Capacity For Lithium-ion Batteries Research Articles

Unraveling capacity recovery behavior of 78 Ah pouch cells after long-term storage for EVs: Passive anode and calendar-aged SEI effects

The unavoidable long-term storage after production can result in capacity and power fading in commercial lithium-ion batteries. Remarkably, the decreased capacity is partially and gradually recovered when the stored cells are cycled again, known as capacity recovery. However, it is challenging to analyze the capacity recovery phenomenon independently from the capacity decay during re-cycling. Moreover, the most well-known explanation for capacity recovery, the passive anode effect (PAE) is only focus on the lithium inventory inside the overhang anode site. To address these issues, the large-capacity LiNi0.4Co0.3Mn0.3O2/graphite pouch cells stored for 4 years is used. Typically, throughout the re-cycling, the capacity is gradually recovered up to 120 cycles, and the fully recovered capacity was maintained up to 500 cycles, indicating that additional capacity decay by re-cycling was almost suppressed. Also, we clearly reveal the impact of breakdown and thinning of the calendar-aged solid electrolyte interphase (SEI) on the whole anode site influences both capacity and power recovery, not just limited to the PAE. Furthermore, by combining both the PAE and breakdown of the calendar-aged SEI into a life prediction model, the capacity recovery behavior for up to 1000 cycles under various operating conditions can be predicted.

A Lithium-Ion Battery Remaining Useful Life Prediction Model Based on CEEMDAN Data Preprocessing and HSSA-LSTM-TCN

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a predictive model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) data preprocessing and IHSSA-LSTM-TCN. Firstly, CEEMDAN is used to decompose lithium-ion battery capacity data into high-frequency and low-frequency components. Subsequently, for the high-frequency component, a Temporal Convolutional Network (TCN) prediction model is employed. For the low-frequency component, an Improved Sparrow Search Algorithm (IHSSA) is utilized, which incorporates iterative chaotic mapping and a variable spiral coefficient to optimize the hyperparameters of Long Short-Term Memory (LSTM). The IHSSA-LSTM prediction model is obtained and used for prediction. Finally, the predicted values of the sub-models are combined to obtain the final RUL result. The proposed model is validated using the publicly available NASA dataset and CALCE dataset. The results demonstrate that this model outperforms other models, indicating good predictive performance and robustness.

Structural Reversibility of Nanoscaled Sn Anodes.

Alloying-type anode materials provide high capacity for lithium-ion batteries; however, they suffer pulverization problems resulting from the volume change during cycling. Realizing the cycling reversibility of these anodes is therefore critical for sustaining their electrochemical performance. Here, we investigate the structural reversibility of Sn NPs during cycling at atomic-level resolution utilizing in situ high-resolution TEM. We observed a surprisingly near-perfect structural reversibility after a complete cycle. A three-step phase transition happens during lithiation, accompanied by the generation of a significant number of defects, grain boundaries, and up to 202% volume expansion. In subsequent delithiation, the volume, morphology, and crystallinity of the Sn NPs were restored to their initial state. Theoretical calculations show that compressive stress drives the removal of vacancies generated within the NPs during delithiation, therefore maintaining their intact morphology. This work demonstrates that removing vacancies during cycling can efficiently improve the structural reversibility of high-capacity anode materials.

Dilithium pyridine-2, 5-dicarboxylate as potential anode material for Li-ion battery

Amongst various small molecules with different functional groups, conjugated carbonyls are the most promising materials for lithium storage at low potential. This work reports lithium storage in dilithium pyridine-2, 5-dicarboxylate small molecule. We show here that dilithium pyridine-2, 5-dicarboxylate reversibly stores 2 mol of Li-ion per molecule, giving a reversible capacity of 307 mAh/g at an average voltage of 1.0 V. The capacity retention after 500 cycles at 1C rate is 69 %. This material shows impressive rate performance with a capacity retention of 140 mAh/g at 5C rate. The inclusion of electronegative nitrogen in the benzene ring helps in improving the storage performance compared to lithium terephthalate. Further ex-situ studies demonstrate possible mechanism for reversible lithium storage. Our findings showcase that the deliberately engineered organic electrode material facilitates the development of high capacity lithium-ion battery.

Numerical thermal control design for applicability to a large-scale high-capacity lithium-ion energy storage system subjected to forced cooling

Overheating and non-uniform temperature distributions within the energy storage system (ESS) often reduce the electric capacity and cycle lifespan of lithium-ion batteries. In this numerical work, the thermal design inside the battery cabinet is explored. The battery cabinet has seven-level configurations with the suction fans located on the top of the ESS to efficiently realize heat dissipation. First, the numerical modeling is validated by the available experimental data, and the discrepancy in the maximum temperature increase between the experimental and computational results is found to be 3.2% or 0.64 K. Then, the optimum airflow rates of the fans are determined, where the single-cell model is also compared with the model of seven-level modules (i.e., 1/3ESS cabinet). Finally, the airflow channel widths, air gaps between the battery modules, as well as the air gaps between the 7th level module and the fans are examined. It is indicated for the precise model of 1/3ESS cabinet that: i) The maximum temperature rise of 4.63 K, and the maximum temperature variation of 2.14 K from cell to cell & the temperature uniformity of 2.82 K from module to module are all controlled with the requirements of operating temperature < 313.15 K, and temperature differences < 5.0 K, respectively; ii) The airflow channel width of 3.0 mm and the air gap of 10 mm between two adjacent modules & air gap of 20 mm between 7th level module and six parallel-fans are designed, and the ESS cabinet can increase the batteries of one level caused by the saved inside space.

Double-Shelled Fe-Fe3C Nanoparticles Embedded on a Porous Carbon Framework for Superior Lithium-Ion Half/Full Batteries.

Cost-effective and environmentally friendly Fe-based active materials offer exceptionally high energy capacity in lithium-ion batteries (LIBs) due to their multiple electron redox reactions. However, challenges, such as morphology degradation during cycling, cell pulverization, and electrochemical stability, have hindered their widespread use. Herein, we demonstrated a simple salt-assisted freeze-drying method to design a double-shelled Fe/Fe3C core tightly anchored on a porous carbon framework (FEC). The shell consists of a thin Fe3O4 layer (≈2 nm) and a carbon layer (≈10 nm) on the outermost part. Benefiting from the complex nanostructuring (porous carbon support, core-shell nanoparticles, and Fe3C incorporation), the FEC anode delivered a high discharge capacity of 947 mAh g-1 at 50 mA g-1 and a fast-rate capability of 305 mAh g-1 at 10 A g-1. Notably, the FEC cell still showed 86% reversible capacity retention (794 mAh g-1 at 50 mA g-1) at a high cycling temperature of 80 °C, indicating superior structural integrity during cycling at extreme temperatures. Furthermore, we conducted a simple solid-state fluorination technique using the as-prepared FEC sample and excess NH4F to prepare iron fluoride-carbon composites (FeF2/C) as the positive electrode. The full cell configuration, consisting of the FEC anode and FeF2/C cathode, reached a remarkable capacity of 200 mAh g-1 at a 20 mA g-1 rate or an energy density of approximately 530 Wh kg-1. Thus, the straightforward and simple experimental design holds great potential as a revolutionary Fe-based cathodic-anodic pair candidate for high-energy LIBs.

Lithium-Ion Battery Capacity Prediction Method Based on Improved Extreme Learning Machine

Abstract Currently, research and applications in the field of capacity prediction mainly focus on the use and recycling of batteries, encompassing topics such as SOH estimation, RUL prediction, and echelon use. However, there is scant research and application based on capacity prediction in the battery manufacturing process. Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method consumes considerable time and energy. To address the above issues, this study establishes an improved extreme learning machine (ELM) model for predicting battery capacity in the manufacturing process, which can save approximately 45% of energy and time in the grading process. The study involves the extraction of features from the battery charge–discharge curve that can reflect battery capacity performance and subsequent calculation of the grey correlation between these features and capacity. The feature set comprises features with a high correlation with capacity, which are used as inputs for the ELM model. Kernel functions are used to adjust the ELM model, and Bayesian optimization methods are employed to automatically optimize the hyperparameters to improve the capacity prediction performance of the model. The study uses lithium-ion battery data from an actual manufacturing process to test the predictive effect of the model. The mean absolute percentage error of the capacity prediction results is less than 0.2%, and the root-mean-square error is less than 0.3 Ah.

Interfacial modulation of hollow ZnS-SnS2 microboxs by Ti3C2Tx MXene to construct three-dimensional hybrid anodes for lithium-ion batteries with ultra high stability at low temperature

Metal sulfides exhibit obvious volume expansion due to the inherent poor conductivity and large temperature fluctuations, leading to reduced storage capacity. Herein, an electrostatic self-assembly strategy was proposed to fabricate a three-dimensional (3D) polyaniline (PANI) encapsulated hollow ZnS-SnS2 (H-ZSS) heterojunction confined on Ti3C2Tx MXene nanosheets (H-ZnS-SnS2@MXene@PANI, denoted as H-ZSSMP), which exhibits remarkable reversible capacity and cyclic stability (520.3 mAh/g at 2 A/g after 1000 cycles) at room temperature. Additionally, specific capacity can stabilized at 362.5 mAh/g for 250 cycles at −20 °C. A full cell with the configuration of H-ZSSMP//lithium iron phosphate (LiFePO4) can retain a satisfactory reversible capacity of 424.7 mAh/g after 100 cycles at 0.1 C. Theory calculations confirm heterogeneous interface can accelerate the transfer of ions through the interfacial regulation effect of MXene on H-ZSS. Our work provides a simple strategy to improve the capacity and stability of lithium-ion batteries (LIBs), as well as the new applications of MXene and bimetallic sulfides as anode materials, which will facilitate the development of MXene based composites for energy storage.

One-pot synthesis of TiO2-decorated SnSe/ZnS as a novel anode material with promoted storage capacity and cyclability in lithium-ion batteries

One-pot synthesis of TiO2-decorated SnSe/ZnS as a novel anode material with promoted storage capacity and cyclability in lithium-ion batteries

Polyoxovanadate Li7[V15O36(CO3)] and its derivative γ-LiV2O5 as superior performance cathode materials for aqueous zinc-ion batteries

Recently, many reports have been published about polyoxovanadates (POVs) with multiple electrons redox activity for energy storage. However, they are easily transformed into solid-state oxides after calcination. We noted that although dehydrated Li7[V15O36(CO3)] (Li7V15) exhibited a high capacity for lithium-ion batteries (LIBs), it was reported that the electrochemical behavior of the dehydrated sample same as γ-LiV2O5 (γ-LVO) which prepared by annealing the cluster and undehydrated Li7V15 was not redox-active. Interestingly, we were surprised to find that undehydrated Li7V15 and its derivative γ-LVO showed better electrochemical performance than LIBs when first proposed as the cathode to store Zn2+. Both Li7V15 and γ-LVO electrodes provided excellent rate performance and satisfied cyclability (135.0 and 214.1 mAh/g at 3 A/g after 1000 cycles, respectively). The fast kinetics of Li7V15 and γ-LVO were verified by pseudocapacitive analysis, GITT and EIS tests. Moreover, the Zn2+ storage mechanism and redox behavior of Li7V15 and γ-LVO were examined by ex-situ measurements. First-principles calculations revealed that the Zn2+ was chemisorbed on the clusters and physisorbed between the clusters. This work enriches the POVs cathode chemistry in aqueous energy storage.

In-situ surface growth strategy to synthesize MXene@graphdiyne heterostructure for achieving high capacity and desirable stability in lithium-ion batteries

Two-dimensional (2D) heterostructured electrodes, combining graphdiyne (GDY) and MXenes, have exhibited substantial promise in augmenting the mobility of both ionic and electron movement. However, the widespread advancement and industrial utilization have been impeded by intricate manufacturing procedures and insufficient stability. This study introduces a more streamlined, in-situ growth approach for preparing MXene@GDY electrodes, presenting a novel heterostructure. The method simplifies the manufacturing process while enhancing the specific capacity of the electrode and the more stable cycle life of lithium-ion batteries (LIBs). The resulting electrode exhibited an impressive initial specific capacity of 464.4 mA h g−1, maintaining a high capacity of 492.9 mA h g−1 after 100 cycles at 100 mA current density. More importantly, after 1200 cycles, MXene@GDY showed a capacity of 340.7 mA h g−1, significantly outperforming pure MXene, which only reached 122.3 mA h g−1 at a current density of 1.0 A g−1. The proposed in-situ construction of heterojunction on the MXene surface has demonstrated immense potential for designing high-performance electrode materials which are applicable to LIBs.

A novel variable activation function-long short-term memory neural network for high-precision lithium-ion battery capacity estimation

A novel variable activation function-long short-term memory neural network for high-precision lithium-ion battery capacity estimation

Modeling the propagation of internal thermal runaway in lithium-ion battery

The trend toward high capacity and huge size in lithium-ion batteries has made it necessary to investigate the internal thermal characteristics. In this study, a thermal runaway model was developed to describe lithium-ion batteries' internal thermal characteristics. Moreover, triggering energy was proposed as a critical feature for evaluating and characterizing the thermal runaway under diverse thermal abuse situations, with large differences among characteristic temperatures. Finally, the effects of battery configuration on thermal runaway behaviors were investigated. The modeling results showed that internal temperature distribution can be divided into four characteristic stages with two jelly rolls, and the application of more numerous and thinner cells inside a battery can accelerate the propagation of thermal runaway. The experimental results showed that the ratio of triggering energy of self-heat onset to total self-heat generation remained consistent in an adiabatic environment. The mean value of the ratio was 24.5%, indicating that lithium iron phosphate batteries obtain most of the energy (generally 80%) from internal exothermic reactions during adiabatic thermal abuse. The triggering energy of thermal runaway remained constant when various heating powers were applied to one of the batteries' laterals (about 20.8% of theoretical energy contained inside lithium iron phosphate batteries). Triggering energy can provide new insights into the modeling of thermal runaway mechanisms and propagation.

Battery health prediction using two-dimensional multi-channel ensemble models

The accurate assessment of battery health is crucial for maximizing the performance and lifespan of lithium-ion batteries. In this paper, an ensemble model based on a two-dimensional multi-channel convolutional neural network is proposed to predict the maximum usable capacity of lithium-ion batteries. First, based on the charge–discharge process, the characteristic-derived lines of the capacity–voltage (Q–V) curve are extracted. Next, the Gramian angular summation field (GASF) is employed to convert one-dimensional time series data into image matrices, with the aim of preserving temporal characteristics. Finally, the paper discusses the impact of 2D matrix size on prediction results and introduces the use of a 2D multi-channel ensemble model for the unsupervised recognition and extraction of battery decline information from images, marking the first time this approach has been utilized. The experimental results demonstrate that the proposed method effectively captures battery degradation patterns from multiple perspectives. The average mean absolute percentage error of 1.95% and root mean square error of 1.41% on the verified batteries indicate the robustness and generalizability of the approach.

Influence of cathode materials on thermal characteristics of lithium-ion batteries.

In this work, the thermal stability of four types of 18,650 lithium-ion batteries with LiCoO2 (LCO), LiFePO4 (LFP), LiNi0.8Co0.1Mn0.1O2 (NCM811) and LiNi0.8Co0.15Al0.05O2 (NCA) materials as cathodes are experimentally investigated by the accelerating rate calorimeter (ARC) and the isothermal battery testing calorimeter (iso-BTC) under adiabatic and isothermal conditions, respectively. The thermal runaway danger level of these batteries can be ranked as LCO > NCA > NCM811 >> LFP by judging from the values of Tmax and HRmax, nominal. The higher the nickel and cobalt content, the higher the lithium-ion battery capacity, but the worse the thermal stability. The Qtotal of NCA is the largest in the complete standard charge and discharge process, due to that the capacity of NCA is significantly higher than that of the other three batteries, resulting in remarkable increase in Qirre proportioned to the square of the current. When the ambient temperature rises, the energy release decreases owing to the decrease in the internal resistance of the battery. These studies are expected to have important implications for the subsequent safe design of commercial lithium-ion batteries with different cathode materials.

Stress-dependent capacity fade behavior and mechanism of lithium-ion batteries

Understanding the behavior and mechanism of capacity fade in lithium-ion batteries can assist in promoting their wider commercial and industrial applications. We develop an Electrochemical-Thermal-Mechanical coupled Capacity Fade model (ETMCF) for nickel-manganese-cobalt (NMC111)/graphite (C) lithium-ion batteries. The main fade behaviors include SEI formation, lithium plating/stripping, SEI re-formation, and loss of active material. ETMCF model focuses on the influence of stress on capacity fade behaviors, improves the accuracy of predicting battery capacity fade at high charge/discharge rates (C-rates) and a wide temperature domain, and confirms that the stress effect in lithium-ion battery capacity fade is essential and cannot be ignored. We systematically examine the impact of C-rate, operating temperature, and coupling effects on the capacity fade mechanism and discover markedly distinct dominant fade behaviors under various C-rates and temperatures. Moreover, we also construct a phase diagram of the capacity loss ratio and elucidate the dependency of the C-tare, temperature, and capacity loss relationship. This work provides new insights into fast charging strategy and temperature regulation in battery management systems and offers a comprehensive analytical framework for predicting and evaluating battery capacity fade.

Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Battery energy storage systems (BESS) play a pivotal role in energy management, and the precise estimation of battery capacity is crucial for optimizing their performance and ensuring reliable power supply. Deep learning methodologies applied to battery capacity estimation have exhibited exemplary performance. However, deep learning methods necessitate supervised training with a significant volume of labeled data, presenting challenges for data collection in industrial scenarios. Moreover, a diverse range of battery types in industrial settings makes it difficult to develop capacity estimation models for different types of batteries from scratch. To address these issues, a semi-supervised adversarial deep learning (SADL) method is proposed for lithium-ion battery capacity estimation. Initially, a subset of labeled lithium-ion battery data, coupled with a subset of unlabeled data, is collected. Voltage and current data are then transformed into capacity increment features. Subsequently, an adversarial training strategy is employed, subjecting labeled and unlabeled data to adversarial training to enhance the performance of SADL. Finally, the effectiveness of the SADL method in estimating the capacity of other lithium-ion batteries is analysed. Experimental results demonstrate that the SADL method accurately estimates the capacity of various battery types, showcasing an RMSE error of approximately 2%, surpassing the performance of other methods. The proposed SADL method emerges as a promising solution for the precise estimation of lithium-ion battery capacity in BESS.

Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits

As the adoption of electric vehicles (EVs) continues to rise due to increasing environmental concerns and policy support, the accurate estimation of battery capacity becomes crucial for efficient vehicle management and prolonging battery life. This study presents a framework for capacity estimation under real-world electric vehicle charging scenarios, reflecting a variety of driving habits. A novel CNN-LSTM neural network architecture featuring a probabilistic regression layer (CLPNN) is developed, utilizing short time interval data to provide accurate, reliable capacity and uncertainty estimates for diverse charging situations. The adaptability of the proposed method is evaluated by intercepting real-vehicle statistics to simulate charging events. We introduce two result fusion strategies for refining capacity estimations: the Trimmed Mean Strategy and the Uncertainty Screening Strategy. Their performances are meticulously compared to establish the most effective approach. Moreover, probabilistic layer transfer learning is employed to address the challenge of insufficient labeled data in the target domain, thereby improving the transfer and generalization performance of the model. The findings of this study underscore the potential of the proposed methods in accurately evaluating battery capacity and dealing with uncertainties in real-world EV operations.

Dual modification of phosphate toward improving electrochemical performance of LiNiO2 cathode materials

Lithium nickel oxide (LiNiO2) cathode materials are featured with high capacity and low cost for rechargeable lithium-ion batteries but suffer from severe structure and interface instability. Bulk doping together with surface coating has been proven to be an efficient approach to improve the inner structure and interfacial stability of the LiNiO2 cathode material. Nevertheless, the role of anion doping seems to be quite different from that of cation doping, and a deep insight will be desirable for the structure design of the LiNiO2 cathode material. In this paper, PO43−-doped and Li3PO4-coating of dual modification of LiNiO2 are achieved via a facile approach. It is demonstrated that the PO43− anions are doped into the tetrahedron vacant sites of the crystal structure, alleviating the phase transition and improving the reversibility of crystal structure. Besides, the Li3PO4 coating layer ameliorates the interface stability to restrain the side reactions. Therefore, the dual modification enhances overall structural stability of the material to provide excellent performance. Moreover, the consumption of the Li residues by the formation of Li3PO4 coating layer, and the enlarged interlayer spacing of the crystal structure by PO43− doping can facilitate the Li+ ions diffusion, resulting in a superior rate capability.

Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction

An autoencoder based neural network architecture, CD-Net, is proposed to predict Lithium-ion battery capacity degradation as a function of operation time as part of a battery management system. CD-Net's generalization performance on various LIB cell chemistry is tested. The incorporation of cell chemistry in CD-Net leads to an improvement in the overall battery capacity prediction accuracy of >2 % for LiNiMnCoO2 cells, >5 % for LiNiCoAlO2 cells, and >12 % for LiFePO4 cells when compared to the similar ML models that do not incorporate cell chemistry information. A comparison of onboard battery health prediction using CD-Net against support vector regression, Bayesian regression, and Gaussian process regression-based approaches shows that CD-Net has higher computational efficiency with <2 % of relative remaining useful life (RUL) prediction error in a no-cell chemistry information setting. In summary, our work presents a chemistry-independent neural network model tailored specifically for onboard BMS applications, showcasing notable predictive capabilities in the context of Lithium-ion battery health assessment.