High-rate lithiation-induced reactivation of mesoporous hollow spheres for long-lived lithium-ion batteries
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Degradation in lithium-ion batteries occur mainly due to the loss of cyclable lithium and loss of active material in the electrodes. 1 The lifetime of a battery is limited by the reduction in capacity as the battery is cycled, which can occur through mechanisms such as formation and growth of the solid interface layer (SEI), electrolyte decomposition, dissolution of transition metals at the cathode, lithium deposition at the anode, and porosity changes. 2 , 3 While there are many physics-based degradation models that exist in the literature, the ability to accurately predict capacity and power fade over many cycles is limited. This is because not all capacity fade mechanisms are well understood or incorporated in a single model, and a wide range of parameters and different operating conditions make it difficult to arrive at a consistent model. 2 , 4 In this study, we look at SEI models in the literature such as Ramadass’ kinetically limited model, 5 Ploehn’s diffusion limited model, 6 and Safari’s mixed model, 7 and apply them to the single particle model (SPM). Mechanisms proposed by Lin 8 and Delacourt 4 that model side reactions at the cathode will also be explored. We look at the effects of charging/discharging rates and depth of discharge on the degradation during cycling, and the effects of temperature and state of charge on degradation during storage. We will also analyze how predicted degradation changes for different parameter ranges. A rubric of qualitative trends that have been observed experimentally will be used to determine the consistency of existing models. References P. Arora, R. E. White, and M. Doyle, J. Electrochem. Soc. , 145 , 3647–3667 (1998). V. Ramadesigan, K. Chen, N. A. Burns, V. Boovaragavan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc. , 158 , A1048–A1054 (2011). X. G. Yang, Y. Leng, G. Zhang, S. Ge, and C. Y. Wang, J. Power Sources , 360 , 28–40 (2017). C. Delacourt and M. Safari, J. Electrochem. Soc. , 159 , A1283–A1291 (2012). P. Ramadass, B. Haran, R. White, and B. N. Popov, J. Power Sources , 123 , 230–240 (2003). H. J. Ploehn, P. Ramadass, and R. E. White, J. Electrochem. Soc. , 151 , A456–A462 (2004). M. Safari, M. Morcrette, A. Teyssot, and C. Delacourt, J. Electrochem. Soc. , 156 , A145–A153 (2009). X. Lin, J. Park, L. Liu, Y. Lee, A. M. Sastry, and W. Lu, J. Electrochem. Soc. , 160 , A1701–A1710 (2013).
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“Polarization increase reduces capacity” is frequently used to explain capacity fading in rechargeable batteries. To verify this empirical law, failure mode and effect analysis (FMEA) was used to identify capacity fade mechanism and derive the contribution of each failure mode in graphite–LiCoO 2 cells cycled between 3.00 V and 4.35 V. The thermodynamic and kinetic attributes to the capacity fade at the material, electrode, and cell levels were quantified respectively. Loss of Li inventory dominates in the capacity fade, followed by the loss of active materials in the electrodes. The capacity loss due to the impedance increase in the cell was relatively insignificant, contrary to what often conceived. This work emphasizes the importance of using quantitative FMEA to assess cell degradation and conduct failure analysis so the contributions from material, electrode, to the cell level can be distinctly identified. The polarization increase does not affect the charge retention significantly.
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Nickel-rich layered oxides have been widely used as positive electrode materials for high-energy-density lithium-ion batteries, but the underlying mechanisms of their degradation have not been well understood. Here we present a model at the particle level to describe the structural degradation caused by phase transition in terms of loss of active material (LAM), loss of lithium inventory (LLI), and resistance increase. The particle degradation model is then incorporated into a cell-level P2D model to explore the effects of LAM and LLI on capacity fade in cyclic ageing tests. It is predicted that the loss of cyclable lithium (trapped in the degraded shell) leads to a shift in the stoichiometry range of the negative electrode but does not directly contribute to the capacity loss, and that the loss of positive electrode active materials dominates the fade of usable cell capacity in discharge. The available capacity at a given current rate is further decreased by the additional resistance of the degraded shell layer. The change pattern of the state-of-charge curve provides information of more dimensions than the conventional capacity-fade curve, beneficial to the diagnosis of degradation modes. The model has been implemented into PyBaMM and the source codes are openly available in the GitHub repository https://github.com/mzzhuo/PyBaMM/tree/pe_degradation.
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Cell voltage inconsistency of battery module is correlated with cell capacity fading inconsistency caused by uneven temperature or improper charge/discharge rate, so it is essential to study on cell voltage inconsistency when establishing a battery module capacity fade model. An accelerated life experiment is conducted on 12-series (12S) LiMn2O4 battery. The evaluation index of the voltage inconsistency is given, and the evolution of the voltage of the 12S battery module is obtained. Furthermore, a model of capacity fade of this 12S battery is established based on cell voltage inconsistency, which is extended to any-series battery by means of probability distribution. Based on this extended model, the relationship between the number of cells and the life of the battery is obtained.
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The capacity of a lithium‐ion battery decreases during cycling. This capacity loss or fade occurs due to several different mechanisms which are due to or are associated with unwanted side reactions that occur in these batteries. These reactions occur during overcharge or overdischarge and cause electrolyte decomposition, passive film formation, active material dissolution, and other phenomena. These capacity loss mechanisms are not included in the present lithium‐ion battery mathematical models available in the open literature. Consequently, these models cannot be used to predict cell performance during cycling and under abuse conditions. This article presents a review of the current literature on capacity fade mechanisms and attempts to describe the information needed and the directions that may be taken to include these mechanisms in advanced lithium‐ion battery models.
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In order to develop long-lifespan batteries, it is of utmost importance to identify the relevant aging mechanisms and their relation to operating conditions. The capacity loss in a lithium-ion battery originates from (i) a loss of active electrode material and (ii) a loss of active lithium. The focus of this work is the capacity loss caused by lithium loss, which is irreversibly bound to the solid electrolyte interface (SEI) on the graphite surface. During operation, the particle surface suffers from dilation, which causes the SEI to break and then be rebuilt, continuously. The surface dilation is expected to correspond with the well-known graphite staging mechanism. Therefore, a high-power 2.6 Ah graphite/LiNiCoAlO2 cell (Sony US18650VTC5) is cycled at different, well-defined state-of-charge (SOC) ranges, covering the different graphite stages. An open circuit voltage model is applied to quantify the loss mechanisms (i) and (ii). The results show that the lithium loss is the dominant cause of capacity fade under the applied conditions. They experimentally prove the important influence of the graphite stages on the lifetime of a battery. Cycling the cell at SOCs slightly above graphite Stage II results in a high active lithium loss and hence in a high capacity fade.
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