Lithium-ion Batteries Research Articles

Cu@MOF based composite materials: High performance anode electrodes for lithium-ion and sodium-ion batteries

In order to satisfy the increasing demand for energy, it is essential to improve the efficiency of lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) batteries. Metal selenides (MSes) have a high theoretical specific capacity, can be designed in a variety of ways, conduct electricity well, can change morphology easily, and have a multi-electron reaction mechanism. These characteristics make them very competitive as anode materials for LIBs and SIBs. Herein, we synthesise Cu@MOF and Cu@NH2-MOF as the precursor to prepare Cu1.95Se embedded into porous carbon matrix (Cu1.95Se@PC) and porous-N-doped carbon matrix (Cu1.95Se@NPC) via pyrolysis process of Cu@MOF and Cu@NH2-MOF, respectively. Cu1.95Se@PC and Cu1.95Se@NPC electrodes for half-cell LIBs and SIBs exhibit a high reversible capacity of 313.1, 480.9 (for LIBs), 216.3 and 303.8 (for SIBs) mAhg−1after 1000 cycles at 2 A g−1, respectively. We assess the electrochemical performance of the Cu1.95Se@PC and Cu1.95Se@NPC anodes by integrating them with commercially available LiFePO4 (LFP) into full-cell LIBs. The LFP//Cu1.95Se@PC and LFP//Cu1.95Se@NPC full-cells have discharge capacities of approximately 330 and 293 mAh g−1 at 0.3 A g−1 at the initial cycle. In order to explore the sodium storage mechanism of the Cu1.95Se composites, we conducted an in situ XRD test during the first charge/discharge cycle, considering their favourable cycling and rate performance. Our work provides a promising anode electrode material for both half-cell LIBs and SIBs with high volume utilization and superior electrochemical performances.

Mechanical failure modeling for cathode sheets

The mechanical integrity of the cathode is critical for the operation and safety of lithium-ion batteries (LIBs). This study presents a comprehensive exploration of the progression and underlying mechanisms of failure within LIB cathodes, employing a combination of experimental and numerical methodologies. First, shear and 180° peel tests are designed and conducted to calibrate the parameter values governing the interface between the aluminium (Al) foil and the active layer, leveraging cohesive zone modeling (CZM). Furthermore, based on finite element modeling (FEM) and extended finite element modeling (XFEM), failure criteria and damage models are introduced to simulate the damage behavior of both Al foils and active layer. Notably, XFEM computes the initiation and expansion of the crack in cathode. The numerical simulation demonstrates a good correlation with the experimental observations, effectively delineating the failure process of the cathode. Moreover, a parametric analysis is conducted to study the influence of the active layer and interface on cathode failure. It reveals that as the failure strain of the active layer increases, the failure strain of the cathode rises, ultimately converging with the failure strain of an isolated Al foil. However, the failure strain of cathode decreases and then increases with increasing interface peel strength. At a peel strength of 304 N/m, the stress distribution within the active layer and the interface is more uniform compared to the lower peel strength scenarios. Besides, the interface stress transfer behavior leads to the failure of the Al foil in early. The results enhance our understanding of the failure process and mechanisms in LIB electrodes and thus provide guidance for the design and fabrication of cathode.

Mass balance of nickel manganese cobalt cathode battery recycle process

Batteries made from lithium, nickel, manganese, and cobalt are widely used, especially in the electrical industry, because they have high specific capacity, high safety, and low production costs. According to the International Energy Agency (IEA), the consumption of batteries used for electric vehicles will increase from 8 million in 2019 to 50 million in 2025 and to 140 million in 2030. As a result, the waste produced is also increasing. This type of lithium ion battery (LIB) which contains heavy metal elements such as nickel, manganese and cobalt can be recycled. This research aims to calculate the mass balance of the recycling process for nickel manganese cobalt (NMC) battery cathodes. The processing process begins with mixing, leaching, filtration, drying the results of the filtration process, molarity adjustment, Flame Assisted Spray Pyrolysis, and calcination. Based on the results of mass balance calculations for the NMC recycle battery cathode, the amount obtained was 43.427 kg/batch from 100 kg of cathode waste raw material. Apart from that, data was obtained on the metals that were successfully recycled, namely NiO, MnO, CoO, Fe2O3, MgO, Al2O3, Cr2O3, and Li2O. The research results provide information that NMC battery waste can be an opportunity for the NMC metal supply chain and can reduce environmental pollution.

Analysis and prediction of battery temperature in thermal management system coupled SiC foam-composite phase change material and air

Lithium-ion batteries crucially rely on an effective battery thermal management system (BTMS) to sustain their temperatures within an optimal range, thereby maximizing operational efficiency. Incorporating bio-based composite phase change material (CPCM) into BTMS enhances efficiency and sustainability. This study commences by blending lauric acid and myristic acid in a 7: 3 mass ratio to synthesize a bio-based CPCM, following which the thermophysical properties of this CPCM are tested. Subsequently, the CPCM is integrated into a SiC foam and coupled with air to develop a novel BTMS. Then the effects of air velocity and initial temperature on the temperature of the battery pack are analyzed. Finally, the RIME-Convolutional Neural Network (CNN)- Self-Attention (SA)- Gated Recurrent Unit (GRU) model is established to predict the temperature of the battery in this BTMS. The results indicate that the latent heat of the CPCM is 97.98 J/g, melting at 35 °C upon heating. The incorporation of SiC foam-CPCM effectively reduces battery temperatures. When the air velocity is set at 3 m/s, the battery temperature remains below 40 °C during a discharge rate of 2C. Notably, the CPCM melts at higher initial temperatures, effectively mitigating the temperature rise in the battery pack. The RIME-CNN-SA-GRU model exhibits remarkable accuracy, with a maximum prediction error of 0.56 °C and a RMSE of 0.23, precisely capturing the trend of a sharp temperature rise at the end of discharge. This study presents an efficient solution for lithium-ion battery thermal management and temperature prediction.

Decoupling the influence of impact energy and velocity on dynamic failure of cylindrical lithium-ion batteries

Ensuring battery safety in electric vehicles during crashes is crucial due to the complexities of battery failure under dynamic loading. This study presents a dynamic test apparatus that isolates the effects of impact energy and velocity. Experiments show that impact energy primarily drives battery failure, with impact velocity also influencing outcomes. Notably, the battery demonstrates mitigated electrical failure within a specific impact energy range, which is lower than the threshold for failure characterized by a sudden voltage drop due to major fractures. The self-discharging rate of the impaired batteries within this range exhibits randomness, likely caused by complex electrode contact conditions following minor separator fractures and subsequent deformation recovery. Interestingly, under similar impact energy, electro-mechanical failure exhibits a nonlinear “severe-mild-severe” pattern as velocity increases, differing from the typical strain-rate effect observed in components like separators. X-ray computerized tomography and dynamic material behavior analysis reveal this anomaly as a competition between strain-rate hardening of materials and inertia effect of electrolyte flow and wound structure. The findings highlight that different factors dominate battery failure under varying impact velocities. This research enhances understanding of the energy- and velocity-dependent responses of lithium-ion batteries, aiding in optimizing battery designs for improved safety during collisions.

Cross-domain machine transfer learning for capacity aging trajectory prediction of lithium-ion batteries

Machine transfer learning-based methods have emerged as a promising solution to capacity aging trajectory prediction for different types of batteries. However, the methods lack transferring interpretability, coupled with obvious variations in data across diverse batteries, presenting challenges to early lifespan prediction. A voltage-capacity (V-Q) curve reconstruction-based transfer learning method across source and target domains is proposed to predict the aging trajectories for various batteries. Initially, a generalized mathematical model is established for the V-Q curves of three types of batteries after a two-step transformation. Based on the model, a partial loss function for parameter optimization within deep learning is constructed. Subsequently, a Global Attention-sequence-to-sequence model is developed to reconstruct the V-Q curves in the target domain. The reconstructed data distribution in the target domain exhibits high similarity to that in the source domain, significantly reducing the detrimental impact of domain differences on transfer learning. Ultimately, a predictive model for aging trajectories, applicable to different types of batteries, is formulated with the transfer method. The model's accuracy is validated across three types of batteries. It is demonstrated that the mean absolute percentage error of aging trajectory for Lithium Cobalt Oxide battery and Ternary Lithium-ion battery are 1.09 % and 1.71 %, respectively.

Multidimensional octahedron (de) intercalation cathode for aqueous zinc-ion battery

CuS is a suitable material for use in lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). Nevertheless, the Zn2+ reaction between CuS is challenging due to the high radius and the considerable electrostatic force between the Zn2+ and CuS cathode. In this work, CuS nanosheets were in situ grown on a template surface via the hydrothermal method. The composition, surface morphology, and microstructure were then studied in detail. The zinc storage properties and mechanism were investigated through electrochemical testing, density functional theory and molecular dynamics simulation. The findings demonstrate that CuS nanosheets are formed in situ along the octahedral surface, resulting in the development of multistage structures. The distinctive configuration is capable of efficaciously preventing nanosheet aggregation, augmenting the specific surface area and active site of electrochemical reactions. Furthermore, the formation of a hollow structure through ion exchange reduces the intercalation energy barrier of Zn2+, facilitates rapid ion diffusion, enhances the charge transfer rate, and markedly improves battery performance.

Integrating ensemble learning and meta bagging techniques for temperature-specific State of Health prediction in Lithium-ion Batteries

Accurately predicting the State of Health (SOH) of lithium-ion batteries is crucial in the field of battery technology to guarantee operational dependability and durability. This study introduces a novel methodology that integrates ensemble learning and meta-bagging techniques to enhance the precision of SOH prediction, particularly in scenarios characterized by fluctuating temperature conditions. We start our methodology by conducting an extensive data-gathering phase, specifically targeting crucial variables: temperature, voltage, current, and capacity. Subsequently, a thorough data preparation stage ensues, encompassing tasks such as cleansing, standardization, and curating relevant features, with particular attention given to temperature characteristics. Next, we employ a temperature-adaptive ensemble learning model that combines different prediction algorithms, such as Linear Regression (LR) and Long Short-Term Memory (LSTM) networks. These algorithms are trained on temperature-stratified data subsets using a Meta-Bagging Estimator (MBE). This strategy enhances the precision of predictions and the ability to overcome the obstacle of temperature changes impacting battery performance. The efficacy of the ensemble model is thoroughly assessed utilizing diverse performance criteria, showcasing its improved predictive capability compared to conventional approaches. Our findings indicate that this customized strategy is better equipped to manage the intricate dynamics of lithium-ion battery behavior, resulting in more dependable and precise assessments of battery SOH. This work substantially contributes to battery health monitoring and can significantly advance battery management systems.

A K2C2O4/nano-CaCO3 dual-templated method to prepare rich nitrogen-doped hierarchical porous carbon with excellent lithium storage performance

Three-dimensional interconnected porous carbon with diverse guest element doping is considered one of the most promising anode materials in lithium-ion batteries (LIBs) owing to their large lithium storage capacity and the acceleration effects on the electrochemical kinetics. However, the regulation of desirable pore structure and guest element amount and species for high energy density and stable cycling performance is still challenging. Herein, sucrose-based hierarchical N-doped porous carbon (SHC-750) is prepared via a facile one-step carbonization/activation process at 750 °C for 1 h. The K2C2O4 and nano-CaCO3 are employed as the dual activating-agent/template, and the urea is used as an N source. Benefiting from the hierarchical porous structure, abundant defects, and high amount of N/O-containing functional groups with a multi-chemical state, SHC-750 delivers an excellent reversible specific capacity (1051.2 mAh/g at 0.1 A/g and) and great cycling stability. Moreover, SHC-750 exhibits fast lithium ions diffusion kinetics, which is attributed to the special ordered and hierarchical pore structure. This work provides a new pathway for the preparation and application of 3D interconnected porous carbon materials in the field of electrodes.

Reconstruction of LATP-LT composite solid electrolyte and analysis of Li+ conduction mechanism using 3D CT

Li1+xAlxTi2-x(PO4)3 (LATP) is a highly promising solid-state electrolyte, known for its high lithium-ion conductivity and stability at room temperature. This study successfully prepared LATP-LT composite solid-state electrolytes by combining LATP with Li2O-TeO2 (LT) glass, utilizing the molten state of the glass at high temperatures. The investigation focused on the effects of different ratios and sintering conditions on the performance of the LATP-LT composites. Results indicate that an appropriate amount of LT enhances the material's sinterability, facilitating continuous lithium-ion conductivity while inhibiting the diffusion of lithium dendrites within the pores. The LATP-LT composite solid electrolyte achieved an ionic conductivity of 1.81×10-4 S/cm at room temperature. Thus, selecting an optimal concentration of LT can serve as a breakthrough in the development of all-solid-state lithium-ion batteries, advancing the field of solid-state electrolytes.

Carbon-coated MoS2/Mxene hybrids for enhanced Li-ions storage performance

MoS2/Mxene/C was synthesized by a facile hydrothermal method. The electrochemical measurements revealed that, in comparison to MoS2/Mxene, the MoS2/Mxene/C composite exhibited an initial charge/discharge capacity of 1440 mAh/g and 2107 mAh/g at a current density of 0.1 A/g. Furthermore, it maintained a specific capacity of 688.2 mAh/g after undergoing 200 cycles at a higher current density of 1 A/g, thereby demonstrating remarkable superior specific capacity and cycling stability. Consequently, MoS2/Mxene/C is anticipated to serve as a high-performance anode material for lithium-ion batteries (LIBs).

Enhancement of battery thermal management effect by a novel MOF based composite phase change material

The introduction of phase change materials (PCMs) into battery thermal management systems (BTMS) can effectively enhance the cooling performance, safety and practical thermal management applications of lithium-ion batteries (LIBs). However, further study is needed to address the low heat conductivity and rapid phase change leakage of PCMs. This study proposed a metal–organic framework based shape-stabilized composite PCM (MOF/expanded graphite (EG) /multi-walled carbon nanotube (MWCNT) /paraffin wax (PW)) to construe battery cooling system, and its effect of battery thermal management is experimentally tested under various conditions. The results show the CPCM reveals a superior thermal management effect under varying ambient temperatures and discharge multipliers and outperforms natural convection. Even in the harsh environment of 40 °C and 3 C, the maximum temperature (Tmax) and Tmax difference (ΔTmax) of the CPCM-cooled module are 61.38 and 2.67 °C, which are 22 and 9 °C below the natural air-cooled module. Remarkably, the ΔTmax of the CPCM-cooled battery module in discharge decreases with the temperature rise at the discharge rate of 3 C, which is the exact opposite to the case of the air-cooled module. Moreover, the ΔTmax of CPCM-cooled module is 1.69, 1.84, 2.67 °C, all below the safe 5 °C at high temperatures (40 °C) as the discharge rate increases. The designed MOF-CPCM cooling system can effectively improve the temperature uniformity and thermal safety of the battery in harsh environments. Therefore, this novel MOF-based CPCM shows great promise in BTM.

Separator porous skeleton regulation dependency on lithium-ion and lithium-metal battery performances

Building separator porous skeletons that suit lithium-ion and lithium-metal batteries (LIBs and LMBs) remains challenging since battery assembly and operation degrade separator mechanical-chemical stabilities inevitably. To fabricate separators with industrial considerations and battery compatibility, herein, the effect of intrinsic porous skeletons on separator mechanical/chemical stabilities and subsequent LIB or LMB performances are clarified based on uncovering integrated porous skeleton formation mechanisms. Lamellae separation and slip dominate longitudinal drawing, severally generating inter-fibril defects and inner-fibril lamellae. Transverse drawing sequentially compels inter-fibril defects to split, which deflects inner-fibril lamellae and enlarges pores. Homogenized lamellae dispersions thus exhibit unique advantages, maximizing fibrous refinement and uniformizing porous skeletons. This superior porous skeleton with smoother linearity facilitates homogeneous Li+ fluxes, stabilizes Li plating-stripping, and enables improved battery performances, especially at high currents, indicating more suitable with high-power density batteries and fast charging technologies. The dependency clarifications of separator fabrication-structure-function lend theoretical guidance for fabricating and choosing ideal porous skeletons for not only LIBs and LMBs but also the other prospective rechargeable batteries.

Degradation modeling of serial space lithium-ion battery pack based on online inconsistency representation parameters

Establishing an inconsistency-based degradation model for lithium-ion battery packs is crucial for suppressing the degradation of battery packs by optimizing the inconsistency. This paper proposes a method for modeling the degradation of serial space lithium-ion battery packs based on online inconsistency representation parameters. Firstly, the static inconsistency representation parameters are acquired online by quantifying the difference among voltage intervals of cells through Gaussian distribution, addressing the challenge of acquiring static inconsistency representation parameters online. Simultaneously, dynamic inconsistency representation parameters are serialized representations by quantifying the voltage differences of cells at multiple moments, better capturing the rapid evolutionary process of inconsistency. Secondly, a linear model is used to model the serialized parameters and degradation, simplifying the modeling process while achieving multi-source information fitting. Meanwhile, a nonlinear model is constructed to better fit the nonlinear degradation trend of the battery pack. Then, Kalman filter is utilized for model fusion to achieve complementary advantages and improve modeling accuracy. Cross-validation with laboratory battery pack data confirms that this method outperforms comparison approaches, achieving the mean absolute error and maximum error of less than 1.73% and 3.31%, respectively. The method proposed in this paper provides a basis for future work on battery pack life extension.

Carbon sequestration behavior of magnesium oxychloride cement based on salt lakes magnesium residue and industrial solid waste

With the extensive utilization of lithium-ion battery in the electric vehicle and energy storage field, the consumption of lithium has been sharply increasing. Lithium resource occurrence area were facing increasing environmental pressure, particularly the magnesium residue (MR) produced in the lithium extraction process, and a sustainable exploitation pathway have not been established. In the framework of "net-zero", MRs were onverted to Salt lake magnesium oxide (SL-MgO) which was characterized by various elemental and surface analysis methods. Magnesium oxychloride cement (MOC) was prepared form SL-MgO and two industrial solid wastes [fly ash (FA) and phosphogypsum (PG)], and its carbon sequestration capacity was analyzed and evaluated. If all the MRs produced from the lithium extraction process were used to manufacture MOC materials for CO2 sequestration. When the PG content was 20 %, the CO2 sequestration capacity of the MOC was 0.29 kg/m2, the compressive strength was 85.30 MPa, and the MOC neutralized 220.10 % of the CO2 emissions from the lithium extraction process. In this procedure, evidence was found of the typical metastable carbonate products identifiable. Overall, utilizing MRs and industrial solid waste to manufacture new low-carbon MOCs may become the most direct and effective countermeasures to alleviate environmental pressure in these regions.

Thermal management strategies for lithium-ion batteries in electric vehicles: A comprehensive review of nanofluid-based battery thermal management systems

Thermal management strategies for lithium-ion batteries in electric vehicles: A comprehensive review of nanofluid-based battery thermal management systems

Investigation of the mechanical response and modeling of prismatic lithium-ion batteries upon abusive loading

Investigation of the mechanical response and modeling of prismatic lithium-ion batteries upon abusive loading

Unveiling the effect of cycle time and ambient temperature on the failure mechanisms of commercial LiCoO2/artificial graphite pouch cells

Many efforts have been devoted to investigate failure mechanisms of LiCoO2-based lithium-ion batteries at ≥4.55 vs. Li+/Li. However, most of these works are conducted on coin-type half-cells and rarely consider the effects of cycle time and ambient temperature on mechanisms. So, it calls an alarming demand for making clear the cyclabilities of cells at different temperatures, then differentiating the root causes for their capacity degradations. Herein, the cyclabilities of an ~3.1 Ah commercial LiCoO2/artificial graphite pouch cell at 3–4.45 V are compared at 25 and 60 °C. It is found that the cycle lives at 25 and 60 °C are 2944 and 122 cycles, respectively. The structural variations of the cycled electrodes are systematically studied. Results reveal that the main cause for the capacity beginning to decline after 3000 cycles at 25 °C is the damage of LiCoO2 structure, while its impedance growth and other side reactions are not severe. Differently, the capacity dropping upon cycling at 60 °C is caused by the parasitic reactions including successive electrolyte decomposition, severe cobalt dissolution and Li deposition. Obviously, these findings can provide a theoretical basis for further optimizing of LiCoO2 cells.

A Data-Driven Fault Tracing of Lithium-Ion Batteries in Electric Vehicles

A Data-Driven Fault Tracing of Lithium-Ion Batteries in Electric Vehicles

MINN: Learning the Dynamics of Differential-Algebraic Equations and Application to Battery Modeling.

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in modeling real-world dynamic systems for optimization and control purposes. We propose a novel machine learning architecture, termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.