Capacity Li-ion Batteries Research Articles

Self-assembled Si-based anode combined with electrostatic spinning method to realize high-performance lithium-ion batteries

Addressing the volume expansion when silicon and metal oxides alone are used as anode materials for lithium-ion batteries. This study used a simple self-assembly method and electrostatic spinning technique to prepare silicon@copper oxide@carbon nanofibres (CNFs) anodes with dual modification. The high rigidity of metal oxide CuO and the excellent cycling stability of CNFs effectively reduce the buildup of silicon particles, alleviate the volume expansion effect, and improve the electrical conductivity, which leads to better cycling stability and larger specific capacity of lithium-ion batteries. An excellent reversible specific capacity of 748.5 mAh g‐1−1 was observed after 800 cycles at a high current density of 1 A g−1. In addition, the surface of Si@CuO@CNFs electrodes remains smooth and undamaged after 800 cycles, and the increase in cross-sectional thickness is about 68 %, which is significantly smaller than the 300 % increase in cross-sectional thickness of pure Si anode and effectively improves the specific capacity of Li-ion batteries. This research optimizes the design of silicon-based anode materials with simple and mature process technology, which makes an indispensable contribution to developing high-efficiency, long-life, and environmentally friendly lithium-ion batteries. The easy availability and non-polluting nature of the materials used also effectively reduce the reliance on rare or expensive elements, minimize the production process's environmental impact, and vigorously promote the global energy transition and low-carbon green development strategy.

Cracks in Polycrystalline Li(NiMnCo)O2 Particles Enable Rapid Discharging of Li-Ion Batteries

The degradation of capacity in Li-ion batteries during charging and discharging cycles has been a persistent challenge in advancing battery lifetime. Among the commonly accepted causes of capacity degradation is the formation of cracking along the grain boundaries of polycrystalline NMC particles. These cracks are known to occur from the anisotropic expansion and contraction of the crystal lattice induced by the (de)intercalation of lithium.To address this issue, single crystalline NMC particles were introduced, lacking grain boundaries and seemingly less prone to developing cracks. batteries utilizing single crystalline particles exhibited improved capacity retention. However, in terms of rapid charging and discharging, these single particle batteries experienced a sudden increase in overpotential compared to polycrystalline particle batteries. Yet, a clear explanation for this abrupt rise in overpotential during rapid charging and discharging in single crystalline particle batteries has remained elusive.In this study, we investigated the rate-capability of individual single and polycrystalline NMC particles using an innovative high-throughput single-particle electrochemistry platform. Based on our rate-capability findings, we argue that the cracks, previously identified as a factor contributing to capacity degradation, are actually a crucial element enabling rapid charging and discharging in polycrystalline particles.

Joint prediction of the capacity and temperature of Li-ion batteries by using ConvLSTM Network

Joint prediction of the capacity and temperature of Li-ion batteries by using ConvLSTM Network

Unsupervised feature extraction for lithium-ion battery electrochemical impedance spectroscopy and capacity estimation using deep learning method

In this paper, we propose an unsupervised feature extraction technique using a variational autoencoder and generative adversarial network (VAEGAN) that automatically extracts meaningful latent variables from the electrochemical impedance spectra (EIS) of lithium-ion (Li-ion) batteries. These variables accurately reflect the degree of Li-ion battery degradation and are closely related to its capacity. By analyzing the latent variables, it was found that the network can learn the effect of Li-ion battery capacity degradation on the EIS. The extracted latent variables are then used as input features for Gaussian Process Regression (GPR) to estimate the capacity of Li-ion batteries under uneven usage and with unknown historical data. The results show that the capacity prediction method proposed in this paper can significantly reduce prediction errors compared to the current state-of-the-art methods. The method not only provides a more reliable capacity estimation but is also robust to highly noisy EIS data, making it more suitable for practical application scenarios.

Lithium Plating Characteristics in High Areal Capacity Li-Ion Battery Electrodes.

Li-ion battery degradation and safety events are often attributed to undesirable metallic lithium plating. Since their release, Li-ion battery electrodes have been made progressively thicker to provide a higher energy density. However, the propensity for plating in these thicker pairings is not well understood. Herein, we combine an experimental plating-prone condition with robust mesoscale modeling to examine electrode pairings with capacities ranging from 2.5 to 6 mAh/cm2 and negative to positive (N/P) electrode areal capacity ratio from 0.9 to 1.8 without the need for extensive aging tests. Using both experimentation and a mesoscale model, we identify a shift from conventional high state-of-charge (SOC) type plating to high overpotential (OP) type plating as electrode thickness increases. These two plating modes have distinct morphologies, identified by optical microscopy and electrochemical signatures. We demonstrate that under operating conditions where these plating modes converge, a high propensity of plating exists, revealing the importance of predicting and avoiding this overlap for a given electrode pairing. Further, we identify that thicker electrodes, beyond a capacity of 3 mAh/cm2 or thickness >75 μm, are prone to high OP, limiting negative electrode (NE) utilization and preventing cross-sectional oversizing the NE from mitigating plating. Here, it simply contributes to added mass and volume. The experimental thermal gradient and mesoscale model either combined or independently provide techniques capable of probing performance and safety implications of mild changes to electrode design features.

TensorRT Powered Model for Ultra-Fast Li-Ion Battery Capacity Prediction on Embedded Devices

TensorRT Powered Model for Ultra-Fast Li-Ion Battery Capacity Prediction on Embedded Devices

A technique for separating the impact of cycle aging and temperature on Li-ion battery capacity

Reduction of battery capacity is a well-known symptom of aging, making it a universally accepted indicator of the state of health. Capacity also significantly depends on temperature, therefore, separating the effect of temperature from that due to aging has utmost important for a proper state of health assessment. However, according to the latest literature, there is a lack of information about how the temperature dependency of capacity changes with battery aging. In this respect, the study presented in this paper is based on an experimental campaign aimed at measuring battery capacity at different temperatures and cycling levels. Starting from the obtained results, an analytical model describing how the variation law of battery capacity with temperature is affected by cycling was proposed and validated. The achieved accuracy is better than 0.6 % for all the considered operating conditions.

Design and Manufacturing of EV Go-Kart

Abstract: Go-karts are small-size and small-weight vehicles that were developed for racing. These are made with materials that are strong and durable. It consists of many parts which include a chassis, engine, steering and braking system, and electronic controls. The chassis is the main part that is responsible for the stability of the vehicle. Chassis is made with the material having greater endurance and rigidity. It was developed in the 1950s in the USA and now getting popular. These are now used in amusement parks as a recreational activity. Many researchers have done work on go-karts and improve their design. This project was intended to design and fabricate a reliable and durable go-kart. Its primary objective to build a go-kart using local resources and applying different techniques to limit the cost of vehicles. These objectives were achieved by going through a detailed literature review and studying different techniques which can be implemented. A reliable design was chosen which can be implemented and can be completed in our period. Critically evaluate the design of the vehicle and then different parts were designed. This paper presents an approach to a go-kart chassis design, vehicle dynamics calculation, Li-ion battery capacity analysis, and electric motor choice for optimized vehicle performance. The chassis analysis shown in this paper was performed using a CAD/FEA software package, SolidWorks Student Edition. Three highlights can be found in this paper: An original design was implemented; the basic analysis was composed of chassis optimization using beam elements and modelling such an optimized chassis “locally” with solid elements for sub-modelling purposes. The most stressed tube joint was sub-modelled to calculate the risk of tube wall stability. Vehicle dynamics were calculated for the case of braking on a curved path and the case of a collision with the front tire due to road imperfection. The authors intend to install a data acquisition system in the future to analyse the stress of local chassis tubes.

Predicting battery capacity with artificial neural networks

Li-ion batteries are a commonly used type of battery in various electronic devices and electric vehicles. The capacity of these batteries can decrease over time and affect the lifespan of the device. Therefore, predicting the capacity status of Li-ion batteries is important, there are several ways to estimate the SOC of a battery. When the literature was reviewed and relevant articles were examined, it was observed that artificial neural networks could be an effective tool for predicting the capacity status of Li-ion batteries. In this study, a study was conducted to predict the capacity status of Li-ion batteries using artificial neural networks. For this purpose, data collection, data preprocessing, and the use of artificial neural networks were carried out in stages for the prediction of the capacity status of Li-ion batteries. When the results obtained were examined, it was seen that artificial neural networks were able to correctly predict the capacity status of Li-ion batteries. The comparative analysis among various ANN models, including RNN, LTSM, and GRU highlights the superiority of GRU in performance, with RNN exhibiting comparable performance and LSTM lagging. These predictions can be used to extend the lifespan of Li-ion batteries and optimize the performance of the device. In addition, efforts such as expanding the data set and optimizing the network structure can be made to increase the accuracy of these predictions. This research presents an exemplary study of predicting Li-ion battery capacity using ANNs and has been successfully conducted using NASA datasets.

LDH nanocrystal@amorphousness core–shell structure derived from LDH → LDO transformation: Synergistically enhanced energy stored for LIBs anode

With the emergence of advanced electronics and appliances, there is a growing demand for high power/energy density and cyclic stability of Li-ion batteries (LIBs), which stimulates the development of high-performance electrode materials. Herein, we proposed and implemented a novel strategy of employing the LDH → LDO intermediate-transformation amorphization to improve the performance of LDH as LIBs anode. In this work, flower-like NiCo-LDH nanoparticles (LDHRT) are synthesized for the first time by co-precipitation using triethanolamine (TEA) as an alkali source and H2O2 as a size-controlling reagent. Then, the unique LDH nanocrystal@amorphousness core–shell structure was obtained by fine-tuning the heat treatment, which significantly improved the electrochemical properties of the material. The layered structure of LDH and the internal defects of the amorphous phase provide abundant transport channels and a larger accommodation space for Li+, improving the rate capability and capacity of LIBs. In addition, the surface amorphous layer and the reduced size of LDHRT nanocrystals effectively alleviate the volume effect, improving the cycling stability of the electrode material. As a result, superior capacity (1821.3 mAh g−1 at 0.1 A g−1), rate capability, and cyclic stability (∼687.7 mAh g−1 at 0.5 A g−1 after 500 cycles, with the average capacity attrition rate of 0.092 %) were achieved. The Li+ diffusion coefficient and capacity retention rate (after 300 cycles) are higher than nearly 3 orders of magnitude and 50.12 % compared to the LDHRT, respectively. This study has opened a simple, safe, and economical new avenue for developing high-performance electrode materials.

Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

Binder-free germanium nanoparticle decorated multi-wall carbon nanotube anodes prepared via two-step electrophoretic deposition for high capacity Li-ion batteries.

Germanium (Ge) has a high theoretical specific capacity (1384 mA h g-1) and fast lithium-ion diffusivity, which makes it an attractive anode material for lithium-ion batteries (LIBs). However, large volume changes during lithiation can lead to poor capacity retention and rate capability. Here, electrophoretic deposition (EPD) is used as a facile strategy to prepare Ge nanoparticle carbon-nanotube (Ge/CNT) electrodes. The Ge and CNT mass ratio in the Ge/CNT nanocomposites can be controlled by varying the deposition time, voltage, and concentration of the Ge NP dispersion in the EPD process. The optimized Ge/CNT nanocomposite exhibited long-term cyclic stability, with a capacity of 819 mA h g-1 after 1000 cycles at C/5 and a reversible capacity of 686 mA h g-1 after 350 cycles (with a minuscule capacity loss of 0.07% per cycle) at 1C. The Ge/CNT nanocomposite electrodes delivered dramatically improved cycling stability compared to control Ge nanoparticles. This can be attributed to the synergistic effects of implanting Ge into a 3D interconnected CNT network which acts as a buffer layer to accommodate the volume expansion of Ge NPs during lithiation/delithiation, limiting cracking and/or crumbling, to retain the integrity of the Ge/CNT nanocomposite electrodes.

Structural properties and electrochemical performances of mesoporous carbon towards enhanced lithium-ion storage

This study analyzes the structural characteristics of mesoporous carbon (CMK-3) and how they affect the specific capacity of Li-ion batteries (LIBs). To achieve this, CMK-3 is synthesized with and without the addition of oxalic acid as a catalyst to polymerize furfuryl alcohol (carbon precursor) in the template-assisted synthesis process using mesoporous silica. CMK-3 (without oxalic acid) and CMK-3_O (with oxalic acid) exhibit rod-like particles with cylindrical pores, showing high specific surface area (SSA) of 998 and 1067 ​m2 ​g−1 and oxygen-to-carbon ratios of 0.23 and 0.43, respectively. At 178 ​mA ​g−1, the electrodes demonstrate specific capacities of 993 ​mA ​h ​g−1 (CMK-3) and 520 ​mA ​h ​g−1 (CMK-3_O). According to the cyclic voltammetry technique, the non-diffusion-controlled process (approximately 55 ​%) significantly contributes to the total charge storage in CMK-3. Although electrodes (CMK-3 and CMK-3_O) have similar SSA, this study highlights the significance of the disordered and defective structure in achieving a higher specific capacity from the same material without any changes in morphology, particle size, or synthesis route. Consequently, this work emphasizes the necessity of considering the structural properties of porous carbon electrodes when developing high-performance batteries for electric vehicles, where specific capacity and cyclic stability are crucial.

Atomistic-Scale Simulations Toward the Understanding of the Conversion–Alloying Mechanism in Li-Ion Battery Anodes

The increasing demands for energy storage call for improved capacity and cycling stability of modern Li-ion batteries (LIBs). However, the application of the state-of-the-art LIB anode material – graphite is limited as graphite underperforms as a high-rate/high-storage-capacity anode. Alternatively, materials capable of alloying with Li-ions have attracted attention as a promising pathway to solve high-rate and high-capacity challenges. Furthermore, when alloying reaction in a material is coupled with irreversible conversion reaction, the materials operating under such mechanism exhibit a high Li-ion storage capacity and long-term stability during electrochemical cycling. However, despite the optimistic performance of these materials, a number of parameters need to be optimized and fundamentally understood.Among the unknowns for the conversion/alloying materials are the atomistic details of the reaction mechanism - understanding the atomistic structure during transformations is a pre-requisite for the future rational optimization of the materials. Furthermore, amorphization, which is prevalent in these materials, severely limits the conventional characterization methods essentially impeding further development and understanding the degradation mechanisms. Hence, to investigate the atomistic structure changes, we proposed a computational methodology for these conversion-alloying materials and their reaction mechanism by taking amorphous substoichiometric silicon nitride as a showcase system [1]. The molecular dynamics (MD) simulations utilized the ReaxFF parameter set which was specifically developed for the investigation. Subsequently, the experimental pair distribution functions (PDF) were used to verify the modeling results.While the reactive molecular dynamics was coupled with the PDF simulation to complement the experimental measurements, the effective and practical approach guided us to the atomistic structure evolution understanding for LIBs in a conversion–alloying process, revealing the bond coordinations around the Si atoms in the active material. Consequently, the analysis opened an in-depth understanding of the cycling stability of substoichiometric a-SiNx anode materials by revealing the atomistic features relevant to the changes this material undergoes during lithiation and delithiation processes in a LIB. Furthermore, the proposed approach allows modeling of the changes at the atomistic scale for the amorphous materials and predicting the experimental PDF at different cycling stages, verifying the modeling outcome. The topic also addresses the applications of the approach for other anode materials.

Electrochemical Scanning Probe Microscopy Evaluation of Li-Ion Battery Cathode Degradation through in Situ Oxygen Evolution Measurement and Interfacial Property Characterization

Degradation processes involving oxygen evolution significantly limit the performance, reversibility, and capacity of high energy density Li-ion battery (LiB) cathodes. In addition, oxygen evolution processes create hazardous conditions for LiB operation. Despite the importance of these processes, characterization of evolved O2 with sufficient spatial and temporal versatility is hampered by the complex setups required to perform electrochemical mass spectrometry, the gold standard for evaluating this process. Recently, we introduced a scanning electrochemical microscopy (SECM) approach that enables in-situ, real-time (i.e., few ms temporal resolution), chemically selective detection of O2.[1] Measurements were accomplished by positioning an SECM probe (i.e., an Au ultramicroelectrode) near operating cathode surfaces for detecting O2 via the oxygen reduction reaction (ORR). The superior signal-to-noise of this method enabled the observation of two peaks corresponding to a steady-state evolution at high voltage and a transient O2 evolution at low voltage on materials such as lithium cobalt oxide (LCO) and NMC electrodes.Here, we expand the capabilities of our technique by incorporating correlated measurements aimed at understanding how oxygen evolution profiles are related to surface electrochemical and mechanical properties. Specifically, we will focus on the use of feedback-mode SECM and the use of reactive mediators to evaluate the changes in the rates of electron transfer measured on materials undergoing O2 and after structural degradation. Furthermore, the use of electrochemical AFM allows us to measure the mechanical properties of these interfaces, in an attempt to tie the structural changes and the evolution of oxidizing species with the formation of cathode-electrolyte interfaces. Altogether, these multimodal SECM and AFM methods elucidate the manifold chemical, structural, and performance changes underpinning LiB cathode operation.[1] Mishra, A.; Sarbapalli, D.; Hossain, M.S.; Gossage, Z.T.; Li, Z.; Urban, A.; Rodríguez-López, J. Highly Sensitive Detection and Mapping of Incipient and Steady-State Oxygen Evolution from Operating Li-Ion Battery Cathodes via Scanning Electrochemical Microscopy. J. Electrochem. Soc., 2022, 169, 086501. Figure 1

In Situ Raman Spectroelectrochemical Investigation of Composite Si Nanoparticle-Based Anode for Li-Ion Batteries during (de)Lithiation Process

The key degradation processes in the composite anode for the Li-ion batteries (LIBs) prepared using Si nanoparticles (SiNPs) with two types of a conductive carbon-based matrix [carbon black (CB) and carbonized polypyrrole (CPPy)] were studied by in situ Raman spectroelectrochemistry (SEC). This combined technique provides non-destructive and real-time monitoring of the chemical and structural changes that occur during battery operation. These processes, such as the crystal lattice changes (expansion/contraction) and possible degradation/amorphization of silicon, the solid electrolyte interphase (SEI) layer formation on the electrode surface during the charge/discharge reactions, and the stability of the carbon-based matrix, significantly affect the performance of Si-based LIBs in many ways. Especially, the large silicon volume expansion (more than 300%) and associated stress cause mechanical instability resulting in rapid capacity fading. To overcome this challenge, various strategies have been proposed, including the use of CPPy as a conductive matrix to prevent large volumetric changes and also provide a pathway for electron transfer.This study investigates the initial degradation mechanisms such as the formation of the SEI layer, as well as possible structural changes caused by the volume expansion of SiNPs. The shift of the first-order Raman peak of Si at 521 cm-1 which is assigned to crystalline silicon is related to the stress evolution in nanoparticles caused by the (de)lithiation-induced stress (tensile-to-compressive transition) in SiNPs and the native oxide on their surface. Additionally, the detailed in situ Raman measurement of the first lithiation cycle allowed us to detect the decomposition of the electrolyte (LiPF6 in EC/DMC) close to the Si surface which is associated with SEI layer formation. A decrease in the intensity of the Raman vibrational modes (700–1050 cm−1) of EC/DMC corresponding to the decomposition of the electrolyte was observed at reduction potentials of 0.8 V and 0.3 V vs. Li/Li+ corresponding to the same potentials determined by cyclic voltammetry. The Si-based anode containing CPPy as active carbon showed better electrochemical properties (higher charge/discharge capacity and cycling stability) in Li-ion batteries than Si/CB despite the higher conductivity of the latter. The correlation between the chemical structure of the active carbon and the electrochemical properties of the resulting Si-based anodes will be discussed.Acknowledgment: This work was supported by the Czech National Foundation, Project No. 21–09830S.

Operando NMR Characterization of Cycled and Calendar Aged Si Anodes

Replacing graphite anodes with Si anodes can greatly increase the capacity of current Li-ion batteries. Detailed characterization of Si lithiation reactions, solid electrolyte interphase (SEI) formation, and reversibility are therefore active areas of research. Solid-state nuclear magnetic resonance (NMR) spectroscopy is useful for characterizing different local environments within Si anodes as well as differentiating surface and bulk environments. Furthermore, non-invasive and non-destructive NMR methods can reveal metastable LixSi phases or SEI species forming on (dis)charge that may be too reactive to detect via ex situ methods. Here, we use an in situ and in operando NMR methodology to characterize full pouch cells comprising of Si anodes and NMC cathodes. We can observe changes in the relative amounts of Li in different environments within the same Si anode before/during/after charge & discharge and with various amounts of cycling or calendar aging using in situ 7Li NMR. The pouch cells used in our in situ NMR study are comparable to pouch cells made using mid- to large-scale fabrication methods as opposed to laboratory scale; in other words, challenges of cell-to-cell variability are minimized. We hypothesize “trapped” lithiated phases formed in the Si anode no longer contribute to electrochemical cycling and thus limit capacity in subsequent cycles/aging. We also characterized the SEI/surface species in situ with aging via 13C NMR measurements, identifying reactions of lithium silicides with electrolyte solvents and their subsequent decomposition.

Aligned NMC811 electrodes by functionalized conductive graphite flakes for fast-charging Li-ion battery

In recent years, the demand for high capacity, long cycle life, fast charging, and increased safety lithium-ion batteries has become increasingly urgent, especially in the electric vehicle industry. The specific capacity of Li-ion batteries is growing steadily under the great efforts of researchers. However, fast charging, as one of the key technologies of lithium-ion batteries, is still not fully and efficiently resolved. Electric vehicles now have a sufficient driving range, but they take too long to refill compared to traditional vehicles. Here, we aligned polycrystalline NMC 811 microparticles by functionalized graphitic flakes (conductive graphite KS6) under a rotating magnetic field by 2 steps in the cathode fabrication process and constructed an effective and fast conducting three-dimensional conductive network for both electron and ion transport in the cathode system of a high-rate performance Li-ion battery. We successfully regulate and control the tortuosity and porosity of the NMC811 cathode on the electrode level. The new electrode structure has higher ionic conductivity and higher specific capacity. The specific capacity of the electrode with the new electrode structure is about four times and five times the reference electrode at 5 C and at 10 C, respectively. And, the half-cell of the electrode can be fed with 70 % SOC in a 6-min charge at 5 C.

A Two-State-Based Hybrid Model for Degradation and Capacity Prediction of Lithium-Ion Batteries with Capacity Recovery

The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the phenomenon of capacity recovery during battery degradation and propose a hybrid lithium-ion battery capacity prediction framework based on two states. First, to improve the density of capacity-related information, the simultaneous Markov blanket discovery algorithm (STMB) is used to screen the causal features of capacity from the initial feature set. Then, the life-long cycle sequence of batteries is partitioned into global degradation regions and recovery regions, as part of the proposed prediction framework. The prediction branch for the global degradation region is implemented through a long short-term memory network (LSTM) and the other prediction branch for the recovery region is implemented through Gaussian process regression (GPR). A support vector machine (SVM) model is applied to identify recovery points to switch the branch of the prediction framework. The prediction results are integrated to obtain the final prediction results. Experimental studies based on NASA’s lithium battery aging data highlight the trustworthy capacity prediction ability of the proposed method considering the capacity recovery phenomenon. In contrast to the comparative methods, the mean absolute error and the root mean square error are reduced by up to 0.0013 Ah and 0.0043 Ah, which confirms the validity of the proposed method.

SU8 polymer derived high capacity and performance anode material for secondary and flexible Li-ion batteries: Experimental and first principle study

This study provides a novel and sustainable protocol for the synthesis of SU8 polymer-based pattern, un-pattern carbon nanofibers (CNF) using an electrospinning process followed by a photolithography technique. Scanning electron microscopy (SEM) analysis of pattern, un-pattern CNFs shows that the average diameter of CNF is within the range of 60–140 nm. Transmission electron microscopy (TEM) studies disclose the average size of CNF exists within the range of 40–150 nm. The first-principle calculations predict that the gravimetric energy and power densities of Li/CNF half cells are 26.96 kW h g−1 and 26.96 kW g−1, respectively. The corresponding theoretical volumetric energy and power densities are 0.07 W h cm−3 and 0.07 W cm−3, respectively. Electrochemical analysis demonstrates the initial discharge capacities of pattern, un-pattern Li/CNF half cells at 100 mA g−1 are 3450 and 2429.8 mA h g−1, respectively. The gravimetric energy density of pattern, un-pattern Li/CNF half cells are within the range of 1800–1366.76 W h kg−1, and subsequent power densities are in the span of 519.63–434.22 kW g−1. The theoretical gravimetric energy and power densities of CNF/LiMn0.33Ni0.33Co0.33 (LMNC) full cells are 2.9 kWh kg−1 and ∼ 2.9 kW kg−1, respectively. Their theoretical volumetric energy and power densities are 1.96 W h cm−3 and 1.96 W cm−3, respectively. Electrochemical analysis discloses that the gravimetric energy and power densities of pattern CNF/LMNC full cells are in the range of 43.18–42.4 kW h kg−1 and 51.82–50.90 kW kg−1, respectively. The gravimetric energy and power densities of un-pattern CNF/LMNC full cells are 42.07–41.59 W h kg−1 and 50.48–49.92 kW kg−1, respectively.