stand for transmitter and receiver transducer, respectively.

Figure 6. Comparative analysis of the selected NDT methods.

Figure 6. Comparative analysis of the selected NDT methods.

Table 1. Studies on predicting SOH based on EIS.

Table 1. Studies on predicting SOH based on EIS.

Table 2. Studies on predicting state of LIB based on IRT.

Table 2. Studies on predicting state of LIB based on IRT.

Table 4. Studies on predicting SOC of LB based on Ultrasound.

Table 4. Studies on predicting SOC of LB based on Ultrasound.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Chacón, X.C.A.; Laureti, S.; Ricci, M.; Cappuccino, G. A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis. World Electr. Veh. J. 2023, 14, 305. https://doi.org/10.3390/wevj14110305

AMA Style

Chacón XCA, Laureti S, Ricci M, Cappuccino G. A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis. World Electric Vehicle Journal. 2023; 14(11):305. https://doi.org/10.3390/wevj14110305

Chicago/Turabian Style

Chacón, Ximena Carolina Acaro, Stefano Laureti, Marco Ricci, and Gregorio Cappuccino. 2023. "A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis" World Electric Vehicle Journal 14, no. 11: 305. https://doi.org/10.3390/wevj14110305

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