Lithium-ion Cells Research Articles

Influence of pin fins and channel geometry on the hydrothermal performance of hexagonal mini-channel heat sink for cooling cylindrical batteries

This study numerically and experimentally examined the effects of pin fin configuration and channel geometry on the performance of a hexagonal mini-channel heat sink (HMCHS) to cool cylindrical lithium-ion battery cells. Various configurations of HMCHS with circular pin fins are studied, including circular fins centrally positioned within flat channels, circular fins positioned within grooved channels, circular fins situated within hybrid channels transitioning between wavy and straight channels and circular fins placed within hybrid channels that incorporate secondary flow channels between the primary flow channels. This numerical study utilized the finite volume method to investigate the laminar water flow at Reynolds numbers below 800. Findings showed that the HMCHS exhibited superior thermal performance compared with the conventional cylindrical mini-channel heat sink design. Moreover, the incorporation of pin fins into the HMCHS design was more effective in enhancing the Nusselt number than simply increasing the Reynolds number across various configurations. Meanwhile, the hybrid channel design with secondary channels, combined with the addition of pin fins, achieved the optimal overall performance of the HMCHS, resulting in the highest performance index of 1.88.

Impact of State of Health (SOH) on the Thermal Safety of Lithium Ion Cells for Long 1st Life and 2nd Life Applications

Lithium ion battery (LIB) safety incidents can be a threat for people and the environment. Since today, only safety tests on fresh cells are decisive for safety level determination, the effect of long-term operation on their safety characteristics needs to be addressed. A large loss of lithium over long periods of time could, for example, result in reduced structural and thermal stability of the cathode. LIBs are normally used until they reach an end-of-life criterion of typically 70%–80% state of health (SOH). However, they can be reused in second-life applications such as stationary (“grid”) energy storage, afterwards. To ensure safety during long first life and second-life, in this study the influence of aging was investigated over a higher cycle number and a longer time period than ever before. 5 Ah LiNi0.6Co0.2Mn0.2O2(NMC622)||graphite (G) pouch cells were aged at 20 °C between 530 and 3,806 cycles (151–615 d of continuous cycling). SOHs between 91% and 63% were obtained. After aging, the thermal properties of the cells were investigated by heat-wait-search experiments under adiabatic conditions using an accelerating rate calorimeter. The cells showed almost exclusively improvements in their safety characteristics, the thermal runaway even tended to be shifted to higher temperatures.

Bayesian impedance deconvolution using timescale distribution for lithium-ion battery state estimation

The distribution of relaxation times (DRT) and distribution of diffusion times (DDT) are widely recognized as effective model-free methods for deconvolving the internal properties of complex electrochemical systems using electrochemical impedance spectroscopy (EIS) data. This study proposes an integrated framework that employs a Bayesian approach to accurately estimate both DRT and DDT and machine learning techniques to enhance capacity estimation, incorporating Gaussian process regression and transformer networks. These methods utilize the inferred DRT and DDT as inputs to predict the discharge capacity. In addition, we perform a peak analysis on the estimated DRT and DDT to extract additional physically meaningful features, which have also proven to be effective inputs for capacity prediction. The applicability of the proposed framework to the EIS experimental data of lithium-ion cells is demonstrated and compared with existing capacity estimation methods. The proposed framework demonstrates a mean absolute error in capacity prediction below 3.03% when using DRT and DDT directly and 2.71% when using extracted features, outperforming existing methods by approximately one to three percentage points. Our Bayesian approach, combined with peak analysis and the integration of machine learning techniques, enables a more robust diagnosis of the internal state of lithium-ion batteries through EIS measurements.

Data driven approach for state-of-charge estimation of lithium-ion cell using stochastic variational Gaussian process

Modern electric vehicles rely on lithium-ion batteries. Electric vehicles (EVs) utilize intricate battery packs that require the oversight of a battery management system (BMS) to ensure safe, reliable, and efficient operation. The state estimation of the battery pack is an important responsibility carried out by the BMS. Accurately estimating the State-of-Charge (SOC) poses a considerable engineering challenge since it cannot be directly measured at the battery terminals. This study introduces a novel data-driven methodology for accurately estimating the SOC in Lithium-ion batteries, with a particular focus on its relevance in EV contexts. The framework is built upon the Stochastic Variational Gaussian Process (SVGP)—an improved version of the conventional Gaussian Process (GP). Unlike GP, It can scale up to very large datasets. Furthermore, SVGP uses variational inference to estimate the posterior instead of calculating, making it computationally efficient. The model training process involves using laboratory test data from an 18650 Lithium-ion Nickel Manganese Cobalt (NMC) cell that has gone through eight dynamic drive cycles. The findings showcase a remarkable level of precision in estimation, as indicated by an average R2 value of 0.99 and a Mean Square Error (MSE) as low as 0.02.

Study on the effects of electrode volume fraction and electrode particle radius on dynamic electrochemical-thermal-aging coupling characteristics of lithium cell based on cylindrical cell design method

Electrode volume fraction and electrode particle radius are important electrode parameters for the lithium cell design and deeply influence the electrochemical-physical processes inside lithium cell, which is still insufficiently clear. In order to obtain the effects of electrode parameters on lithium cell, a combine method of the dynamic electrochemical-thermal-aging coupling model and cylindrical cell design was proposed. Simulation was conducted at charge rate 1C. Cell temperature, current density and Solid-Electrolyte-Interface at ambient temperature 25 °C, and total strain energy density and lithium plating at ambient temperature −5 °C were analyzed. As electrode volume fraction or electrode particle radius increases, maximum temperature and maximum temperature difference of lithium cell increase. When electrode volume fraction increases from 0.3 to 0.7, maximum temperature of cell increases by 2.6 °C at least. Current density peak increases with the increase of the positive electrode volume fraction or electrode particle radius. When electrode volume fraction increases from 0.3 to 0.7, current density peak increases by 44 %∼74 %. As electrode volume fraction increases, total strain energy density Ws decreases in the early stage of charge, while increases in the late stage of charge. As the negative (or positive) electrode particle radius increases, Ws of the negative (or positive) electrode increase, while Ws of the positive (or negative) electrode decreases. Solid-Electrolyte-Interface thickness increases with the increase of the electrode volume fraction or electrode particle radius. The effect range of negative electrode volume on lithium plating is 0.5 ∼ 0.7. The lithium metal thickness increase with the increase of the positive electrode volume fraction. As the negative or positive electrode particle radius increases, the lithium metal thickness increase or decrease, respectively.

Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data

Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts.

Understanding the heat generation mechanisms and the interplay between joule heat and entropy effects as a function of state of charge in lithium-ion batteries

The thermal performance of lithium-ion battery cells is critical for ensuring their safe and reliable operation across various applications. In this study, we employed an isothermal calorimetry method to investigate the heat generation of commercial 18650 lithium-ion battery fresh cells during charge and discharge at different current rates, ranging from 0.05C to 0.5C, and across various temperatures: 20 °C, 30 °C, 40 °C, and 50 °C. Our findings revealed a direct correlation between heat generation and current rates, indicating that higher current rates lead to increased heat generation within the cells. Conversely, we observed that heat generation remained relatively stable as the temperature rose, suggesting that temperature changes within this range may not significantly impact the heat generation of fresh cells during typical operations. Furthermore, our study explored irreversible heat generation, which depends on the applied current and overpotential, using the galvanostatic intermittent titration technique at 0.05C–0.5C and 30 °C. Additionally, electrochemical impedance spectroscopy was performed on the same cells during charge and discharge at 20 °C, 30 °C, and 40 °C to analyze cell impedance. Our results indicated a consistent dependence of impedance on the state of charge and depth of discharge, with a significant increase in impedance observed at the end of the discharge process.

Performance of Protection Devices Integrated into Lithium-Ion Cells during Overcharge Abuse Test

Lithium-ion batteries currently represent the most suitable technology for energy storage in various applications, such as hybrid and electric vehicles (HEVs and BEVs), portable electronics and energy storage systems. Their wide adoption in recent years is due to their characteristics of high energy density, high power density and long life cycle. On the other hand, they still face challenges from a safety point of view for the possible faults that could generate several problems, ranging from simple malfunctioning to a dangerous thermal runaway. Overcharge is one of the most critical types of faults, and, depending on the level of abuse, it may trigger a thermal runaway. To prevent high levels of overcharge abuse, some cells include integrated protection devices that cut off the circuit when a critical condition is met. In this paper, the performance of these protection devices is evaluated to assess their effectiveness. The cells were tested at different ambient temperatures and current levels. In the worst-case scenarios, the maximum cell temperature slightly exceeded 70 °C and the State of Charge (SOC) reached a peak of 127% when the Current Interruption Device (CID) was activated. These conditions were not critical, so serious events such as thermal runaway were not triggered. These outcomes confirm the effectiveness of the CID, which always intervenes in maintaining a safe state. However, since it never intervened in the overcharge abuse tests, a specific set up was also used to investigate the operation of the other protection device, the Positive Temperature Coefficient.

Visualization of stepwise electrode decomposition in a nail penetrated commercial lithium-ion cell using low-temperature synchrotron X-ray computed tomography

The transition towards zero carbon emissions in power generation hinges on the integration of efficient electrical energy storage systems, with lithium-ion batteries (LIBs) positioned as a pivotal technology. While generally safe, deviations in their operational guidelines due to manufacturing defects or misuse can lead to critical safety concerns, notably thermal runaway (TR) events. Internal short circuits (ISCs) are primary initiators of TR within LIBs. For abuse testing, ISCs are often triggered by nail penetration. This study explores the morphological changes and mechanisms underlying ISC-induced TR in LIBs using operando synchrotron X-ray computed tomography (SXCT) at subzero temperatures. A novel cryogenic setup was developed to control a stepwise temperature increase in the damaged sample while monitoring electrochemical characteristics and simultaneously enabling acquisition of high-resolution SXCT images. The findings reveal that conducting nail penetration at minus 80 °C prevents immediate TR, enabling detailed analysis of subsequent structural and electrochemical behavior during controlled thawing. Thus, the initiation of TR processes at localized ISC sites has been observed, evidenced by voltage fluctuations and morphological changes, such as cathode material cracking and decomposition. These results underscore the importance of temperature control in mitigating TR risks and provide critical insights into the internal dynamics of LIBs under abusive conditions. The developed cryogenic SXCT methodology offers a powerful tool for non-destructive, high-resolution investigation of battery failure mechanisms, contributing to the enhancement of LIB safety.

Quantifying the Aging of Lithium-Ion Pouch Cells Using Pressure Sensors

Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 °C, 25 °C, and 40 °C) on the increase in battery pressure is investigated. By analyzing the change in the minimum, maximum, and pressure difference per cycle, we identify and discuss the effects of different factors (i.e., SEI layer damage, electrolyte decomposition, lithium plating) on the pressure behavior. Operating at high C-rates or low temperatures rapidly increases the residual pressure as the battery is cycled. The results suggest that lithium plating is predominantly responsible for battery expansion and pressure increase during the cycle aging of Li-ion cells rather than electrolyte decomposition. Electrochemical impedance spectroscopy (EIS) measurements can support our conclusions. Postmortem analysis of the aged cells was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to confirm the occurrence of lithium plating and film growth on the anodes of the aged cells. This study demonstrates that pressure measurements can provide insights into the aging mechanisms of Li-ion batteries and can be used as a reliable predictor of battery degradation.

A multiscale model to understand the interface chemistry, contacts, and dynamics during lithium stripping

A reversible Li-metal electrode, paired with a solid electrolyte, is critical for attaining higher energy density and safer batteries beyond the current lithium-ion cells. A stable stripping process may be even harder to attain as the stripping process will remove Li-atoms from the surface, and naturally reduce surface contact area, if not self-corrected by other mechanisms, such as diffusion and plastic deformation under an applied external stack pressure. Here, we capture these mechanisms occurring at multiple length- and time- scales, i.e., interface interactions, vacancy hopping, and plastic deformation, by integrating density functional theory (DFT) simulations, kinetic Monte Carlo (KMC), and continuum finite element method (FEM). By assuming the self-affine nature of multiscale contacts, we predict the steady-state contact area as a function of stripping current density, interface wettability, and stack pressure. We further estimate the exponential increase of overpotential due to contact area loss to maintain the same stripping current density. We demonstrate that a lithiophilic interface requires less stack pressure to reach the same steady-state contact area fraction than a lithiophobic interface. A “tolerable steady-state” contact area loss for maintaining stable stripping is estimated at 20 %, corresponding to a 10 % increase in overpotential. To constrain contact loss within the tolerance, the required stack pressure is 0.1, 0.5, and 2 times the yield strength of lithium metal for three distinct interfaces, lithiophilic Li/lithium oxide(Li2O), Li/lithium lanthanum zirconium oxide(LLZO), and lithiophoblic Li/lithium fluoride(LiF), respectively. The modeling results agree with experiments on the impact of the stack pressure quantitatively, while the discrepancy in stripping rate sensitivity is attributed to the simplifying interface interaction in our simulations. Overall, this multiscale simulation framework demonstrates the importance of electrochemical-mechanical coupling in understanding the dynamics of the Li/SE interface during stripping.

Age-related development of cell-to-cell variation under various operating conditions in commercial NMC/graphite lithium-ion cells

In real applications, lithium-ion cells are usually used in individual interconnections, with the worst cell determining capacity, performance and service life. Due to different ageing rates of the assembled cells, cell-to-cell variation changes over lifetime. In literature, cell-to-cell variation is usually investigated near room temperature under one operating condition. Data regarding cell-to-cell variation above and below room temperature under different operating conditions are hard to find. To fill the gap in literature, this study considers cell-to-cell variation as a function of different charge/discharge currents at ambient temperatures between 0°C and 40°C on commercial NMC/graphite cells (Samsung SDI ICR18650-26J). The results demonstrate that cell-to-cell variation strongly depends on the selected operating conditions. In particular, operating conditions near the lithium plating boundary at the beginning of the ageing process have a particularly high risk of developing a pronounced variation. Fast charging or charging at lower temperatures should therefore be avoided. Operation at moderate temperatures and currents, on the other hand, only leads to a slight increase in cell-to-cell variation in the tests carried out. A 24-month storage during the testing period at 30% SOC and room temperature of the same cells shows even a slight reduction in cell-to-cell variation.

The numerical and experimental investigation of the transient behaviours of a lithium-ion pouch battery cell under dynamic conditions

In this paper, the transient model of a high energy density Lithium-ion pouch battery cell is developed under dynamic conditions. The battery cell has a capacity of 73 Ah and volumetric energy density of 642 Wh L−1. This is one of the few studies included the electro-thermal behaviour of high energy density battery under transient conditions by using second ordered model. The transient model indicated that the state of charge (SOC) decreased by 7 % at the end of the WLTP driving cycle and this value is good agreement with the experimental data. Moreover, the developed model provides an accurate prediction of the terminal voltage with a R2 value of 0.99 and a maximum relative error of 2.0 %. Furthermore, the proposed model predicts the electro-thermal characteristics more precisely and the heat generation rate and the entropic term can also be determined without using measurement device. The calculated total heat emitted from battery is about 6W at 1 C-rate for constant current and the heat generation rate is a peak value of ±1.75 105 Wm−3 under dynamic conditions. By using the estimated heat generation rate, more effective cold plates will be designed for thermal management of high energy density battery cells.

Degradation mechanism and assessment for different cathode based commercial pouch cells under different pressure boundary conditions

Lithium-ion pouch cell exhibits expansion behavior during cycling, which determines the overall performance and external pressure evolution. It is significant to reveal the impact of pressure boundary conditions on battery degradation. This study investigated the degradation mechanism of different cathode-based pouch cells (LiFePO4 (LFP) and Li(Ni0.6Co0.2Mn0.2)O2 (NCM622)) under four distinct pressure boundary conditions (I-II. clamping with two foam pads having varying compression force deflection (CFD) respectively, III. without foam pad, IV. unpressurized). One hundred of pre-experiment cycles determined the optimal external pressure (12.5 kPa) to slow down the degradation process. Subsequently, multi-cell paralleled cycling experiments were conducted for 1000 cycles. The capacity degradation of LFP and NCM cells with the optimal condition was 6.0 % and 12.6 % less than that of unpressurized cells. The electrochemical tests indicated that unpressurized cells had the fastest rate of lithium-ion loss, which is related to solid electrolyte interface (SEI) film growth and lithium plating. Post-mortem characterization analysis further revealed that the pressure boundary condition is crucial for the electrode morphology, pressure distribution, and gas generation, which affect battery degradation. The pressurized cells’ surface exhibited fewer instances of lithium plating. With the stiff foam clamped, pouch cell swelling can be absorbed while maintaining contact between cell components, resulting in fewer electrode folds and cracks. This study provides valuable insights for designing long-lifespan battery modules or packs.

A multi-stage lithium-ion battery aging dataset using various experimental design methodologies

This dataset encompasses a comprehensive investigation of combined calendar and cycle aging in commercially available lithium-ion battery cells (Samsung INR21700-50E). A total of 279 cells were subjected to 71 distinct aging conditions across two stages. Stage 1 is based on a non-model-based design of experiments (DoE), including full-factorial and Latin hypercube experimental designs, to determine the degradation behavior. Stage 2 employed model-based parameter individual optimal experimental design (pi-OED) to refine specific dependencies, along with a second non-model-based approach for fair comparison of DoE methodologies. While the primary aim was to validate the benefits of optimal experimental design in lithium-ion battery aging studies, this dataset offers extensive utility for various applications. They include training of machine learning models for battery life prediction, calibrating of physics-based or (semi-)empirical models for battery performance and degradation, and numerous other investigations in battery research. Additionally, the dataset has the potential to uncover hidden dependencies and correlations in battery aging mechanisms that were not evident in previous studies, which often relied on pre-existing assumptions and limited experimental designs.

Low-carbon upcycling of spent anode graphite: Integrating graphene and dislocations for sustainable lithium/potassium storage

Nowadays, the recycling and sustainable energy storage techniques of spent rechargeable batteries have garnered tremendous passion and scrutiny on account of the escalating environmental crisis and energy shortage. This study presents a facile and green method for upcycling graphite from spent lithium-ion batteries. Unlike conventional high-temperature calcination approaches, our technique leverages low-temperature annealing at 350 °C in air to treat graphite from spent anodes after diluted nitric acid (5 mol/L) leaching. This craft is characterized by its simplicity, safety, and cost-effectiveness. Experimental results reveal that the upcycled graphite stimulated both lithium and potassium storage. Specifically, the upcycled graphite maintains the boosting capacity of 415.6 mAh/g after 180 cycles in lithium-ion cells. This stellar performance is attributed to the formation of graphene sheets, abundant dislocations, and the preservation of ample interlayer spacing. Our method substantially reduces energy consumption and environmental pollution. Future research will further explore the applications of upcycled graphite in other types of ion batteries and mitigate its environmental impacts.

Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell

Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies help to better understand and model degradation and to optimize the operating strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications. To our knowledge, the dataset1 presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to cells. The dataset1 includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution). The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operating strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.

Investigation of the Effects Caused by Current Interruption Devices of Lithium Cells at High Overvoltages

A faulty voltage measurement can lead to the overcharging of a Li-Ion cell, resulting in gas formation and heating inside the cell, which can trigger thermal runaway. To mitigate this risk, cylindrical cells are equipped with a Current Interrupt Device (CID), which functions as a pressure relief valve, disconnecting the electrical circuit within the cell when internal pressure rises. However, this disconnection causes the cell to suddenly become highly resistant, posing a significant issue in series-connected cells. In such configurations, a portion or even the entire system voltage may drop across the disconnected cell, substantially increasing the likelihood of an electric arc. This arc could ignite any escaping flammable gases, leading to catastrophic failures. In a series of tests conducted on three different cell chemistries—NMC (Nickel Manganese Cobalt), NCA (Nickel Cobalt Aluminum), and LFP (Lithium Iron Phosphate)—it was found that the safe operation of the CID cannot be guaranteed for system voltages exceeding 120 V. Although comparative tests at double the nominal cell voltage did not exhibit the same behavior, these findings suggest that current safety standards, which recommend testing at double the nominal voltage, may not adequately address the risks involved. The tests further revealed that series connections of cells with CIDs are inherently dangerous, as, in the worst-case scenario, the entire system voltage can be concentrated across a single cell, leading to potential system failure.

A hybrid data-driven approach for state of health estimation in lithium-ion batteries

ABSTRACT The assessment of the state of health (SOH) in lithium-ion batteries is essential for ensuring safe operation and implementing scientific management practices. This paper proposes a novel hybrid data-driven approach for accurately estimating the SOH of lithium-ion cells. Initially, the sparrow search algorithm (SSA) was implemented to optimize the structural configuration of a hybrid long-short-term memory neural network and convolutional neural network model. Second, four health indicators, namely, constant current charging time (CCCT), constant current charging capacity (CCCC), discharging time (DT), and discharging capacity (DC), were identified as inputs to the estimation model by Pearson correlation analysis. The CALCE battery dataset is utilized for conducting experiments, yielding remarkably low average values of root mean square error (RMSE) and mean absolute error (MAE), standing at merely 0.6737% and 0.5286%, respectively. And comparing with GRU, TCN, CNN, LSTM, and PSO optimization models, the proposed method has higher SOH estimation accuracy, which is more than 11% better than that. The robustness of three additional kinds of batteries is tested to validate their performance under varying current rates and temperatures. The results demonstrate that the accurate estimation of SOH, and the estimated values’ relative errors for each cycle of the three different types of batteries are all basically within 1%, with their maximum average RMSE are only 0.4840%, 0.3609%, and 0.5681%, respectively, and their maximum average MAE are only 0.3900%, 0.2828%, and 0.4149%, respectively.

A Comparative Study of Nano and Micro-Sized Silicon in Lithium-Ion Cells with a Nickel-Rich Cathode

Reducing particle size has been widely adopted to mitigate the cracking and pulverization of silicon particles and to enhance electrode reaction kinetics for silicon electrodes in cycling. However, the increased surface area promotes parasitic reactions with electrolyte solvents. This work comparatively studies nano-sized silicon (Si-NP) and micro-sized silicon (Si-MP) as anodes in Li-ion cells using nickel-rich LiNi0.80Co0.1Mn0.1O2 (NCM811) as the cathode. The focus is on capacity, capacity retention, Coulombic efficiency (CE), and rate capability by changing the negative-to-positive capacity (N/P) ratio and charging cutoff voltage. It is found that Si-NP initially exhibits a CE above 90%, however, it rarely exceeds 98% in subsequent cycles, leading to rapid capacity fade. Additionally, increasing the N/P ratio and lowering the charging cutoff voltage does not obviously improve the cycling stability of Si-NP cells. Compared with Si-NP, Si-MP experiences lower capacity and lower CE in the initial several cycles. However, with continued cycling, both the capacity and CE gradually increase to a maximum and stably remain at ∼99.9%. The findings of this work suggest that, with its excellent rate capability, Si-MP may be more advantageous than Si-NP in developing practical Li-ion batteries, provided its low CE during initial cycles can be successfully addressed.