Imperial College London


Faculty of EngineeringDepartment of Mechanical Engineering

Research Associate







409City and Guilds BuildingSouth Kensington Campus





Publication Type

8 results found

O'Kane SEJ, Kirkaldy N, Offer GJ, Marinescu Met al., 2022, Lithium-Ion Battery Degradation: How to Diagnose It, ECS Meeting Abstracts, Vol: MA2022-01, Pages: 396-396

<jats:p> 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].</jats:p> <jats:p>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.</jats:p> <jats:p>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.</jats:p> <jats:p>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.</jats:p> <jats:p>[1] Jacqueline Edge <jats:italic>et al.</jats:italic>, <jats:italic>Phys. Chem.: Chem. Phys.</jats:italic> vol. 23, pp. 8200-8221, 2021.</jats:p> <jats:p>[2] Christoph Birkl <jats:italic>et al.</jats:italic>, <jats:italic>Journal of Power Sources</jats:italic> vol. 341, pp. 373-386, 2017.</jats:p> <jats:p>[3] Matthieu Dubarry &l

Journal article

Wang AA, OKane SEJ, Brosa Planella F, Houx JL, ORegan K, Zyskin M, Edge J, Monroe CW, Cooper SJ, Howey DA, Kendrick E, Foster JMet al., 2022, Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models, Progress in Energ, Vol: 4, Pages: 1-40, ISSN: 2516-1083

The Doyle–Fuller–Newman (DFN) 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, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature.

Journal article

Li R, O'Kane S, Marinescu M, Offer GJet al., 2022, Modelling solvent consumption from SEI layer growth in lithium-ion batteries, Journal of The Electrochemical Society, Vol: 169, Pages: 1-14, ISSN: 0013-4651

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 dominantdegradation mechanism for LIBs. SEI growth consumes cyclable lithium and leads to capacity fade and power fade via severalpathways. However, SEI growth also consumes electrolyte solvent and may lead to electrolyte dry-out, which has only beenmodelled in a few papers. These papers showed that the electrolyte dry-out induced a positive feedback loop between loss of activematerial (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 greatlyimprove their service life. This study provides new insights into the degradation of LIBs and a tool for cell developers to designlonger lasting batteries.

Journal article

O'Kane SEJ, Ai W, Madabattula G, Alvarez DA, Timms R, Sulzer V, Edge JS, Wu B, Offer GJ, Marinescu Met al., 2022, Lithium-ion battery degradation: how to model it, Publisher: Royal Society of Chemistry

Predicting lithium-ion battery degradation is worth billions to the globalautomotive, aviation and energy storage industries, to improve performance andsafety and reduce warranty liabilities. However, very few published models ofbattery degradation explicitly consider the interactions between more than twodegradation mechanisms, and none do so within a single electrode. In thispaper, the first published attempt to directly couple more than two degradationmechanisms in the negative electrode is reported. The results are used to mapdifferent pathways through the complicated path dependent and non-lineardegradation space. Four degradation mechanisms are coupled in PyBaMM, an opensource modelling environment uniquely developed to allow new physics to beimplemented and explored quickly and easily. Crucially it is possible to see'inside' the model and observe the consequences of the different patterns ofdegradation, such as loss of lithium inventory and loss of active material. Forthe same cell, five different pathways that can result in end-of-life havealready been found, depending on how the cell is used. Such information wouldenable a product designer to either extend life or predict life based upon theusage pattern. However, parameterization of the degradation models remains as amajor challenge, and requires the attention of the international batterycommunity.

Working paper

O'Kane SEJ, Campbell ID, Marzook WWJ, Offer GJ, Marinescu Met al., 2021, Physical Origin of the Differential Voltage Minimum Associated With Lithium Plating in Li-Ion Batteries, ECS Meeting Abstracts, Vol: MA2021-02, Pages: 466-466

Journal article

Edge JS, O'Kane S, Prosser R, Kirkaldy ND, Patel AN, Hales A, Ghosh A, Ai W, Chen J, Yang J, Li S, Pang M-C, Bravo Diaz L, Tomaszewska A, Marzook MW, Radhakrishnan KN, Wang H, Patel Y, Wu B, Offer GJet al., 2021, Lithium ion battery degradation: what you need to know, Physical Chemistry Chemical Physics, Vol: 23, Pages: 8200-8221, ISSN: 1463-9076

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.

Journal article

O'Kane SEJ, Campbell ID, Marzook MWJ, Offer GJ, Marinescu Met al., 2020, Physical origin of the differential voltage minimum associated with lithium plating in Li-Ion batteries, Journal of The Electrochemical Society, Vol: 167, Pages: 1-11, ISSN: 0013-4651

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.

Journal article

Tomaszewska A, Chu Z, Feng X, O'Kane S, Liu X, Chen J, Ji C, Endler E, Li R, Liu L, Li Y, Zheng S, Vetterlein S, Gao M, Du J, Parkes M, Ouyang M, Marinescu M, Offer G, Wu Bet al., 2019, Lithium-ion battery fast charging: A review, eTransportation, Vol: 1, Pages: 1-28, ISSN: 2590-1168

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 in operando 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.

Journal article

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