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
In an effort to increase the specific energy of lithium-ion batteries, silicon additives are often blended with graphite (Gr) in the negative electrode of commercial cells. However, due to the large volumetric expansion of silicon upon lithiation, these Si-Gr composites are prone to faster rates of degradation than conventional graphite electrodes. Understanding the effect of this difference is key to controlling degradation and improving cell lifetimes. Here, we investigate the effects of state of charge and temperature on the ageing of a commercial cylindrical cell with a Si-Gr electrode (LG M50T). Using degradation mode analysis, we were able to quantify the rates of degradation for Si and Gr separately. Loss of active Si is shown to be worse than Gr under all operating conditions, but especially at low state of charge and high temperature, with up to 80% loss in Si capacity after 4 kA h of charge throughput (~400 equivalent full cycles). The results indicate cell lifetimes can be improved by limiting the depth of discharge of cells containing Si-Gr, which suggests Si is not beneficial for all applications. The degradation mode analysis methods developed here provide valuable new insight into the causes of cell ageing by separating the effects of the two active materials in the composite electrode. These methods provide a suitable framework for data analysis of any experimental investigations on cells involving composite electrodes.
The demand for large format lithium-ion batteries is increasing, because they can be integrated and controlled easier at a system level. However, increasing the size leads to increased heat generation risking overheating. 1865 and 2170 cylindrical cells can be both base cooled or side cooled with reasonable efficiency. Large format 4680 cylindrical cells have become popular after Tesla filed a patent. If these cells are to become widely used, then understanding how to thermally manage them is essential. In this work, we create a model of a 4680 cylindrical cell, and use it to study different thermal management options. Our work elucidates the comprehensive mechanisms how the hot topic 'tabless design' improves the performance of 4680 cell and makes any larger format cell possible while current commercial cylindrical cells cannot be simply scaled up to satisfy power and thermal performance. As a consequence, the model identifies the reason for the tabless cell's release: the thermal performance of the 4680 tabless cell can be no worse than that of the 2170 cell, while the 4680 tabless tab cell boasts 5.4 times the energy and 6.9 times the power. Finally, via the model, a procedure is proposed for choosing the thermal management for large format cylindrical cell for maximum performance. As an example, we demonstrate that the best cooling approach for the 4680 tabless cell is base cooling, while for the 2170 LG M50T cell it is side cooling. We conclude that any viable large format cylindrical cell must include a continuous tab (or 'tabless') design and be cooled through its base when in a pack. The results are of immediate interest to both cell manufacturers and battery pack designers, while the developed modelling and parameterization framework is of wider use for all energy storage system design.
A degradation model for high-nickel positive electrode materials that undergo a structural reorganisation involving oxygen loss and the formation of a disordered (spinel or rock-salt structure) passivation layer is presented for the first time. The model is a thermally coupled continuum model based on the single-particle model and is based upon a LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) layered oxide in this instance. The theoretical framework assumes a shrinking core mechanism, where lattice oxygen, [O], release occurs at the interface between the bulk and the passivation layer, and the rate of reaction is controlled by either [O]-diffusion through the passivation layer or the reaction kinetics at the interface. As the passivation layer grows, the core of active positive electrode material shrinks giving rise to both loss in active material (LAM) and loss in lithium inventory (LLI) through trapping lithium in the passivation layer, giving rise to capacity fade. The slower diffusion of lithium through the passivation layer also gives rise to power fade. The model predicts two limiting cases, “ diffusion dominated” if [O]-diffusion is slow, and “reaction dominated” if [O]-diffusion is fast, relative to the reaction rate of [O]-release and also the thickness of the passivation layer.
Lithium-ion batteries commonly experience significant internal thermal gradients during operation which have a direct impact on the safety, performance, cost and lifetime of a cell. The estimation of the internal temperature of cells is therefore particularly important. In this work, a 3D distributed electro-thermal model for internal temperature estimation is developed for a cylindrical lithium-ion cell. The model is parameterized and comprehensively validated against experimental data for 21700 cylindrical cells, including direct core temperature measurements. Multiple types of electrical load are considered, including constant current discharge, pulse discharge, drive cycle and instant current-switching scenarios. The developed model is used to estimate core temperature based on the surface temperature measurement; its predictions are shown to have good accuracy. We show that the widely adopted two/three-node lumped thermal estimation model is increasingly inaccurate for more aggressive discharge conditions, when thermal gradients become higher. Compared to the standard three-node model, the distributed Equivalent Circuit Network model (dECN) contains the effects of features such as detailed internal cell structure and distributed internal heat generation. The results are of immediate interest to both cell manufacturers and battery pack designers, while the modelling and parameterization framework is a useful tool for energy storage systems design.
Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a 'behavioural' interpretation of the ECN model; as Li-S exhibits a 'steep' open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV.
Li-S batteries exhibit poor rate capability under lean electrolyte conditions required for achieving high practical energy densities. In this contribution, we argue that the rate capability of commercially-viable Li-S batteries is mainly limited by mass transfer rather than charge transfer during discharge. We first present experimental evidence showing that the charge-transfer resistance of Li-S batteries and hence the cathode surface covered by Li2S are proportional to the state-of-charge (SoC) and not to the current, directly contradicting previous theories. We further demonstrate that the observed Li-S behaviors for different discharge rates are qualitatively captured by a zero-dimensional Li-S model with transport-limited reaction currents. This is the first Li-S model to also reproduce the characteristic overshoot in voltage at the beginning of charge, suggesting its cause is the increase in charge transfer resistance brought by Li2S precipitation.
The battery management system of a hybrid electric vehicle requires a computationally simple yet accurate model of the battery.In this paper a reduced order battery model is developed using a stochastic top-down approach.Firstly a pseudo-2D, multi-particle electrochemical model, considered as a surrogate for the real system, is used to obtain the observational data.Then the model structure is inferred directly from the data.The dependencies between the states and the model parameters are analysed, which results in a 5 th order piecewise state dependent parameter model which can describe the nonlinear relationship between the current, the voltage and the state of charge of the battery.
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