Battery models generally fall into two categories: physics-based models and ECM models. Physics-based Doyle-Fuller-Newman (DFN) models can accurately simulate the battery internal electrochemical processes, but to properly account for thermal effects requires a strong coupling between a DFN model and a 3D thermal model, which is computationally unaffordable. Distributed Equivalent Circuit Network (ECN) models can perform simulations with high speed and reasonable accuracy. However, these models rely heavily on the characterisation experiments for ECN parameter identification, which is resource-intensive and can lead to inaccurate parametrisation outcomes due to internal thermal inhomogeneity. To harness the strengths of both models, we propose a computational framework to integrate electrochemical DFN model and 3D distributed ECN model together. Using this framework, we simulate constant current discharge experiments of Kokam 7.5 Ah pouch cell (Model SLPB75106100) and compare the simulations with the commonly-used lumped DFN-thermal model. The computational model outperforms the lumped DFN model at low-temperature and/or high C-rate scenarios significantly. The largest predicting error of the framework at 3 C-rate &Tam = 25oC and at 1 C-rate &Tam = 0 oC is approximately 1/3 of that for DFN model. At 3 C-rate &Tam = 5oC, the difference between these two can rise to 377 mV. By integrating DFN and 3D-distributed ECN together, the computational framework can simulate the complicated interplay between electrochemistry, thermal process, and electricity within a cell fast and accurately. We anticipate this computational framework to be a valuable toolset to assist researchers and engineers in the design and control of Li-ion batteries.

Degradation models are important tools for understanding and mitigating lithium-ion battery aging, yet a universal model that can predict degradation under all operating conditions remains elusive. One challenge is the coupled influence of calendar and cycle aging phases on degradation mechanisms, such as solid electrolyte interphase (SEI) formation. In this work, we identify and systematically compare three different SEI interaction theories found in the literature, and apply them to experimental degradation data from a commercial lithium-ion cell. In a step-by-step process, and after careful data selection, we show that SEI delamination without any cracking of the active particles, and SEI microcracking, where cycling only affects SEI growth during the cycle itself, are both unlikely candidates. Instead, the results indicate that upon cycling, both the SEI and the active particle crack, and we provide a simple, 4-parameter equation that can predict the particle crack rate. Contrary to the widely-accepted Paris' law, the particle crack rate decreases with increasing cycles, potentially due to changing intercalation dynamics resulting from the increasing surface-to-volume ratio of the active particles. The proposed model predicts SEI formation accurately at different storage conditions, while simply adding the degradation from pure calendar and cycle aging underestimates the total degradation.

High-throughput computing (HTC) is a pivotal asset in many scientific fields, such as biology, material science and machine learning. Applying HTC to the complex physics-based degradation models of lithium-ion batteries enables efficient parameter identification and sensitivity analysis, which further leads to optimal battery designs and operating conditions. However, running physics-based degradation models comes with pitfalls, as solvers can crash or get stuck in infinite loops due to numerical errors. Also, how to pipeline HTC for degradation models has seldom been discussed. To fill these gaps, we have created ParaSweeper, a Python script tailored for HTC, designed to streamline parameter sweeping by running as many ageing simulations as computational resources allow, each with different parameters. We have demonstrated the capability of ParaSweeper based on the open-source platform PyBaMM, and the approach can also apply to other numerical models which solve partial differential equations. ParaSweeper not only manages common solver errors, but also integrates various methods to accelerate the simulation. Using a high-performance computing platform, ParaSweeper can run millions of charge/discharge cycles within one day. ParaSweeper stands to benefit both academic researchers, through expedited model exploration, and industry professionals, by enabling rapid lifetime design, ultimately contributing to the prolonged lifetime of batteries.

High-throughput computing (HTC) is a pivotal asset in many scientific fields, such as biology, material science and machine learning. Applying HTC to the complex physics-based degradation models of lithium-ion batteries enables efficient parameter identification and sensitivity analysis, which further leads to optimal battery design and operating conditions. However, running physics-based degradation models comes with pitfalls, as solvers can crash or get stuck in infinite loops due to numerical errors. Also, how to pipeline HTC for degradation models has seldom been discussed. To fill these gaps, we have created ParaSweeper, a Python script tailored for HTC, designed to streamline parameter sweeping by running as many ageing simulations as computational resources allow, each with different parameters. We have demonstrated the capability of ParaSweeper based on the open-source platform PyBaMM, and the approach can also apply to other numerical models which solve partial differential equations. ParaSweeper not only manages common solver errors, but also integrates various methods to accelerate the simulation. Using a high-performance computing platform, ParaSweeper can run millions of charge/discharge cycles within one day. ParaSweeper stands to benefit both academic researchers, through expedited model exploration, and industry professionals, by enabling rapid lifetime design, ultimately contributing to the prolonged lifetime of batteries.

Predicting lithium-ion battery lifetime is one of the greatest unsolved problems in battery research right now. Recent years have witnessed a surge in lifetime prediction papers using physics-based, empirical, or data-driven models, most of which have been validated against the remaining capacity (capacity fade) and sometimes resistance (power fade). However, there are many different combinations of degradation mechanisms in lithium-ion batteries that can result in the same patterns of capacity and power fade, making it impossible to find a unique validated solution. Experimentally, degradation mode analysis involving measuring the loss of lithium inventory, loss of active material at both electrodes, and electrode drift/slippage has emerged as a state-of-the-art requirement for cell degradation studies. In this paper we coupled five degradation mechanisms together for the first time. We also showed how three models with different levels of complexity can all fit the remaining capacity and resistance well, but only the model with five coupled degradation mechanisms could also fit the degradation modes at all temperatures. This work proves that validating only against capacity and power fade is no longer sufficient, and state-of-the-art experimental and modelling degradation studies should include degradation mode analysis for validation in the future.

A model of a lithium-ion battery containing a cosolvent electrolyte is developed and implemented within the open-source PyBaMM platform. Lithium-ion electrolytes are essential to battery operation and normally contain at least two solvents to satisfy performance requirements. The widely used Doyle-Fuller-Newman battery model assumes that the electrolyte comprises a salt dissolved in a single effective solvent, however. This single-solvent approximation has been disproved experimentally and may hinder accurate battery modelling. Here, we present a two-solvent model that resolves the transport of ethylene carbonate (EC) and lithium salt in a background linear carbonate. EC concentration polarization opposes that of Li+ during cycling, affecting local electrolyte properties and cell-level overpotentials. Concentration gradients of Li+ can be affected by cross-diffusion, whereby EC gradients enhance or impede salt flux. A rationally parametrized model that includes EC transport predicts 6% more power loss at 4.5C discharge and ~0.32% more capacity loss after a thousand 1C cycles than its single-solvent equivalent. This work provides a tool to model more transport behaviour in the electrolyte that may affect degradation and enables the transfer of microscopic knowledge about solvation structure-dependent performance to the macroscale.

The pseudo two-dimensional (P2D) model is one of the most powerful tools in modelling lithium-ion batteries (LIBs) 1, in that it can describe the complex electrochemical and thermal behaviours of LIBs with high fidelity yet maintain relatively high computing efficiency. To achieve that, many assumptions have been made, one of which is the single solvent assumption. However, most electrolytes used in LIBs uses multiple solvents to balance the requirements of conductivity, diffusivity and viscosity 2. Therefore, the single solvent assumption indicates that all solvents move as a single entity. However, previous experimental studies have shown that Li+ preferentially attracts cyclic carbonates (like ethylene carbonate, EC) rather than linear carbonates (such as ethyl-methyl carbonate, EMC) to form ion-solvent clusters 3. During charge/discharge, ion-solvent clusters move between the positive and negative electrodes to constitute ionic current. At the electrolyte-electrolyte interface, Li+ de-solvates from the clusters and intercalates into the electrode, or vice versa. Such process will induce concentration gradients of both solvents and lithium ions; the solvent concentration has been ignored in the P2D model. The simplification means the current P2D model fails to capture two important phenomena: (1) many electrolyte properties - including conductivity, diffusivity, and thermodynamic factors - are sensitive to the solvent concentration 4; (2) the solvent components in the ion-solvent clusters are preferentially consumed by interfacial side reactions such as the growth of solid-electrolyte interface (SEI) 3. To fill this gap, we add an extra governing equation for the solvent concentration (in our case, EC) which allows us to describe an electrolyte with two solvents and one salt. We also include a cross diffusion term to consider the dragging effect between the working solvent (EC) and lithium ions. For the charge conservation equation, we directly use measured liquid junction potential as a function of both solvent and lithium-ion concentration, which avoids possible errors brought by identifying the thermodynamic factors. To elucidate the effect of solvent segregation, we compare the overpotential and concentration profile of Li ion and EC at the end of 3C discharge of the normal DFN (single case) and our revised model (double case). The revised model predicts opposite EC concentration compared with Li+, which has been observed by Wang et al. 5 For a high value of , the dragging effect between Li+ and EC is more significant, inducing high concentration gradients of both species. The EC overpotential can be as high as 10 mV and further affects the rate performance of LIBs. This revised model captures more complicated transport mechanisms in the electrolyte and opens the chance of linking the microscopic understanding on solvation structure to a continuum level model. Reference: (1) Newman, J.; Thomas-Alyea, K. E. Electrochemical Systems; 2004. (2) Xu, K. Electrolytes and interphases in Li-ion batteries and beyond. Chem Rev 2014, 114 (23), 11503-11618. DOI: 10.1021/cr500003w. (3) Xu, K. Solvation Sheath of Li+ in Nonaqueous Electrolytes and Its Implication of Graphite/ Electrolyte Interface Chemistry. J. Phys. Chem. C 2007. (4) Ding, M. S.; Xu, K.; Zhang, S. S.; Amine, K.; Henriksen, G. L.; Jow, T. R. Change of Conductivity with Salt Content, Solvent Composition, and Temperature for Electrolytes of LiPF6 in Ethylene Carbonate-Ethyl Methyl Carbonate. Journal of The Electrochemical Society 2001, 148 (10). DOI: 10.1149/1.1403730. (5) Wang, A. A.; Greenbank, S.; Li, G.; Howey, D. A.; Monroe, C. W. Current-driven solvent segregation in lithium-ion electrolytes. Cell Reports Physical Science 2022, 3 (9). DOI: 10.1016/j.xcrp.2022.101047. Figure 1

Predicting lithium-ion battery lifetime is one of the greatest unsolved problems in battery research right now. Recent years have witnessed a surge in lifetime prediction papers using physics-based, empirical, or data-driven models, most of which have been validated against the remaining capacity (capacity fade) and sometimes resistance (power fade). However, there are many different combinations of degradation mechanisms in lithium-ion batteries that can result in the same patterns of capacity and power fade, making it impossible to find a unique validated solution. Experimentally, degradation mode analysis involving measuring the loss of lithium inventory, loss of active material at both electrodes, and electrode drift/slippage has emerged as a state-of-the-art requirement for cell degradation studies. In this paper we coupled five degradation mechanisms together for the first time. We also showed how three models with different levels of complexity can all fit the remaining capacity and resistance well, but only the model with five coupled degradation mechanisms could also fit the degradation modes at all temperatures. This work proves that validating only against capacity and power fade is no longer sufficient, and state-of-the-art experimental and modelling degradation studies should include degradation mode analysis for validation in the future.

Predicting lithium-ion battery (LIB) lifetime is one of the most important challenges holding back the electrification of vehicles, aviation, and the grid. The continuous growth of the solid-electrolyte interface (SEI) is widely accepted as the dominant degradation mechanism for LIBs. SEI growth consumes cyclable lithium and leads to capacity fade and power fade via several pathways. However, SEI growth also consumes electrolyte solvent and may lead to electrolyte dry-out, which has only been modelled in a few papers. These papers showed that the electrolyte dry-out induced a positive feedback loop between loss of active material (LAM) and SEI growth due to the increased interfacial current density, which resulted in capacity drop. This work, however, shows a negative feedback loop between LAM and SEI growth due to the reduced solvent concentration (in our case, EC), which slows down SEI growth. We also show that adding extra electrolyte into LIBs at the beginning of life can greatly improve their service life. This study provides new insights into the degradation of LIBs and a tool for cell developers to design longer lasting batteries.

Many different degradation mechanisms occur in lithium-ion batteries, all of which interact with one another [1]. However, there are few fewer observable consequences of degradation than there are mechanisms [2]. It is possible to measure the different degradation modes: loss of lithium inventory (LLI), loss of active material (LAM), impedance change and stoichiometric drift [3]. It is not always possible to link these observable consequences of degradation to any particular mechanism or combination of mechanisms. Many models of degradation exist [4], but these models have many parameters that cannot be measured directly. A recent modelling study [5] found the number of parameters that the model is sensitive to is greater than the number of observable degradation modes. However, the same model [5], despite including just four degradation mechanisms, found five possible degradation pathways a battery can follow. The model was built so that more mechanisms can easily be added later, so more pathways will be found. In this work, a new approach to diagnosing battery degradation is proposed, based on these pathways. Experimental data for the degradation modes can be identified as being consistent with a particular pathway. Once the correct pathway is found, the parameters that particular pathway is sensitive to can be fit to the data, feeding back into the model. [1] Jacqueline Edge et al., Phys. Chem.: Chem. Phys. vol. 23, pp. 8200-8221, 2021. [2] Christoph Birkl et al., Journal of Power Sources vol. 341, pp. 373-386, 2017. [3] Matthieu Dubarry et al., J. Electrochem. En. Conv. Stor. vol. 17, pp. 044701, 2020. [4] Jorn Reniers et al., J. Electrochem. Soc. vol. 166 pp. A3189-A3200, 2019. [5] Simon O’Kane et al., Phys. Chem.: Chem. Phys., submitted, 2022. https://arxiv.org/abs/2112.02037

Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. However, very few published models of battery degradation explicitly consider the interactions between more than two degradation mechanisms, and none do so within a single electrode. In this paper, the first published attempt to directly couple more than two degradation mechanisms in the negative electrode is reported. The results are used to map different pathways through the complicated path dependent and non-linear degradation space. Four degradation mechanisms are coupled in PyBaMM, an open source modelling environment uniquely developed to allow new physics to be implemented and explored quickly and easily. Crucially it is possible to see 'inside' the model and observe the consequences of the different patterns of degradation, such as loss of lithium inventory and loss of active material. For the same cell, five different pathways that can result in end-of-life have already been found, depending on how the cell is used. Such information would enable a product designer to either extend life or predict life based upon the usage pattern. However, parameterization of the degradation models remains as a major challenge, and requires the attention of the international battery community.

The expansion of lithium-ion batteries from consumer electronics to larger-scale transport and energy storage applications has made understanding the many mechanisms responsible for battery degradation increasingly important. The literature in this complex topic has grown considerably; this perspective aims to distil current knowledge into a succinct form, as a reference and a guide to understanding battery degradation. Unlike other reviews, this work emphasises the coupling between the different mechanisms and the different physical and chemical approaches used to trigger, identify and monitor various mechanisms, as well as the various computational models that attempt to simulate these interactions. Degradation is separated into three levels: the actual mechanisms themselves, the observable consequences at cell level called modes and the operational effects such as capacity or power fade. Five principal and thirteen secondary mechanisms were found that are generally considered to be the cause of degradation during normal operation, which all give rise to five observable modes. A flowchart illustrates the different feedback loops that couple the various forms of degradation, whilst a table is presented to highlight the experimental conditions that are most likely to trigger specific degradation mechanisms. Together, they provide a powerful guide to designing experiments or models for investigating battery degradation.

The Doyle-Fuller-Newman framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provides an effective balance between detail, needed to capture key microscopic mechanisms, and simplicity, needed to solve the governing equations at a relatively modest computational expense. Nevertheless, implementation requires values of numerous model parameters, whose ranges of applicability, estimation, and validation pose challenges. This article provides a critical review of the methods to measure or infer parameters for use within the isothermal DFN framework, discusses their advantages or disadvantages, and clarifies limitations attached to their practical application. Accompanying this discussion we provide a searchable database, available at www.liiondb.com, which aggregates many parameters and state functions for the standard Doyle-Fuller-Newman model that have been reported in the literature.

DandeLiion (available at dandeliion.com) is a robust and extremely fast solver for the Doyle Fuller Newman (DFN) model, the standard electrochemical model for (dis)charge of a planar lithium-ion cell. DandeLiion conserves lithium, uses a second order spatial discretisation method (enabling accurate computations using relatively coarse discretisations) and is many times faster than its competitors. The code can be used "in the cloud" and does not require installation before use. The difference in compute time between DandeLiion and its commercial counterparts is roughly a factor of 100 for the moderately-sized test case of the discharge of a single cell. Its linear scaling property means that the disparity in performance is even more pronounced for bigger systems, making it particularly suitable for applications involving multiple coupled cells. The model is characterised by a number of phenomenological parameters and functions, which may either be provided by the user or chosen from DandeLiion's library. This library contains data for the most commonly used electrolyte (LiPF6) and a number of common active material chemistries including graphite, lithium iron phosphate (LFP), nickel cobalt aluminum (NCA), and a variant of nickel cobalt manganese (NMC).

The Newman model for charge transport within porous electrodes was developed by Newman, Fuller and Doyle in the mid-90s, and has become the standard tool for simulating Li-ion cell (dis)charge. The model is comprised of a system of nonlinear partial differential equations that must, in general, be solved numerically. The computational costs associated with solving the Newman model, even for a single planar cell, are relatively high; for example, most solvers take several minutes to compute a single discharge curve. This makes its application to computationally expensive problems (such as cell optimisation, parameter estimation or battery pack simulation) problematic. Motivated by the lack of a fast Newman solver in the literature we have developed a new software tool, named DandeLiion. This provides extremely fast code with which to solve the Newman model. It is straightforward to install, comes with concise and clear documentation, is fully parallelisable and free to use. As an example of its power it has the capability of simulating a full discharge curve in less than a second (with fully nonlinear transport and sufficient resolution to compute the solution to several digits of accuracy) which is approximately several hundred times faster than most commercial solvers. In addition, it is equipped with a library of battery chemistries that incorporates models for nonlinear lithium transport and open circuit voltage in most active electrode materials, and for lithium transport and conduction in the most common electrolytes. This gives the user the ability to simulate a wide range of devices designs. It can also be used to simulate realistic drive cycle data. In order to illustrate the power of this software we perform a thermally coupled simulation in a realistic pouch cell geometry, using DandeLiion to compute the electrochemical discharge of the cell stack in the resulting three-dimensional evolving temperature field. We note that such thermally coupled pack simulations are normally performed using simple equivalent circuit models of the cell, rather than the Newman model, because the computational cost associated with a thermally heterogenous Newman model has, up until now, been too high.

The main barrier to fast charging of Li-ion batteries at low temperatures is the risk of short-circuiting due to lithium plating. In-situ detection of Li plating is highly sought after in order to develop fast charging strategies that avoid plating. It is widely believed that Li plating after a single fast charge can be detected and quantified by using a minimum in the differential voltage (DV) signal during the subsequent discharge, which indicates how much lithium has been stripped. In this work, a pseudo-2D physics-based model is used to investigate the effect on Li plating and stripping of concentration-dependent diffusion coefficients in the active electrode materials. A new modelling protocol is also proposed, in order to distinguish the effects of fast charging, slow charging and Li plating/stripping. The model predicts that the DV minimum associated with Li stripping is in fact a shifted and more abrupt version of a minimum caused by the stage II-stage III transition in the graphite negative electrode. Therefore, the minimum cannot be used to quantify stripping. Using concentration-dependent diffusion coefficients yields qualitatively different results to previous work. This knowledge casts doubt on the utility of DV analysis for detecting Li plating.

In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

The current decay in response to a sudden change of applied bias up to 1 V has been measured on a methylammonium lead triiodide perovskite solar cell with titania and spiro-OMeTAD transport layers, for temperatures between 258 and 308 K. These measurements are highly reproducible, in contrast to most other techniques used to investigate perovskite cells. A drift-diffusion model that accounts for slow moving ions as well as electrons and holes acting as charge carriers was used to predict the current transients. The close fit of the model predictions to the measurements shows that mobile ions in the perovskite layer influence transient behaviour on timescales of up to 50 s. An activation energy of 0.55 eV is inferred from fitting simulations to measurements made at room temperature.

There are two main methods for modelling the electronic properties of perovskite devices. Drift-diffusion modelling [1,2,3] works by solving differential equations, while equivalent circuit modelling [4,5] works by treating different parts of the device as electrical components. This work focuses on drift-diffusion modelling. Most drift-diffusion models of perovskite use finite-difference methods [1,3], which solve the differential equations at equally-spaced grid points. These models have achieved a valuable qualitative description of the influence of ion motion on the electrical characteristics of perovskite solar cells. However, in order to relate the model predictions to microscopic material properties, a more quantitative model is required. We use a hybrid model [2] where an asymptotic approximation is used to model the accumulation of ionic charge at the interfaces between materials. The charge accumulation, which is a function of time, is then input into a second model, in which the electron and hole concentrations across the film are expressed as a sum of Chebyshev polynomials using the MATLAB add-on module Chebfun. [6] In this work, the ability of different drift-diffusion modelling methods to reproduce experimental current-voltage measurements quantitatively is compared. References [1] S. van Reenen, M. Kemerink and H. J. Snaith, J. Phys. Chem. Lett. (2015), Vol. 6, 1511-1515 [2] G. Richardson et al, Energy Environ. Sci. (2016), Vol 9, 1476-1485 [3] P. Calado et al., arXiv (2016), https://arxiv.org/abs/1606.00818 [4] A. Pockett et al., J. Phys. Chem. C. (2015), Vol. 119, 3456-3465 [5] L. Cojocaru et al., Chem. Lett. (2015), Vol. 44, 1750-1752 [6] T. A. Driscoll, N. Hale and L. N. Trefethen, Chebfun Guide (2015)