Lithium-ion battery cell formation: status and future directions towards a knowledge-based process design

Felix Schomburg ^a, Bastian Heidrich ^b, Sarah Wennemar ^c, Robin Drees ^def, Thomas Roth ^g, Michael Kurrat ^de, Heiner Heimes ^c, Andreas Jossen ^g, Martin Winter ^bh, Jun Young Cheong *^ai and Fridolin Röder *^a
aBavarian Center for Battery Technology (BayBatt), Weiherstr. 26, 95448 Bayreuth, Germany. E-mail: jun.cheong@uni-bayreuth.de; fridolin.roeder@uni-bayreuth.de
^bMEET Battery Research Center, Institute of Physical Chemistry, University of Münster, Corrensstr. 46, 48149 Münster, Germany
^cProduction Engineering of E-Mobility Components RWTH Aachen University Bohr 12, 52072 Aachen, Germany
^delenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Schleinitzstraße 23, 38106 Braunschweig, Germany
^eBattery LabFactory Braunschweig, Technische Universität Braunschweig, Langer Kamp 8, 38106 Braunschweig, Germany
^fFraunhofer Research Institution for Battery Cell Production FFB, Bergiusstraße 8, 48165 Münster, Germany
^gTechnical University of Munich, School of Engineering and Design, Department of Energy and Process Engineering, Chair for Electrical Energy Storage Technology, Arcisstraße 21, 80333 Munich, Germany
^hHelmholtz Institute Münster, IEK-12, Forschungszentrum Jülich GmbH, Corrensstr. 46, 48149 Münster, Germany
ⁱDepartment of Chemistry, University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany

First published on 13th February 2024

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Felix Schomburg

Felix Schomburg is a research associate and PhD candidate at the University of Bayreuth and Bavarian Center for Battery Technology. He obtained a MSc at the TU Braunschweig in 2017. Afterwards he worked on several projects as a software developer and engeering consultant in the automotive industry. Since joining Prof. Röder's group in 2020, his research focuses on model-based investigations of formation procedures and their influence on the ageing behavior of lithium-ion batteries.

Jun Young Cheong

Jun Young Cheong is currently a group leader at University of Bayreuth, serving both Department of Chemistry and Bavarian Center for Battery Technology. Prior to joining University of Bayreuth, he was a staff engineer at Samsung SDI, specializing on electrode processing. He received his BS degree, MS degree, and PhD degree in materials science and engineering from KAIST in Korea (2014/2016/2020, respectively). His current research topic includes the synthesis of functional materials, energy storage and conversion, and in situ characterizations. Jun Young has published more than 80 articles and holds numerous patents and patent applications.

Fridolin Röder

Fridolin Röder is assistant professor for methods for battery management at the University of Bayreuth. After a PhD in 2019 at the TU Braunschweig (Germany) and a visiting scholar at MIT in 2015, he had a junior research group on multiscale development of batteries at TU Braunschweig until 2020. For more than 10 years he has been active in the field of modeling and characterization of energy systems with a special focus on batteries.

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Broader context Driven by the transition towards renewable energy sources and electromobility, the demand for cost-effective and sustainable batteries is continuously growing. Simultaneously, the demand for higher energy densities entails safety risks and challenges in maintaining long lifetime. Addressing these challanges is crucial to overcome driving anxiety and concerns against novel technology. These issues are closely linked to the formation process, which is essential to establishing stable interphase layers and utilizing materials outside the electrolyte's stability window. Challenges intensify with new materials like high-voltage cathodes and lithium metal or silicon anodes. In addition, sodium-ion technologies, seen as promising alternatives, face hurdles in establishing stable interphase layers due to high solubility. In all these technologies, a well-designed formation process is pivotal, yet complicated due to many interdependent influencing factors. This review highlights the need for a knowledge-based process design to ensure safe and durable batteries. By reviewing the status, outlining theoretical and experimental methods, the necessary foundation is laid.

Introduction

Since their first commercialisation by Sony in 1991, lithium-ion batteries (LIBs) have been used in various applications ranging from consumer electronics to electric vehicles.^1,2 Reflecting the tremendous impact of LIBs on all aspects of people's life, the Nobel Prize in Chemistry was awarded to Prof. John B. Goodenough, Prof. M. Stanley Whittingham and Prof. Akira Yoshino for the development of LIBs.³ The demand for LIBs is set to increase dramatically in the coming years.⁴ At the same time, increasing energy density poses significant challenges to ensuring safety and long lifetime.^5–9 In addition, it is vital to minimise the cost, time, energy consumption and waste associated with the manufacture of LIBs. Hence, a thorough understanding of each production step is essential to facilitate the development of current and next-generation LIBs.^10,11

Formation is the final active process step in LIB cell manufacturing. The process affects the quality of the freshly assembled cells and contributes significantly to the overall cost, accounting for up to 33% of the production cost.^12,13 Formation typically involves multiple charge and discharge cycles. The formation cycling is required to activate the materials and establish protective interphase layers at the electrochemically active interfaces that enable a stable operation of the cell outside the electrolyte's electrochemical stability window. The drive to increase energy density will place higher demands on the formation process, as high-capacity and high-voltage materials utilize a wider voltage window and are subject to significant volume changes.

The formation process must be carried out carefully, as it is linked to influence key electrochemical and safety properties of LIB cells. However, overly cautious formation is time consuming and therefore at odds with efforts to accelerate battery cell production and reduce overall costs. There is a significant potential to improve the formation process to optimise cell quality and reduce overall costs.¹⁴ The industry uses highly sophisticated procedures, but details are guarded as they are considered essential intellectual property of the manufacturers. Despite its importance, the formation process is not well understood due to the limited number of comprehensive studies in academia.

This review comprehensively summarises, examines and analyses the various facets of the formation process. It describes the formation process, presents the current state of knowledge on the various influencing factors, and highlights the most relevant experimental and theoretical methods for future research. In the light of future battery technologies aimed at higher energy density, a summary and suggestions for the further development of the formation process are presented. This review contributes to the overall understanding of formation and provides suggestions for methods and open questions being relevant to enable a knowledge-based process design for current and future battery technologies.

Description of the cell formation process

Formation within the LIB production

The production of LIBs can be divided into four parts: electrode production, cell production, cell conditioning† and system assembly.¹³ For battery cell production, the system assembly is excluded. Typical design objectives are high energy density, high power density, low production cost, long lifetime and safety. Battery cell formation is part of cell conditioning. Cell conditioning also includes various quality test steps and quality sorting.

The purpose of the formation process is to electrochemically activate the cell so that its subsequent performance is positively influenced. The formation process is critical for a number of reasons. Firstly, formation is the last process step in the production of a battery cell and any scrap that is produced during formation causes the loss of value of all previous process steps.¹³ Secondly, the formation process is very time consuming and energy intensive.^15,16 Finally, the process can have a significant impact on cell performance metrics such as capacity, power capability, lifetime and safety.

The formation process is often a production bottleneck due to the relatively low currents applied to individual cells.¹⁷ Achieving high throughput requires significant investment in equipment and cell formation space. Manufacturers therefore aim to reduce the formation time.¹⁸

Formation time is highly dependent on cell quality requirements and cell-to-cell variation. For batteries with lower quality requirements, relatively short formation times are possible. Here the quality controls performed as part of the formation/conditioning phase are often less sophisticated.¹⁸ Therefore, the progress of formation and individual cell quality is often not known. In applications such as electric vehicles (EV), cell quality and cell-to-cell variation are very important because the lowest cell capacity limits the total usable battery capacity. Further cell-to-cell variation within a battery system promotes ageing and is a safety concern.¹⁹ Therefore, battery cell production for such applications requires much more sophisticated formation procedures, including quality management measures. The formation time is much longer and may include various intermediate cell tests.

The energy consumption of the formation process is significant.¹⁶ According to Kurland et al., Northvolt expects the cell production at Northvolt Ett to use 20% of the total production electricity consumption for the formation step.²⁰ In general, the electricity consumption can vary significantly between laboratory scale and industrial plants. According to Erakca et al. this is because recuperation is commonly used in industrial production facilitities.²¹ Recuperation is the process of recovering energy released during battery discharge. Consumption also depends on the energy consumption of the electrical equipment, which is usually higher for laboratory equipment. Thus, the energy consumption of the formation process can vary from 0.6 Wh of Wh cell capacity produced in pilot plants to 42.6 Wh of Wh cell capacity produced in laboratory cell productions.²¹

The formation process is influenced by several factors. These include material and cell design, as well as the electrochemical conditions during formation. Some of these factors, such as electrode properties or moisture contamination, also depend on previous manufacturing steps. Therefore, the entire process chain and its interdependencies must be considered when optimising the formation process.^22,23

Description of the process

Before the formation process, the cell is filled with electrolyte. This places the active and inactive materials of the electrode in direct contact with the electrolyte, as shown on the left in Fig. 1. Electrochemical reactions and passivation of the interface take place only if the electrolyte and the electrode are in direct contact. Therefore, complete wetting, i.e. filling of the electrode pores with electrolyte, is required for formation.^24–27 As the battery is initially in a completely discharged state, there is limited interaction between the electrodes and the electrolyte. The start of formation can be defined as the point at which the cell is electrically connected, and the first charge is initiated.

During formation several processes take place that lead to significant changes in the cell properties. The main processes observed are shown in the centre of Fig. 1. These processes include solid electrolyte interphase (SEI) formation,²⁸ cathode electrolyte interphase (CEI) formation,²⁹ structural changes in the active material,^30–32 copper (Cu) corrosion^33–37 and aluminium (Al) dissolution.^36,38–40

The interfacial layers formed on the negative and positive electrodes can be expressed in terms of low unoccupied molecular orbitals (LUMO) and high occupied molecular orbitals (HOMO), as shown in Fig. 2.^41,42 Both electrodes can reach potentials that exceed the electrochemical stability window of the electrolyte triggering electrochemical side reactions.

Among the processes during the formation, SEI formation is the most widely studied. This process is initiated by an irreversible electrochemical reduction reaction that consumes electrical charge, lithium (Li), solvent and other electrolyte components at the negative electrode. The reaction products can precipitate on the electrode surface inhibiting further side reactions. The ideal passivation layer exhibits strong electrical insulation, high ion conductivity, mechanical and thermal stability.^43–47 The most commonly reported SEI components are lithium fluoride (LiF),^48–53 lithium carbonate (Li₂CO₃),^48,53–58 lithium oxide (Li₂O),^48,53,55 litihium ethylene dicarbonate (LEDC)^51,52,59 and lithium alkyl (bi)carbonates (ROCO₂Li/RCO₂Li).^50,53,55,58

CEI formation is another side-reaction that is first initiated during formation. The process is initiated by an oxidation reaction that releases electrical charge to the positive electrode.^60,61 Like SEI, CEI inhibits further degradation.⁶² The CEI is thinner than the SEI,^55,63,64 suggesting a lower magnitude of charge transfer due to CEI formation compared to SEI formation. Typical CEI components are LiF,^55,65–68 Li_xPO_yF_z,^65,67–69 Li₂CO₃^{65–67,69,70} and ROCO₂Li.^66–71 For both interface processes, reaction products can also be gaseous species.^72–74

Despite the interfacial processes, structural changes have also been observed during the first cycles. Agglomeration of nanoparticles,⁷⁵ particle cracking^76–81 or exfoliation of particles^82–84 can lead to significant changes of the active material. Structural changes have also been observed at the material level. Here, irreversible rearrangement of the material can cause significant heat release and voltage hysteresis during the first cycle.^31,85–87

After the end of formation, the cell characteristics should stabilise. A commonly used indicator of this stabilisation is the coulombic efficiency (CE), which approaches 100% towards the end of formation.¹⁸ CE is therefore useful in defining a target criterion for the end of formation.

After formation, the cell properties have changed significantly. Firstly, the formation results in a loss of cyclable Li, which has been immobilised mainly by the side reactions forming SEI.^88–90 Secondly, the loss of cyclable Li results in a reduced cell capacity⁹¹ and a relative shift in the electrode open circuit potentials.⁹² Thirdly, there is an increase in interfacial resistance.^93–96 In addition, the grown passivation layer and structural changes are likely to lead to a decrease of porosity. Further, electrode restructuring leads to a decrease in conductivity as seen in ageing studies.^97–99 Similarly, the electrolyte composition is changed after formation,^100,101 which impacts the Li-ion diffusivity and ionic conductivity.^99,102,103

All of these changes can have a significant impact on cell performance in terms of ageing,^104–106 power capability,^107–111 safety,^112–118 energy consumption,^12,13,17 cost^12,13,17 and CO₂ emissions^12,13 as shown in Fig. 1.

Procedures

To influence cell quality and process time, several procedures are possible during formation. The formation procedure is initiated after the initial wetting and includes several possible steps, i.e. pre-charging, formation cycling, degassing, 2nd wetting and ageing. The procedure may vary depending on the cell format and size.^119,120 A possible procedure, applicable to hard-case cell formats and high-quality requirements, e.g. for the automotive industry, is described below. The process and the steps involved are illustrated in Fig. 3.

Prior to the start of the formation process, initial wetting is carried out. This can be done by a high temperature soaking process, in which the cells are exposed to high temperatures, typically between 30–50 °C, for several hours to change the viscosity of the electrolyte and reduce the time between the electrolyte filling process and reaching the optimum wetting level to start the formation.¹²¹ Formation is initiated by the so-called pre-charging process,¹²² in which a low charging current is applied to the cell until a low voltage, e.g. 1.5 V, is reached. Low currents and voltage limits are necessary to protect the Cu-based current collector from corrosion.¹² The voltage limit during pre-charging can also be increased to a state where most of the gas generation during the first cycle has been completed. This is followed by the degassing step. The gases are usually removed by applying a vacuum. The cell is then sealed.¹²³ Generally, the housing is sealed as early as possible to minimise evaporation of volatile electrolyte components.¹¹⁹ If there is no pre-charging step, degassing is carried out after the formation is complete or before shipping to the customers. After degassing, a second wetting and possibly electrolyte filling process may be added to compensate for any electrolyte loss during pre-charging. This is followed by the formation cycling, which includes at least one and possibly several charge and discharge cycles with a defined current and voltage limit. Finally, the cell is aged. During ageing the cells are stored at a defined state of charge (SOC) and temperature. The ageing process completes cell formation. It also serves as a self-discharge test. In this context, the ageing process can also be considered as part of the end-of-line (EOL) cell quality test since an increased capacity loss during storage indicates unwanted self-discharge.

Without the ageing test, industrial formation procedures typically take less than 20 hours.^4,119 The ageing test alone can take up to three weeks.^12,17,119 For application with low quality requirements, formation procedures are significantly shorter.^13,122,124 Once the formation procedure is complete, further EOL testing is performed.¹²³

The EOL test protocol can vary, but can include electrochemical tests for discharge capacity, CE, internal resistance, self-discharge test if not already covered by ageing process, impedance, as well as leakage, weighing and optical tests. Depending on the material, size, type and format of the cell, the formation procedure varies in terms of sequence, duration, and quantity from one cell manufacturer to another. The main objective is to find procedures, cycling protocols and evaluation methods that reduce the overall process time, while ideally performing formation and cell quality testing simultaneously.

Required equipment

The formation area in a battery cell production plant consists of large formation towers with either individual racks or chamber systems, each with one individually controlled channel per cell.¹²⁴ In these areas, the cells are transported in fully automated bundles by product carriers, which also serve a contact to the formation equipment.¹²⁴ Battery formation equipment, also called battery testers, with the correct power system, often AC–DC or DC–DC stage, is required to carry out the formation protocol. If the cells remain unsealed during formation, the formation must either take place in a dry and clean room or a special type of carrier with a suction device must be used. Such open formation procedures are currently only used for large prismatic cells. For closed cells, formation does not take place in a dry and clean room, but under controlled temperature conditions.

To address the high energy consumption and the move towards greener battery production, new formation equipment with a recuperation function has been developed to use the discharge energy from one cell to charge another. Controlling and precisely delivering power to the formation equipment for an entire Gigafactory is a significant challenge. Recently, Tesla announced an innovation to reduce the cost of the formation process by leveraging the production design to allow a group of cells, rather than individual cells, to undergo this formation process resulting in an 86% reduction in formation investment.¹²⁵ In addition, Müller et al. proposed an optimised formation step for serially connected cells.¹²⁶ Thus, serial formation techniques could potentially reduce processing costs. In principle, parallel connection of cells is also possible. However, this reduces the maximum current that can be applied and is therefore only useful for smaller cell formats.

Influencing factors

The formation process is influenced by process conditions, material and cell design. The process conditions are mostly considered for optimizing the formation process. However, the material and cell design must also be tailored to improve the formation process. A comprehensive overview of the most important influencing factors is given below.

Process conditions

The side reactions and structural changes during formation can be controlled in several ways. The main factors to be considered are the formation cycling, i.e. current and voltage profiles, temperature, pressure, and degassing.

Degassing

The formation of the passivation layers produces gaseous species as by-products.⁴⁵ The evolved gas can isolate active material or displace electrolyte from the pores, affecing the homogeneity of the passivation layer and safety.^158,159 One possible measure to handle gas evolution is to implement appropriate degassing steps within the formation process. Various factors the influence the gas evolution must be considered when implementing degassing during the formation process.

The quantity of gas generated is linked to the active material on both electrodes.^160,161 Similar gas species are reported for common negative electrodes, i.e. Gr and silicon (Si). For both active materials the release of ethylene (C₂H₄), carbon monoxide (CO) and carbon dioxide (CO₂) has been reported for pure ethylene carbonate (EC) or EC-based electrolyte solutions.^58,161–166 Additionally, ethane (C₂H₆), methane (CH₄) and hydrogen (H₂) may be formed as by-products of EC on Gr negative electrodes. These gases are related to the specific SEI formation mechanism on Gr and Si-based negative electrodes. The gas species may differ for other negative electrodes.¹⁶⁷ On the positive electrode side, mainly CO and CO₂ are reported as by-products of oxidation reactions.^72,73,168 According to density functional theory (DFT) simulations by Leung et al., EC decomposition on Li_0.6Mn₂O₄ leads to CO₂ production only at high voltages.¹⁶⁹ In contrast, Jung et al. hypothesise that the CO and CO₂ evolution is due to a chemical reaction of the lattice oxygen with the electrolyte by analysing the gas evolution of different NMC||Gr cells for different cut-off voltages.⁷³ In addition, the CO₂ evolution may result from material impurities,⁷²e.g. due to the oxidation of Li₂CO₃ at high potentials of the positive electrode.^170,171

Electrolyte composition has a significant effect on the total gas volume and relative species content. For example, it has been found that EC-based electrolytes generate a higher volume of gas compared to propylene carbonate (PC) based electrolytes.¹⁷² Increased amounts of CO₂ and CH₄ were observed for solutions containing EC and dimethyl carbonate (DMC), respectively.¹⁷² The choice and ratio of solvant as well as of additive on the gassing behaviour was also reported in other experimental studies.^{164,166,173–177}

Bernhard et al. found that the C₂H₄ gas production starts at around 0.9 V vs. Li|Li⁺, with the rate of production peaking at around 0.5 V vs. Li|Li⁺ before decreasing until the end of the first charge cycle. Significant amounts of C₂H₄ were found only in the first cycle.¹⁷⁸ These results were confirmed by Jung et al. at room temperature.¹⁷⁹ However, at 40 °C or 50 °C the C₂H₄ gassing extended into the subsequent cycles. In addition, the total amount of gas increased at elevated temperatures. Jung et al. also found significant amounts of CO and CO₂ gas within the first formation cycle, but the CO₂ was attributed to the NMC positive electrode.¹⁷⁹ Xiong et al. reported increased gas volume for NMC electrodes when stored at elevated temperatures.^74,164,180

It was found that some gases are consumed by SEI reduction reactions,^{165,178,179,181} and, to some extent, cross over from the positive to the negative electrode.¹⁸² Ellis et al. investigated the influence of gas consumption by comparing cells with and without degassing.¹⁶⁵ After an initial 0.05C pre-charge to 3.5 V, the gas was removed. However, the degassed cells resulted in the same performance as the non-degassed cells.¹⁶⁵ In contrast, Bläubaum et al. reported improved cell performance when CO₂ gas was added to the electrolyte prior to formation.¹⁸³ A positive effect of CO₂ on the SEI was also reported in other studies.^173,174 Thorton et al. hypothesis that stabilising effect of CO₂ origins from a reduction reaction, which forms stable solid species and C₂H₄.¹⁸¹ CO₂ injection to actively modify the resulting SEI composition was also suggested by Spotte-Smith et al. in a theoretical study.¹⁶²

The produced gas amount also depends on the formation current and voltage.^{182,184–186} Leißing et al. investigated the influence of different C-rates on gassing.¹⁸⁴ Gassing was found to be caused by both SEI formation and unspecific decomposition. Both very slow and very fast formation led to an increase in non-specific gassing, but the species observed differed.¹⁸⁵

The gas amount was also seen to limit the ability for fast-charging formation. When Drees et al. scaled a single-cycle fast charging strategy (Fig. 7C) from three-electrode coin cells to pouch cells, irregular plating was detected.¹³⁵ By introducing a moderate pre-charge and early degassing step before continuining with the same fast charging protocol, Li plating was successfully prevented. The efficieny of the gas removal was improved by applying external compression on the pouch cells, which also resulted in lower overpotentials.

Significant gas evolution occurs during battery cell formation. The amount and composition of the gas is influenced by the formation protocol, the active material, the electrolyte composition, and cell design. Gas formation can be a limiting factor for fast formation strategies, especially with thick electrodes and larger cell formats. Possible countermeasures are adequate degassing steps, external compression and reduction of gas evolution by selection of suitable electrolytes. Gas injection may also be used as an active measure to influence reactions during the formation process.

Pressure

With respect to pressure influence, a distinction is made between internal and external loads. Internal loads occur, for example, as a result of electrode volume changes during Li insertion and removal, resulting in dynamic stresses. Also, the previously discussed gas evolution can increase the internal cell pressure. External loads can be applied by the housing, e.g. in cylindrical or prismatic hard-case cells, or by external compression via product carrier systems in pouch cells. Cannarella and Arnold analysed mechanical pressure in context of battery lifetime of pouch cells and found that the negative electrode surface area and separator expansion were dependent on the stack pressure applied during the cycle life.¹⁸⁷ Studies of the influence of mechanical stress on formation are rare. However, external pressure is also relevant to the formation process. Heimes et al. investigated the effect of external compression within single-cycle formations by applying a compressive force up to 1.9 kN.¹⁸⁸ This effect is shown in Fig. 9A and B. Higher pressure resulted in lower overpotentials and reduced formation times. They conclude that by mechanically compressing the electrode and separator, electrolyte diffusion in the cell can be optimised. This allows the SEI formation process to be accelerated and cell impedance to be reduced up to 50%, depending on the cell chemistry and pressure range. It has been found that the formation time can be reduced by compressing the cells with longer CC and shorter CV phases.¹⁸⁸

Currently, pressure is rarely studied in the context of formation. However, for novel materials that undergo significant structural changes and volume expansion, pressure requires more attention.

Temperature

The influence of different temperature settings during formation has been investigated in numerous studies. According to the Arrhenius equation, temperature affects the reaction kinetic. This also applies to side reactions in the battery cell, such as the SEI and CEI formation.

An et al. describe that elevated formation temperatures accelerate SEI formation, resulting in a reduction in formation time.⁴⁵ SEI formation at 40 °C results in a layer consisting of compact, inorganic components instead of a less compact, organic structure. Bhattacharya et al. performed formation at 25 °C and 60 °C.¹⁸⁹ They compared the morphology and composition of the SEI layers and concluded that formation at higher temperatures resulted in a more homogeneous layer due to higher diffusion rates. He et al. elaborate that formation at 50 °C resulted in lower total internal resistances during and after formation in comparison to a formation at 25 °C.¹⁹⁰ At 50 °C, the formation time was reduced to 15 h, compared to the formation time at 25 °C of more than 16.5 h.

Heimes et al. discovered similar relationships by investigating the influence of temperature on formation time.¹⁸⁸Fig. 9 shows results for formation at different temperatures and pressure. Fig. 9A and B show that as temperature increases, the overpotentials decrease and the formation time decreases. The authors describe that higher ambient temperatures increase the effective conductivity of the electrolyte in the separator, improve the solid diffusivity in the active material and reduce the charge transfer resistance, which increases the reaction rate and reduces the internal resistance. Therefore, the formation time can be reduced by increasing the temperature.

In contrast to studies that suggest an advantage of elevated temperature, Lee and Pyun describe that as the formation temperature increases, so do capacity losses.¹⁹¹ They conclude that at elevated temperatures the SEI is formed with more defects, resulting in more transport pathways for solvent co-intercalation. Furthermore, the formation of Li₂CO₃ is accompanied by gas evolution which can damage the Gr layer. This is in contrast to an extensive follow-up study, the microstructure of SEI was investigated when formed at different temperatures.¹⁸⁹ This study showed that when formed at 60 °C, the SEI morphology was more homogeneous and the surface composition differed significantly. It was found that formation or pre-treatment at higher temperatures could also be beneficial.

Ellis et al. found no significant influence of the formation protocol on either SEI thickness or the long-term cycling behaviour when they varied the temperature and CV step time during formation cycling.¹⁹² Moretti also analysed the SEI and found no major compositional differences when comparing two formation protocols where the first charge was performed only at 20 °C and partially at 40 °C, respectively.¹⁵⁶

Elevated temperatures have been shown to be useful in reducing formation time by increasing the side reaction rate. However, there are conflicting results regarding possible improvements in cell quality. Both negative effects due capacity loss and positive effects due to improved interfacial homogeneity have been observed.

Material and cell design

In addition to the electrochemical and environmental conditions, design choices, i.e. active material, electrolyte, and cell type, also influence the formation process. The effect of material and cell design on the formation process are outlined below.

Experimental characterisation

Various techniques are used to analyse and evaluate the effects and interdependencies of the influencing factors on the formation process. This chapter summarises the applied experimental methods that have been successfully used to study formation processes or closely related aspects.

Characterisation of the formation process varies in level of detail and scope. Some characterisation techniques can be integrated into battery cell production and are then applied to each individual cell produced, others provide more detail but are typically limited to a small number of samples due to time-consuming preparation or the need for special setups. The latter are therefore most commonly used in research and development activities. This distinction is also used to group available studies:

1. In-line testing: non-destructive characterisation of full cells. Applicable as a quality control measure in cell production.

2. Sample testing: destructive experiments (ex situ, post-mortem or special cell design required). Sample size is limited to small numbers, but in-depth material characterisation is possible.

This chapter aims to illustrate the options available for characterising the various aspects and properties affected by the process. Existing methods with their main results related to the formation process are summarised in Fig. 15.

In-line testing – non-destructive characterization of full cells

Non-destructive or operando characterisation can be used to characterise the battery cell during and after formation. These methods can be integrated into the battery cell production as quality control and integral part of the EOL testing.

Sample testing – ex situ, post mortem or special cell design required

Although much valuable data on the formation outcome can be obtained in-line, other techniques can provide further insight. These methods may require sophisticated preparation, special experimental setups, or destructive post-mortem analysis.

Comprehensive discussions of the analytical techniques that can be used to investigate LIBs can be found in other available reviews. Verma et al. published a review giving insight into many methods, which can be used to characterise the SEI.⁸⁸ The review by Zampardi and La Mantia summarises current and emerging techniques applied to analyse the CEI.⁴⁰⁶ Waldmann et al. provided an overview article on post-mortem analysis methods of LIBs in general.⁴⁰⁷ Here we will focus on discussing some tests specifically informative about the formation process.

Computer-aided process engineering

Computer-aided engineering techniques play an essential role in establishing knowledge-based process optimisation and design. For complex processes with many influencing factors such as the formation process, computational techniques offer the potential to reduce development costs and improve the process quality.

Since the formation process is affected on considerably different time and length scales, different modelling techniques must be used. Frequently used computational techniques, together with their typical length and time scales at which they are applied, are summarised in Fig. 26.

Wang et al. published a comprehensive review of modelling approaches for the SEI.⁴⁵³ The review by Horstmann et al. focuses on multi-scale models.⁴² Quantum mechanical methods used to investigate the main mechanisms involved in SEI reduction reactions of organic solvents are reviewed by Ramos-Sanchez et al.⁴⁵⁴ Theoretical work on the CEI is summarised in a section in Xu's critical review.²⁷⁵ Atkins et al. also devote a section to the simulation of interphases, discussing both interphase layers, i.e. SEI and CEI.⁴⁵⁵

This section highlights modelling approaches specifically related to the formation process. The section is structured according to the relevant areas of application, which are the decomposition reactions at the solid|liqud interfaces, interphase layer growth, interphase layer nanostructures and gas evolution.

Simulation of decomposition reactions at solid–liquid interfaces

Electrolyte decomposition occurs when the potential of the electrode is outside the electrochemical stability window of the electrolyte components. Computational methods can help to identify suitable electrolyte components and explain their decomposition mechanisms.

The electrochemical stability window of state-of-the-art electrolyte has been studied in many DFT simulations.^{341,456–458} In addition, quantum chemistry (QC) calculations can be used to obtain binding energies and redox potentials of common salts, solvents and additives and to study the redox reactions.⁴⁵⁹ Examples for computed reduction potentials are shown in Fig. 27. Such calculated values can be used as an initial assessment to screen and pre-select promising electrolyte components.

The reaction mechanisms of the formation process are very complex, making them difficult to describe mathematically. There are many possible reaction pathways, often involving multi-step reactions with many intermediates. AMID simulations were used to investigate reduction pathways. For example, the decomposition mechanisms of EC¹⁶³ and LiPF₆⁴⁶⁰ in the electrolyte bulk and EC in the proximity of electrodes, i.e. Si^246,461 and Gr,⁴⁶² were investigated with this simulation method. Further, AMID simulations were used to compute redox potentials of additives.⁴⁶³ AMID simulations have been also used to gain a better understanding of the reaction mechanisms at the positive electrode. For example, Leung studied the initial stages of EC decomposition on ideal Li_0.6Mn₂O₂ surfaces at constant potential using DFT and AMID.¹⁶⁹ Computerised reaction networks (CRN)⁴⁶⁴ and their combination with machine learning techniques (CRN-ML)⁴⁶⁵ can be used to construct large reactions networks from first principle calculations.

Combined DFT and AMID simulations were used to calculate electron transfer through SEI components,⁴⁶¹ thermodynamic and transport properties of electrolytes,^466,467 decomposition potentials of solvents⁴⁶⁸ and additives.⁴⁶³ Furthermore, these methods can be used to study the influence of the local solvent concentration,^293,469 the degree of lithiation²⁴⁶ or the electrolyte composition at the interface.^286,470 These aspects must be considered together to predict the SEI composition.

There are two main tasks that computational methods can perform with respect to interfacial reactions. They can be used to quickly screen possible electrolyte components by examining their decomposition potentials. In addition, AMID simulations can be used to investigate complex reaction networks, even near the interface. The combination of these aspects may allow computational design of composition of the interphase layers.

Simulation of interphase layer growth

An essential property of the interphase layers is their ability to passivate against electrolyte decomposition. Passivation defines the continuous growth of the interphase layer and the associated capacity loss during formation and ageing. Furthermore, the CE of the LIB after formation is determined by the growth limiting properties of the interphase layers.

Continuum models can be used to describe the growth of interphase layers in a lumped fashion. These models usually simplify the reaction mechanisms and kinetics by reducing them to a single mechanism and assuming homogeneous interphase properties. The growth rate is limited by various passivation mechanisms. Understanding the growth limitation of the interphase layer is essential.¹¹

Several transport mechanisms that determine the self-limiting nature of the SEI are discussed. These mechanisms are illustrated in Fig. 28 and were discussed by Horstmann et al. in their review on multi-scale models of the SEI growth in detail.⁴² In general, these mechanisms are also applicable for the formation process. Equations and references for these mechanisms are summarized in Table 2.

---

The electron tunneling mechanism (ETM) originates from quantum mechanics, where it describes the possibility of an electron passing through a zone that is normally prevented by an energy barrier. The mechanism can be implemented using a modified Tafel equation.

The electron conduction mechanism (ECM) assumes that the rate is determined by the transport of charge-carrier driven by potential gradients across a dense SEI.⁴⁷¹ Colclasure et al. implemented this mechanisms in a single-particle model that includes reaction kinetics and transport of species within the SEI film.⁴⁷²

The solvent diffusion mechanism (SDM) assumes that the transport of the solvent or salt from the electrolyte bulk to the reaction site is the limiting factor in continuous SEI growth. The process is modelled by assuming Fick's law of diffusion for the solvent components.⁴⁷³

The diffusive interstitial mechanism (IDM) has been proposed by Shi et al.⁴⁷⁴ Small radicals, such as Li atoms, are charge carriers. Shi et al. calculated the concentration of Li interstitials within a compact Li₂CO₃ layer to be between 4.9 × 10⁹ and 2.3 × 10¹⁶ cm⁻³ depending on the electrode potential. The Li interstitials enable the propagation of decomposition reactions in the SEI layer.^42,342,474

Several simulative and experimental studies suggest that the initial growth after the first atomic layer of SEI is established on the surface, is limited by ETM.^408,475,476 The tunneling barrier of SEI has been investigated in several studies. Lin et al. performed DFT simulations to calculate the electron tunnelling barrier of Li₂CO₃, Li₂O, and LiF and a Li metal electrode.⁴⁷⁵ They concluded that the initial capacity loss is driven by ETM until a thickness of approximately 2–3 nm is reached, which was also found to change under compression/expansion.⁴⁷⁵ Leung et al. concluded that electron tunnelling is sufficiently suppressed by a 7 Å insulating oxide SEI in a combined experimental and first-principles study.⁴⁷⁷ A thickness of about 10 Å was calculated by Benitez et al. using AMID and DFT simulations to sufficiently block electron transport through pure LiF or Li₂O films on Si negative electrodes and varying degrees of lithiation by Benitez et al. using AMID and DFT simulations.⁴⁶¹

Beyond the tunneling regime, knowledge of the limiting mechanism is less profound. Using the experimental long-term storage data at different electrode potentials published by Keil et al.,^383,478 Single et al. contrast the discussed mechanisms by comparing their plausibility.⁴⁷⁹ The authors conclude that IDM is the only mechanism that adequately explains the experimentally observed dependencies.⁴⁷⁹ The analysis of the same data was later extended to include the time dependence of the SEI growth mechanisms, focusing on the comparison between SDM and IDM.⁴⁸⁰ It was shown that IDM explains both the SOC and time dependence, while SDM reproduces only one of these dependencies.⁴⁸⁰ They conclude that IDM is the main driver for the SOC-dependent SEI growth.⁴⁸⁰ This is in agreement with the DFT study by Soto et al., which identified IDM as responsible for electron transfer.³⁴²

Schomburg et al. analysed SEI growth curves obtained during formation using DVA.⁴⁰⁸ Using an SEI growth model that considers a concurrent ETM and IDM for two electrolytes, the model-based analysis indicated that the tunnelling barrier shifts as a function of electrolyte composition. It was also shown that the SEI growth obtained during a slow initial formation cycle cannot be explained by ETM alone. As shown recently, growth functions can be also identified with data driven models such as neural ordinary differential equations.⁴⁸¹

A dependence on the current direction has been reported by Attia et al.⁴⁸² To describe this phenomenon, Das et al. introduced a model based on the assumption that the SEI can be considered as a mixed ion-electron conductor.⁴⁸³ By this and by introducing a Li concentration dependence for the electronic conductivity in the SEI, the growth of the layer is promoted or suppressed depending on the lithiation current direction.

Typically, SEI growth models assume a solid layer of a single SEI species and a limiting transport mechanism. However, it is conceivable that the evolution of the SEI morphology may also influence the limiting transport processes. To investigate this possible influence, Single et al. introduced a one-dimensional framework that takes into account the porosity of the SEI.⁴⁸⁴ This approach allows to simulate the evolution of SEI thickness and morphology along the interphase thickness and incorporates different rate-limiting transport mechanisms, i.e. SDM in the SEI pores and IDM or ETM in the solid SEI phase.⁴⁸⁴ The model predicts the often reported two-layer structure with a constant ratio between the thicknesses of the inner and porous outer layers.⁴⁸⁴

Growth models typically neglect mechanical stress. While these variables can be neglected for comparison in storage tests, they play an important role in formation cycling. Mai et al. modified a Pseudo 2D (P2D) battery model using finite-strain theory to account for the interplay between large electrochemical-mechanical deformations at the particle and electrode levels. This was used to model the influence of porosity changes on the mechanical stress of each cell component.⁴⁸⁵ Kolzenberg et al. introduced a chemo-mechanical model that incorporates the mechanical-stress inducted SEI growth.⁴⁸⁶ The model is in good qualitative agreement with the experimentally observed SEI cracking during lithiation and healing during delithiation of Si particles, leading to accelerated capacity fade during cyclic ageing.⁴⁸⁶ However, the model has not yet been tested for formation cycling. Other continuum-level models that consider degradation mechanisms due to mechanical stress have been reviewed by Zhang.²⁰⁵

Model-based analysis of SEI properties after formation is also possible. Witt et al. have applied such an approach to investigate long-term evolution of the SEI properties.⁴⁸⁷ The model comprises a P2D and SEI model capable of calculating EIS and electrochemical discharge curves.⁴⁸⁷ This allows to determine physically insight such as SEI thickness or interfacial SEI|liquid interface area.

Simulation of SEI growth can be used to design formation cycling protocols. However, it remains a challenge to identify suitable growth limiting processes. The situation is even more complicated for materials with significant volume expansion, such as Si, because the mechanical properties of the interface layers must also be considered. The goal of the formation simulation methods discussed is to reduce the number of experiments required to design formation cycling protocols that are tailored to the material and cell design.

Simulation of interphase nanostructures

Due to the changing conditions and the different reactants and intermediates, several reactions compete during the initial formation, resulting in a heterogeneous structure, which was first introduced by Peled et al.^488,489 To obtain spatially resolved insights on the SEI structure, first-principle simulations and the kinetic Monte Carlo (kMC) method were used.^{306,454,490–496}

Although first-principle studies provide spatially resolved information on the SEI and have made progress on the time scale,⁴⁹⁷ they are still limited to the ns range and are therefore unable to describe complete formation cycles. Compared to ab initio simulations, kMC allows longer time scales to be simulated while preserving atomistic detail.^162,493,498

Methekar et al. developed a 2D kMC model to theoretically investigate the effect of process parameters, such as charging voltage, current density and temperature, on the initial SEI layer formation and suggest that there is an optimal temperature to minimise initial capacity loss.⁴⁹⁰ This model was extended by Ramos-Sanchez et al. to a three-dimensional coarse-grained kMC (CG-kMC) model, allowing them to track the growth rate as a function of layer thickness.⁴⁵⁴ The model was used as an example to simulate the layer growth for a multi-step reduction of EC to LEDC using a fixed adsorption rate and DFT-based reaction rates.⁴⁵⁴ Esmaeilpour et al. performed a simulation study of SEI formation using a 2D kMC model with reaction rates obtained from QC calculations.³⁰⁶ Their simulation shows qualitatively good agreement with experimental observations of the SEI morphology for a solution-mediated growth scenario.³⁰⁶ Similarly, based on reaction mechanisms obtained by AIMD and a stochastic analysis method that accelerates CRN-ML simulations,⁴⁹⁴ Spotte-Smith et al. used a kMC model to perform a comprehensive study of the initial SEI formation and evolution for varying potentials and electron tunneling barriers.

Röder et al. dynamically couple a kMC surface film growth model with a macroscopic continuum battery model to simulate SEI formation.⁴⁹⁵ In this way, the structure of the passivating film directly influences the reactions at the particle surface. In a first simulation study, they investigate the effect of two particle sizes on SEI formation for an electrolyte composed of EC and LiPF₆. The simulations show faster SEI growth on larger particles. In subsequent work, the same multiscale approach was extended by combining the kMC model with a P2D model and an advanced coupling method, allowing to account for distribution of concentrations and potentials.^492,496 The novel multiscale model was used to simulate various formation protocols. Simulation results are show in Fig. 29. While slow charging led to locally heterogenous structures and homogeneous distributions of species along the x-axis (Fig. 29A), the opposite was observed for fast charging protocols (Fig. 29B).⁴⁹⁶

In contrast to the continuum models used to simulate SEI growth, the models discussed in this section provide a detailed insight into the molecular structure of the interphase layer. They allow to combine insights from complex decomposition mechanisms and SEI growth mechanisms. Theoretically, it enables to reveal differences in the SEI structure without the need for sophisticated experimental surface characterization methods. The goal of these simulations is to tune the electrolyte choice and cycling protocol to achieve a desired interphase layer structure. However, it is usually not known how the ideal SEI and CEI should be composed to achieve long life and safety. These aspects need to be worked out in order for the methods to reach their full potential.

Simulation of gas evolution

Gas evolution affects the formation at different scales and is considered in different modelling approaches.

In first-principle and kMC simulations, gases are considered as products and reactants of the reactions. For example, Leung predicted the formation of CO gas when examining two-electron reduction pathways of EC and assuming fast electron tunneling.¹⁶³ In the case of slow electron tunneling, the reduction of EC was calculated to yield C₂H₄ and CO₂ instead.¹⁶³ This is consistent with the findings of Alzate-Vargas et al. who reported the presence of C₂H₄ in the later stages of their simulation, covering the first 100 ns of SEI formation.⁴⁹⁷ They also discuss the relevance of the consideration of gas molecules, as they did not observe the formation of gas bubbles.⁴⁹⁷ However, given the uncertainties regarding potential entrapment, temporary influence of these molecules on SEI growth, or change in density they suggest to consider the gas molecules in the simulations.

Gas evolution and its effects on battery performance have been simulated in mechanistic continuum models. These models assume that the generated gas is inert and reduces the volume fraction of electrolyte. Seo et al. introduced a model that discusses how gas evolution from electrolyte decomposition and impurities causes swelling in Li-ion batteries.⁴⁹⁹ Their model explains the non-linear increase in cell resistance with changes in volume fraction caused by gas evolution. They apply the model to study the effects of these changes on discharge curves and heat generation.⁴⁹⁹ The P2D model has been extended by Rashid and Gupta to capture the combined effect of SEI formation and gas evolution on battery performance caused by cyclic degradation.⁵⁰⁰ Here, both growing SEI and generated gases reduce the electrolyte volume fraction.

Although the modeling approaches presented have been applied to study long-term effects of gas evolution, they are also relevant to simulate gas evolution and its effects during formation. As discussed in this review, gas evolution is critical for fast formation processes. Therefore, the amount of gas produced and its transport through the cell during formation must be considered for the model-based design of formation protocols and in particular for the degassing steps. This aspect is currently poorly addressed in the literature.

Summary and discussion

In the following main insights into the influencing factors, the most relevant experimental and theoretical methods are summarised and discussed. In addition, conclusions are drawn and a perspective on future directions is given.

Summary

This review systematically examines the main factors influencing the formation process. The electrochemical conditions, i.e. current and voltage, during the formation cycling and the electrolyte have been widely studied and have a significant influence. However, this review shows other important factors, such as the pressure, temperature, and degassing, as well as material and cell design aspects, are less frequently addressed, although they undoubtably influence the formation processes. By systematically reviewing the literature on these various aspects, it has become clear that the formation process is not independent of the material and the cell design, but that both aspects must be precisely coordinated. The main challenges posed by next-generation materials are related to the significant volume expansion, which requires a better understanding of the mechanical properties of the interphase layer and the influence of external and internal pressure. In addition, as the voltage window expands especially towards high-voltage positive electrodes, the formation process must consider both electrodes simultaneously, while development of new electrolytes additives are shown to be a promising approach to tune both electrodes independently.

Experimental methods suitable for studying the formation process are also reviewed. It is shown that a combination of non-destructive and destructive characterisation techniques can provide valuable insights into the formation process and quality parameters of battery cells. A combination of methods is required to deduce details on the interphase layer properties as a function of the structure and composition. Several techniques are presented and discussed, which can be used as a guide for a detailed study of relevant phenomena of interest.

Computer-aided engineering techniques have become essential for understanding and optimising the formation process. However, few of the available tools are systematically used to support process development. It is shown that the multitude of influential parameters affecting the process on different time and length scales is a key challenge. Different modelling techniques are reviewed that allow the representation of relevant formation process phenomena.

Discussion

Given the recognised importance of formation and its potential correlation with lifetime, safety and cost, systematic variation of formation protocols remains surprisingly scarce.

It was shown that single cycle formation protocols are a promising way to achieve faster formation procedures. However, a key challenge with single cycle protocols is that Li plating must be avoided, and formation may not be complete.

A common definition of when formation is complete is needed for systematic comparison and improvement. Otherwise, no comparison of formation time is possible. This, combined with the lack of systematic variation, limits our current understanding of these formation protocols. Based on our analysis of non-destructive characterisation techniques, we propose to use the CE and CR to evaluate the progression of the formation process. These values can be used to convolute the CEI and SEI contributions. Possibly the change in CE in two consecutive cycles can be used for a general material independent definition of the end of formation. However, measurement accuracy and measurement artefacts caused by electrode overhang in large cell formats are major challenges for this assessment. Therefore, the use of CE and CR alone may not be sufficient and must be accompanied by other methods such as EIS and self-discharge testing.

There is a well-established toolbox of experimental methods that can be used to characterize the influence of the formation process in detail. In this context, surface analysis techniques play a key role. It has been shown that a combination of different techniques is required to gain a comprehensive understanding of the interphase layers. The goal must be to select a comprehensive set of methods that can reveal composition, molecular structure, thickness, ion transport, and passivation capabilities. For example, without a comprehensive set of methods, it is not possible to deduce the difference between improved ionic conductivity and a thinner film.

Due to the large parameter space and time-intensive characterisation, computational approaches are promising to reveal multi-scale and multi-physical correlations or to pre-select promising material candidates, e.g. electrolyte additives. Although simulation studies have already contributed valuable insights to the current understanding of the formation process, it is striking that key influencing factors, such as pressure or gas evolution, are hardly investigated in theoretical work. While simulations have explored the factors influencing battery ageing, their application to the formation process is still limited. Similarly, gas evolution and transport at the electrode and cell level have not been extensively studied in theoretical work. However, gas distribution might be limiting factor for fast formation protocols and should therefore be investigated in more detail. Control of the gaseous species may even prove to be an active means of modifying the formation process, offering further potential for optimisation.

A major problem for knowledge-based optimisation of the formation process is that the relationships between process, structure, and performance are not fully understood yet, despite the progress in understanding key physical aspects and the effects of SEI species on cell performance. To adress this, further knowledge regarding the relationship between electrolyte components is essential, as also emphasised in other articles regarding ageing⁸⁹ and fast-charging.^501,502 To be able to activly form desired passivation layer, also systemtically acquiring more insights on the involved reactions and initial dynamic structural evolution is important.

A methodical comparison of theoretical and experimental work is required. This requires a temporal recording of different physical properties and ideally a quantitative comparison with simulation results. Tracking and comparing the temporal evolution of gaseous by-products could be a simple yet powerful method to parameterise and validate mathematical models. Validation of the temporally and spatially resolved evolution of SEI structure and morphology appears more complex. However, EC-AFM, if possible, in combination with XPS images at selected time points, can provide useful and detailed information on the temporal evolution of the SEI, but is time consuming.

An improved multi-physical understanding also holds potential for model-based operando analysis of formation data for cell quality assessment. It has been shown that formation data can already be used for quality assessment and parameterisation of interphase growth models. Such approaches could even be considered as an integral part of the EOL, potentially leading to a reduction in manufacturing time and cost.

The limitation of commonly used small sample sizes needs to be addressed, particularly given the significant uncertainties associated with the handmade battery electrodes often used in academia. Valid evaluations of reject rates in relation to the formation protocol are often not possible in academia due to limited sample sizes. Larger sample sizes are essential to investigate the effect of the influencing factors and their interdependencies, particularly to assess the impact on battery ageing. The use of cells produced in larger production lines and larger sample sizes is one way to overcome this problem.

There is little information from industry on formation processes. This is likely to lead to differences between academic and industrial perspectives on the formation process, creating a research gap and limiting the transferability of research results to real production lines. Industry expertise and insight is needed to bridge the gap between academic studies and practical implementation.

Additional process, e.g. prelithiation or surface medication, should be investigated in more detail in the context of cell formation as they offer a possibility to influence of formation prior to the formation processes without compromising material and cell format choices. Prelithiation of the negative electrodes can compensate for initial irreversible capacity losses.⁵⁰³ This showed promising improvements in cell quality and performance and should be carefully investigated to better understand its effect, particularly in terms of improved SEI formation for electrodes with increased Si content.²⁴⁹ This is also interesting in terms of gassing, as the SEI is partially built before the actual formation cycle.

Finally, it has been shown that many of the current challenges are even more important for next generation materials such as Li, Si or high-voltage positive electrode materials. These materials are often associated with significant implications for the formation process design. A knowledge-based understanding and a systematic use of computational methods can significantly reduce time and cost for the development of material-tailored formation processes which improve the cells’ sustainability, durability, and safety.

Conclusion

In this review, the current understanding of the formation process is presented, and the most relevant influencing factors are summarised and examined in detail.

Interdependencies between the electrochemical conditions during formation and the materials selected, as well as the lack of a standardised evaluation of formation, often make it difficult to directly compare existing studies, which sometimes prevents general conclusions from being drawn. Therefore, it is highlighted that a key challenge for a knowledge-based design of the formation process lies in an interlocking of material and cell design on the one hand and the process design on the other hand. This is also illustrated in Fig. 30. The discussed experimental and theoretical methods must be applied systematically to reveal the mechanisms and then utilize the knowledge by computational process engineering.

Most important future perspectives are summarised below:

•There is no agreed criterion for when the formation process is complete. Therefore, a chemistry independent criterion for the end of formation is needed.

•Gas evolution during formation is high and affects the overall process dynamics. In some cases, degassing even plays a rate-limiting role. Degassing must therefore be considered as an active part of the formation process.

•The effect of passive materials, i.e. separator design, and cell formats, i.e. pouch or cylindrical cells, also influence the formation process and need to be investigated.

•A better understanding of the relationship between the SEI/CEI structure and the performance metrics is required.

•Different computational methods are available at different scales, covering the most relevant aspects of formation. They should be systematically combined to optimise the process.

•Systematic and standardised characterisation of battery cells after formation with sufficient sample quality and size is essential to allow meaningful comparison of different formation strategies and data-driven analysis.

•Next generation materials often increase the significance of the formation process. Thus, formation must be studied more extensively for these materials.

Reflecting on the current state and future directions of the formation process, further advances are needed at both the materials science and process engineering levels to tailor optimal formation processes to specific chemistries, cell formats and applications. More systematic research is needed to identify key relationships between the formation process and performance metrics, and to pave the way for novel, affordable, high quality, safe and sustainable cell generation with knowledge-driven formation process design.

Author contributions

Felix Schomburg: conceptualization, investigation, project administration, visualization, writing – original draft, writing – review & editing; Bastian Heidrich: conceptualization, investigation, writing – original draft, writing – review & editing; Sarah Wennemar: conceptualization, investigation, writing – original draft, Writing – review & editing; Robin Drees: conceptualization investigation, writing – original draft, writing – review & editing; Thomas Roth: conceptualization, investigation, writing – original draft, writing – review & editing; Michael Kurrat: funding acquisition, supervision; Heiner Heimes: funding acquisition, supervision; Andreas Jossen: funding acquisition, supervision, writing – review & editing; Martin Winter: funding acquisition, supervision, writing – review & editing; Jun Young Cheong: conceptualization, investigation, supervision, writing – original draft, writing – review & editing; Fridolin Röder: conceptualization, funding acquisition, investigation, project administration, supervision, writing – original draft, writing – review & editing.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was supported by the project “FormEL” (03XP0296E) as part of the competence cluster “ProZell” by the Federal Ministry of Education and Research in Germany (BMBF). This work was also supported by the Deutsche Forschungsgemeinschaft (DFG, project number: 533115776). Only the authors are responsible for the content of this article.

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Footnote

† Cell conditioning is also occasionally called cell finalisation. Both refer to the same part of the manufacturing process. Throughout the rest of the script, we only use cell conditioning.

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