Battery System Research Articles

Microscale insight into the proton concentration during electrolytic reaction via an optical microfiber: potential for microcurrent monitoring by a dielectric probe

Local microcurrent monitoring is of great significance for biological and battery systems, yet it poses a formidable challenge. The current measurement techniques rely on electromagnetic materials which inevitably introduce interference to the system under examination. To address this issue, a promising approach based on a dielectric fiber-optic sensor is demonstrated. The microfiber is capable of detecting microcurrent through monitoring the localized proton concentration signal with a pH resolution of 0.0052 pH units. By sensing the refractive index variation surrounding the sensor induced by the interaction between local proton concentration changes and oxidizer-treated microfiber surface through the evanescent field, this sensing mechanism effectively avoids the interference of the electromagnetic material on the performance of the tested system. This sensor exhibits a limit of detection for microcurrent of 1 μA. The sensing region is a microfiber with a diameter of 8.8 μm. It can get invaluable information that cannot be obtained through conventional electrochemical methods. Examples include photocurrent attenuation in photogenerated carrier materials during illumination, electrical activation in nerve cells, and fluctuations in the efficiency of electrical energy generation during battery discharge. This approach provides a powerful complement to electrochemical methods for the elucidation of microscale reaction mechanisms. The information provided by the prepared dielectric fiber-optic sensor will shed more light on proton kinetics and electrochemical and electrobiological mechanisms, which may fill an important gap in the current bioelectricity and battery monitoring methods.

Reinventing the High‐rate Energy Storage of Hard Carbon: the Order‐degree Governs the Trade‐off of Desolvation —Solid Electrolyte Interphase at Interfaces

In alkali metal‐ion battery systems, the electrolyte enables being decomposed on the electrode surface to form a solid electrolyte interphase film (SEI). In principle, a thin, uniform SEI film facilitates to enhancement of the performance of the cell. Herein, we successfully distinguish the effects of desolvation behavior and SEI process on the kinetic behavior of hard carbon (HC) electrodes by adopting the strategy of switching the electrolyte interface model to modulate the properties of SEI film. Our findings reveal that although the SEI film is generally responsible for significantly affecting the HC’s capacity, the equally crucial desolvation process must not be overlooked. The trade‐off between the two factors is found to be determined by the structural features of HCs. Specifically, in the context of a more ordered HC, the desolvation of ions emerges as the rate‐limiting step for Na+ transport across the electrode/electrolyte interface, exerting a more pronounced effect rather than the SEI. Thus, a close correlation was established between the SEI, solvation structure effects, hard carbon structure, and electrode performance. This linkage is thereof fundamental for the strategic design of electrolytes and the targeted enhancement of cell performance.

Reinventing the High-rate Energy Storage of Hard Carbon: the Order-degree Governs the Trade-off of Desolvation -Solid Electrolyte Interphase at Interfaces.

In alkali metal-ion battery systems, the electrolyte enables being decomposed on the electrode surface to form a solid electrolyte interphase film (SEI). In principle, a thin, uniform SEI film facilitates to enhancement of the performance of the cell. Herein, we successfully distinguish the effects of desolvation behavior and SEI process on the kinetic behavior of hard carbon (HC) electrodes by adopting the strategy of switching the electrolyte interface model to modulate the properties of SEI film. Our findings reveal that although the SEI film is generally responsible for significantly affecting the HC's capacity, the equally crucial desolvation process must not be overlooked. The trade-off between the two factors is found to be determined by the structural features of HCs. Specifically, in the context of a more ordered HC, the desolvation of ions emerges as the rate-limiting step for Na+ transport across the electrode/electrolyte interface, exerting a more pronounced effect rather than the SEI. Thus, a close correlation was established between the SEI, solvation structure effects, hard carbon structure, and electrode performance. This linkage is thereof fundamental for the strategic design of electrolytes and the targeted enhancement of cell performance.

RUL Prediction for Lithium Battery Systems in Fuel Cell Ships Based on Adaptive Modal Enhancement Networks

With the widespread application of fuel cell technology in the fields of transportation and energy, Battery Management Systems (BMSs) have become one of the key technologies for ensuring system stability and extending battery lifespan. As an auxiliary power source in fuel cell systems, the prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for enhancing the reliability and efficiency of fuel cell ships. However, due to the complex degradation mechanisms of lithium batteries and the actual noisy operating conditions, particularly capacity regeneration noise, accurate RUL prediction remains a challenge. To address this issue, this paper proposes a lithium battery RUL prediction method based on an Adaptive Modal Enhancement Network (RIME-VMD-SEInformer). By incorporating an improved Variational Mode Decomposition (VMD) technique, the RIME algorithm is used to optimize decomposition parameters for the adaptive extraction of key modes from the signal. The Squeeze-and-Excitation Networks (SEAttention) module is employed to enhance the accuracy of feature extraction, and the sparse attention mechanism of Informer is utilized to efficiently model long-term dependencies in time series. This results in a comprehensive prediction framework that spans signal decomposition, feature enhancement, and time-series modeling. The method is validated on several public datasets, and the results demonstrate that each component of the RIME-VMD-SEInformer framework is both necessary and justifiable, leading to improved performance. The model outperforms the state-of-the-art models, with a MAPE of only 0.00837 on the B0005 dataset, representing a 59.96% reduction compared to other algorithms, showcasing outstanding prediction performance.

In-situ electrochemical activation accelerates the magnesium-ion storage

Rechargeable magnesium batteries (RMBs) have emerged as a highly promising post-lithium battery systems owing to their high safety, the abundant Magnesium (Mg) resources, and superior energy density. Nevertheless, the sluggish kinetics has severely limited the performance of RMBs. Here, we propose an in-situ electrochemical activation strategy for improving the Mg-ion storage kinetics. We reveal that the activation strategy can effectively optimize surface composition of cathode that favors Mg-ion transport. Cooperating with lattice modifications, the CuSe | |Mg batteries exhibit a specific capacity around 160 mAh/g after 400 cycles with a capacity retention of over 91% at the specific current of 400 mA/g. Of significant note is the slight decay in specific capacity from 205 to 141 mAh/g has been observed with an increase in specific current from 20 to 1000 mA/g. This strategy provides insights into accelerating Mg-ion storage kinetics, achieving a promising performance of RMBs especially at high specific current.

Battery aging estimation algorithm with active balancing control in battery system

Battery aging estimation algorithm with active balancing control in battery system

Evaluation of a novel indirect liquid-cooling system for energy storage batteries via mechanical vapor recompression and falling film evaporation

Evaluation of a novel indirect liquid-cooling system for energy storage batteries via mechanical vapor recompression and falling film evaporation

A Brief Overview of Modeling Estimation of State of Health for an Electric Vehicle’s Li-Ion Batteries

The current literature highlights several state-of-health (SOH) prediction models for lithium-ion (Li-ion) batteries used in electric vehicles (EVs). However, a thorough comparative analysis remains absent. This study addresses this gap by conducting a comprehensive evaluation of SOH prediction methods for Li-ion batteries in EV applications, encompassing direct measurement techniques, physics-based approaches, and data-driven methodologies. The analysis identifies the strengths, limitations, and applicability of each modeling method. Additionally, this study explores key indicators of SOH, influential variables affecting battery health, and publicly available datasets that support SOH modeling. By synthesizing these insights, the research provides recommendations for improving existing models and outlines prospective directions for enhancing the accuracy and efficiency of SOH estimation in EV applications. This work aims to contribute to the development of robust, accurate, and practical SOH models, thereby advancing the reliability and sustainability of Li-ion battery systems in the growing EV industry.

Editors’ Choice—Investigation of the Dynamic Evolution of the Cathode-Electrolyte Interphase Using Scanning Electrochemical Microscopy

Cathode-electrolyte interphase (CEI) is critical for inhibiting the cathode degradation to maintain cell life. However, the evolution of the CEI is still unclear due to its complex and slow dynamic process. Here we used scanning electrochemical microscopy (SECM) for in situ investigation of CEI formation process on LiFePO4 cathode. Feedback images and probe scan curves showed a heterogeneous passivation that was gently generated on the LiFePO4 particles during both charging and discharging. Besides, a LiFePO4 composited electrode was also used to investigate the CEI formation to simulate the condition of real battery system. The composited cathode does not show obvious CEI formation within first two cycles. The SECM results between the pristine LiFePO4 particles and the composited LiFePO4 indicated the dynamic accumulation of CEI, which is influenced by the ability to charge transfer kinetics of cathode materials. This approach provided a feasible consideration for the connections between the dynamic evolution of the CEI and changes in charge transfer capability of cathode during cycling.

A lithium-ion battery system with high power and wide temperature range targeting the internet of things applications

A lithium-ion battery system with high power and wide temperature range targeting the internet of things applications

Battery Sentiment Analysis and Recommention System

- The increasing reliance on battery technology across industries such as renewable energy, electric vehicles, and portable electronics underscores the importance of robust analysis and optimization of battery systems. Battery-centric analysis focuses on understanding key parameters such as temperature, current, voltage, state of charge (SOC), and state of health (SOH) to enhance performance, safety, and longevity. These parameters are critical in identifying potential failures, improving efficiency, and predicting battery life cycles. This study employs advanced methodologies, including machine learning algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN), to analyse battery performance data. By training and testing these models on extensive datasets, the research provides accurate predictions of battery behavior under diverse operating conditions. Key metrics, including accuracy, root mean square error (RMSE), and mean absolute error (MAE), are used to evaluate the effectiveness of these algorithms, ensuring reliable insights into battery health and operation. The integration of real-time monitoring systems with predictive analytics enhances the safety and efficiency of batteries, reducing risks such as overheating, overcharging, and premature failure. Additionally, this approach supports the development of sustainable energy solutions by identifying areas for material optimization and improving the design of energy storage systems. Key Words: Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN).

Engineering of Lignocellulose Pulp Binder for Ah‐Scale Lithium–Sulfur Batteries

AbstractNatural binders play attractive roles in stabilizing lithium–sulfur (Li–S) battery systems due to their polymeric skeleton and abundant functional structures, but the complex extraction and modification hinder the practical use. Here, lignocellulose, the unbleached product of the pulp industry, is directly developed as a robust binder in Li–S batteries. Benefiting from various oxygen‐containing functional groups woven strong hydrogen‐bonded network framework, robust mechanical stability, lithium polysulfide anchoring capacity, and high‐speed Li+ transport channel. The Li–S cell battery delivers an initial discharge capacity of 996 mAh·g⁻¹ at a current density of 0.5 C and can stably run 500 cycles. Moreover, an Ah‐scale pouch cell is constructed and delivers notable gravimetric and volumetric energy densities of 322 Wh·kg⁻¹ and 432 Wh·L⁻1, respectively. This work expands the application boundaries of bulk lignocellulose pulp in advanced energy storage systems.

Mechanical degradation on defective lithium-ion batteries

Abstract Unavoidable minor electric vehicle collisions can cause defects and deformations in lithium-ion batteries (LIBs). The safety of such defective batteries is crucial for ensuring the overall safety and stability of battery systems. However, the usability of a defective battery and the mechanisms underlying the damage remain unclear. In this study, we focus on the mechanical performance of defective batteries, safety thresholds, and the associated failure mechanisms. Initially, two typical defective cells are prepared by quasi-static loading using different indenters. The mechanical responses of these cells are then assessed via quasi-static flat compression. Lastly, a mechanical failure criterion for the battery is proposed based on the thickness of the separator. The effects of defect area and defect angle on the performance of defective cells are also discussed. These results provide valuable insights for the safety assessment and management of defective lithium-ion batteries.

Sabatier Principle Inspired Bifunctional Alloy Interface for Stable and High-Depth Discharging Zinc Metal Anodes.

Achieving stable Zn anodes is essential for advancing high-performance Zn metal batteries. Here, we propose a Sabatier principle inspired bifunctional transition-metal (TM) interface to enable homogeneous Zn dissolution during discharging and dendrite-free Zn deposition during charging. Among various TM-coated Zn (TM@Zn) electrodes, Cu@Zn exhibits the highest reversibility and structural stability, attributed to the optimal interaction between Cu and Zn. The heteroatomic interaction-dependent electrochemical performance parallels the Sabatier principle. Morphological analyses reveal that bare Zn anodes display detrimental etching pits during stripping, which is different from the uniform dissolution for Cu@Zn electrodes. During subsequent plating, the conductive interface serves as a secondary current collector for uniform Zn deposition in Cu@Zn, thus demonstrating a bifunctional nature. Atomic observations disclose the working mechanisms of this interface as a gradual phase transition from Cu to CuZn5 during cycling. The Cu@Zn anodes exhibit an ultralong cycling lifespan of over 8000 h at a low current of 1 mA cm-2 and over 250 h at a high depth of discharge of 80%. They also demonstrate practical feasibility by maintaining 88.7% capacity retention after 1000 cycles in Cu@Zn||VO2 full cells. This work provides new insights into the Sabatier chemistry inspired bifunctional layers for Zn metal battery system.

Ligand Engineering Regulation toward Zn Ions and Zn Substrate for All‐Climate Zn Metal Batteries

AbstractThe regulation of artificial interphase for advanced Zn anode is an effective solution to achieve superior electrochemical performance for aqueous batteries. However, the deployment of atomically precise architectures and ligand engineering to achieve functionalization‐oriented regulatory screening is lacking, which is hindered by higher requirements for synthetic chemistry and structural chemistry. Herein, we have first performed ligand engineering which selected zinc ion trapping ligands (−CH3) based on the coordination effect, and zinc substrate binding ligands (−N=N−C6H5) based on the electrostatic interaction. Correspondingly, octa nuclear Zn(II)‐Siloxane‐PhPz/BiPhPz/TriPhPz Cluster (OZSPC/OZSBPC/OZSTPC) are accurately synthesized, and OZSBPC is verified to serve as the most suitable artificial interphase via balancing the interactions with both Zn2+ and Zn substrate (“Zn−Zn effect”). Consequently, at −30 °C, the assembled OZSBPC‐Zn symmetric cells run for 3000 h and the assembled full cells with OZSBPC‐Zn anode could be stable for 10,000 cycles. The pouch cells using OZSBPC‐Zn anode deliver a reversible capacity of ~1.2 Ah and the energy density of 41 Wh kgtotal−1 with excellent cycling performance. The successful structural design of OZSBPC explores a novel well‐defined structural design concept as one criterion for artificial interface as well as motivates batteries systems to meet the requirements of industrialization.

Dual-Induced Directed Deposition Mechanism Based on Anionic Surfactants Enables Long Cycle Aqueous Zinc Ion Batteries.

Aqueous zinc-ion battery has low cost, and environmental friendliness, emerging as a promising candidate for next-generation battery systems. However, it still suffers from a limited cycling life, caused by dendritic Zn growth and severe side reactions. Recent research highlights that the Zn (002) crystal plane exhibits superior anti-corrosive properties and a horizontal growth pattern. However, achieving uniform deposition on the Zn (002) plane remains a formidable challenge. Here, preferential rapid growth of the Zn (002) plane is manipulated via the dual-induced deposition effect of anionic surfactant (2-acrylamido-2-methylpropanesulfonic acid, AMPS), achieving Zn metal anode with ultralong cycle life. AMPS can preferentially adsorb on the Zn (100) and Zn (101) crystal planes, exposing the Zn (002) plane as a nucleation site for Zn2+ ions, while the abundant presence of amide groups in AMPS can form fast ion channels, inducing rapid and uniform Zn deposition. Thus, even using 30 µm Zn foils, the symmetric cells can maintain a stable plating-stripping process over 5000 h, and Zn.

Enhanced Thermal Regulation of Lithium-Ion Batteries Using Composite PCM-Fin Structures for High Heat Dissipation

Abstract Poor thermal conductivity is common in batteries that use phase change material (PCM)-based thermal management systems (BTMS). This study introduces cylindrical rings and longitudinal fins to improve heat transfer. The thermal performance of several BTMS configurations is the primary focus of this analysis. The results show that the PCM-Fin system offers superior temperature management compared to both pure PCM and pure battery systems. To understand the mechanisms, numerical simulations were compared against experimental data available in the literature. Numerical analysis show that the fin shapes increase heat transmission and create a thermal conductive network inside the PCM, improving battery thermal performance. The study indicates that the effectiveness of thermal management is influenced by the number of extended surfaces and rings, as well as the heat generation rate. One cylindrical ring and eight longitudinal fins, with a dimensionless spacing of 0.2 between the battery and the ring, are recommended. The PCM-Fin technology also effectively controls the battery's temperature rise at 20 W, proving its durability in high-heat conditions.

Sabatier Principle Inspired Bifunctional Alloy Interface for Stable and High‐Depth Discharging Zinc Metal Anodes

Achieving stable Zn anodes is essential for advancing high‐performance Zn metal batteries. Here, we propose a Sabatier principle inspired bifunctional transition‐metal (TM) interface to enable homogeneous Zn dissolution during discharging and dendrite‐free Zn deposition during charging. Among various TM‐coated Zn (TM@Zn) electrodes, Cu@Zn exhibits the highest reversibility and structural stability, attributed to the optimal interaction between Cu and Zn. The heteroatomic interaction‐dependent electrochemical performance parallels the Sabatier principle. Morphological analyses reveal that bare Zn anodes display detrimental etching pits during stripping, which is different from the uniform dissolution for Cu@Zn electrodes. During subsequent plating, the conductive interface serves as a secondary current collector for uniform Zn deposition in Cu@Zn, thus demonstrating a bifunctional nature. Atomic observations disclose the working mechanisms of this interface as a gradual phase transition from Cu to CuZn5 during cycling. The Cu@Zn anodes exhibit an ultralong cycling lifespan of over 8000 h at a low current of 1 mA cm‐2 and over 250 h at a high depth of discharge of 80%. They also demonstrate practical feasibility by maintaining 88.7% capacity retention after 1000 cycles in Cu@Zn||VO2 full cells. This work provides new insights into the Sabatier chemistry inspired bifunctional layers for Zn metal battery system.

Surface-Confined Disordered Hydrogen Bonds Enable Efficient Lithium Transport in All-Solid-State PEO-Based Lithium Battery.

Polyethylene oxide (PEO)-based electrolytes are essential to advance all-solid-state lithium batteries (ASSLBs) with high safety/energy density due to their inherent flexibility and scalability. However, the inefficient Li+ transport in PEO often leads to poor rate performance and diminished stability of the ASSLBs. The regulation of intermolecular H-bonds is regarded as one of the most effective approaches to enable efficient Li+ transport, while the practical performances are hindered by the electrochemical instability of free H-bond donors and the constrained mobility of highly ordered H-bonding structures. To overcome these challenges, we develop a surface-confined disordered H-bond system with stable donor-acceptor interactions to construct a loosened chain segments/ions arrangement in the bulk phase of PEO-based electrolytes, realizing the crystallization inhibition of PEO, weak coordination of Li+ and entrapment of anions, which are conducive to efficient Li+ transport and stable Li+ deposition. The rationally designed LiFePO4-based ASSLB demonstrates a long cycle-life of over 400 cycles at 1.0 C and 65 °C with a capacity retention rate of 87.5%, surpassing most of the currently reported polymer-based ASSLBs. This work highlights the importance of confined disordered H-bonds on Li+ transport in an all-solid-state battery system, paving the way for the future design of polymer-based ASSLBs.

Enhancing Smart Microgrid Resilience Under Natural Disaster Conditions: Virtual Power Plant Allocation Using the Jellyfish Search Algorithm

Electric power networks face critical challenges from extreme weather events and natural disasters, disrupting socioeconomic activities and jeopardizing energy security. This study presents an innovative approach incorporating virtual power plants (VPPs) within networked microgrids (MGs) to address these challenges. VPPs integrate diverse distributed energy resources such as solar- and wind-based generation, diesel generators, shunt capacitors, battery energy storage systems, and electric vehicles (EVs). These resources enhance MG autonomy during grid disruptions, ensuring uninterrupted power supply to critical services. EVs function as mobile energy storage units during emergencies, while shunt capacitors stabilize the system. Excess energy from distributed generation is stored in battery systems for future use. The seamless integration of VPPs and networked technologies enables MGs to operate independently under extreme weather conditions. Prosumers, acting as both energy producers and consumers, actively strengthen system resilience and efficiency. Energy management and VPP allocation are optimized using the jellyfish search optimization algorithm, enhancing resource scheduling during outages. This study evaluates the proposed approach’s resilience, reliability, stability, and emission reduction capabilities using real-world scenarios, including the IEEE 34-bus and Indian 52-bus radial distribution systems. Various weather conditions are analyzed, and a multi-objective function is employed to optimize system performance during disasters. The results demonstrate that networked microgrids with VPPs significantly enhance distribution grid resilience, offering a promising solution to mitigate the impacts of extreme weather events on energy infrastructure.