Significant efforts are being made across academia and industry to better characterize lithium ion battery cells as reliance on the technology for applications ranging from green energy storage to electric mobility increases. The measurement of short-term and long-term volume expansion in lithium-ion battery cells is relevant for several reasons. For instance, expansion provides information about the quality and homogeneity of battery cells during charge and discharge cycles. Expansion also provides information about aging over the cell’s lifetime. Expansion measurements are useful for the evaluation of new materials and the improvement of end-of-line quality tests during cell production. These measurements may also indicate the safety of battery cells by aiding in predicting the state of charge and the state of health over the lifetime of the cell. Expansion measurements can also assess inhomogeneities on the electrodes, in addition to defects such as gas accumulation and lithium plating. In this review, we first establish the mechanisms through which reversible and irreversible volume expansion occur. We then explore the current state-of-the-art for both contact and noncontact measurements of volume expansion. This review compiles the existing literature on four approaches to contact measurement and eight noncontact measurement approaches. Finally, we discuss the different considerations when selecting an appropriate measurement technique.

Long-term photovoltaic (PV) module reliability is highly determined by the durability of the polymeric components (backsheet and encapsulation materials). This paper presents the result of experiments on encapsulant degradation influenced by the backsheet permeation properties. Towards this goal, one type of ethylene/vinyl acetate copolymer (EVA) was aged in glass/EVA/backsheet laminates in accelerated aging tests (up to 4000 h for Damp-Heat (DH) and up to 480 kWh/m² for UV and UV-DH combined). The samples contained three backsheets with different permeation properties to examine their impact on EVA degradation. Thermal and chemical characterization shows that the EVA degradation is stronger with the glass–EVA–polyamide (PA)-based backsheet than with the polyethylene terephthalate (PET)-based backsheets. The higher oxygen transmission rate (OTR) of the PA-based backsheet may increase photo-oxidation and aggravating the degradation of EVA in the laminates. Furthermore, FTIR results were used to demonstrate the effect of damp heat exposure on the EVA interfaces, showing an accelerated degradation at the glass–EVA interface. The comparison of accelerated aging stress factors reveals that EVA suffers the strongest chemical and optical degradation when high UV, high temperature and high relative humidity are combined simultaneously.

The identification and location of critical defects inside battery cells before the performance decreases or safety issues arise remain a challenge. This study compares two nondestructive testing methods for the 3D visualization of defects at different depths inside a pouch battery cell: scanning acoustic microscopy (SAM) and X‐ray computed tomography (CT). A manufactured pouch cell with eight electrode sheets is used for this investigation. SAM using a 15 MHz transducer in reflection mode can detect defects at depths of up to four electrode sheets with a lateral resolution of 150 μm in 2 min. CT can locate defects on all eight stacked electrode sheets inside the pouch cell. The CT measurements take about 12.5 h. Both methods can complement each other in detecting defects inside thin pouch cells as an end‐of‐line test after the production or for qualifying individual battery cells for second‐life applications. As an in‐line quality check, SAM has proven to be a cost‐effective and efficient method for detecting defects such as misalignment on stacked electrodes. Both methods have the potential to expand the portfolio of nondestructive quality assurance tests in the production of lithium‐ion battery cells. This contributes to increasing the safety and productivity of battery technology.

Significant efforts are being made across academia and industry to better characterize lithium-ion battery cells as reliance on the technology for applications ranging from green energy storage to electric mobility increases. The measurement of short-term and long-term volume expansion in lithium-ion battery cells is relevant for several reasons. For instance, it provides information about the quality and homogeneity of battery cells during charge and discharge cycles, as well as aging over it’s lifetime. The expansion measurements are useful for the evaluation of new materials and the improvement of end-of-line quality tests during cell production. These measurements may also indicate the safety of battery cells by aiding in predicting state of charge and state of health over the lifetime of the cell. Expansion measurements can also assess inhomogeneities on the electrodes and defects such as gas accumulation and lithium plating. In this review, we first establish the known mechanisms through which short term and long term volume expansion in lithium-ion battery cells occurs. We then explore the current state-of-the-art for both contact and non-contact measurements of volume expansion. This review compiles existing literature to outline the various options available to researchers aiming to make ex situ volume expansion measurements by doing post mortem analyses on individual components and in operando measurements on entire battery cells. Finally, we discuss the different considerations when selecting an appropriate measurement technique. The selection of the optimal method for measuring battery cell expansion depends on the objective of the characterization, duration, required resolution, and repeatability of results. Costs and required space for the measurement equipment are also considered.

The power degradation and failure of photovoltaic (PV) modules can be caused by changes in the mechanical properties of the polymeric components during the module lifetime. This paper introduces instrumented nanoindentation as a method to investigate the mechanical properties of module materials such as polymeric encapsulants. To this end, nanoindentation tests were carried out on ethylene vinyl acetate (EVA) surfaces, which have been separated from the glass panel. Two types of time-dependent indentation cycle modes, the time domain (creep mode) and frequency domain (dynamic mode) were performed to determine the viscoelastic behavior. For each mode, a corresponding model was applied to calculate the main mechanical properties. The general capability of nanoindentation as cross-linking determination method is investigated with the methodological advantages over bulk mechanical characterization methods. A large number of Glass/EVA/Backsheet laminates were built using different lamination conditions resulting in different degrees of curing. Both indentation modes indicate good modulus sensitivity for following the EVA crosslinking in its early stages but could not reliably differentiate between samples with higher EVA branching. Additional dynamic mechanical analysis (DMA) characterization was used as an established method to validate the indentation measurements. Both nanoindentation and DMA tensile mode produce similar quantitative viscoelastic responses, in the form of the damping factor parameter, demonstrated for three different frequencies at room temperature. A statistical study of the data reveals the advantages for the investigation of multilayer PV laminates by using nanoindenation as a surface method while also being applicable to field aged modules.

Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily on electrical measurements for identifying defective cells. However, it is possible that not all pertinent information is encoded within the electrical measurements alone. Reversible expansion in lithium-ion cells is an indicator of lithiation within the cell, while irreversible expansion is a consequence of the ageing process; unexpected expansion may indicate the presence of undesirable defects. By measuring expansion in addition to electrical measurements, we aim to make better and faster quality predictions during end-of-line testing, thereby facilitating the early detection of potential defects. To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. Training these networks requires large training datasets, which are expensive to produce. In this paper, we demonstrate a first-order physical modelling approach for generating synthetic data to pre-train artificial intelligence algorithms that perform anomaly detection on lithium-ion battery cells at the end-of-line. The equivalent circuit model used to generate voltage curves could be fit to real data with a mean absolute error of less than 1%, and the expansion model could be fit to a mean absolute error of less than 2% of the measured values. By pretraining the artificial intelligence network using synthetic data, we can leverage existing physical models to reduce the amount of training data required.