The extensive application in recent years of lithium-ion batteries (LIBs) based on an LiFePO₄ (LFP) cathode in electric vehicles will lead to a large amount of scrapped LFP in the foreseeable future. Therefore, recycling these scrapped cathode materials appropriately will become an extremely important issue. Here, a facile and green method is developed to directly regenerate scrapped LFP into a fresh cathode. We propose a re-lithiation approach that intercalates lithium ions into scrapped LFP in an aqueous solution system. Specifically, the configuration of the recycling device is a H-type electrolytic bath, in which the cathode and anode electrolytes are separated by an anion-exchange membrane, a zinc plate is used as the anode, the scrapped LFP suspension is regarded as the cathode, and the electrolyte is lithium salt aqueous solution. The regenerated LFP is obtained via a discharging process. It is found that the effectiveness of the recycling process is directly related to the parameters of the discharge current (mA) and theoretical intercalation amount of lithium (TIA). The results show that the performance of LFP recycled at a current of 5 mA and 150%-TIA is the best; it has high lithium content and better crystallinity, and it also exhibits excellent electrochemical performance with a high discharge capacity of 134.0 mA h g⁻¹ at 1C and a capacity retention rate of 85.5% after 300 cycles. Furthermore, we adopt FTIR spectroscopy to assess the quality of the regenerated LFP intuitively and simply, thus providing a feasible monitoring standard for industrial production.

Designing bifunctional oxygen electrocatalysts with high activity, lasting stability, and low-cost for rechargeable zinc-air batteries (RZABs) is a tough challenge. Herein, an advanced electrocatalyst is prepared by anchoring atomically dispersed Co atoms on N-doped graphene-like hierarchically porous carbon nanosheets (SA-Co-N₄-GCs) and thereby forming Co-N₄-C architecture. Its unique structure with excellent conductivity, large surface area, and three dimensional (3D) interconnected hierarchically porous architecture exposes not only more Co-N₄ active sites to accelerate the kinetics of both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), but also provides an efficient charge/mass transport environment to reduce diffusion barrier. Consequently, SA-Co-N₄-GCs exhibits excellent ORR/OER bifunctional activities and durability, surpassing noble-metal catalysts. Liquid RZABs using SA-Co-N₄-GCs cathodes display a high open-circuit voltage of 1.51 V, a remarkable power density of 149.3 mW·cm⁻², as well as excellent stability and rechargeability with faint increase in polarization even at a large depth of charge—discharge cycle with 16 h per cycle over an entire 600 h long-term test. Moreover, flexible quasi-solid-state RZABs with SA-Co-N₄-GCs cathodes also deliver a considerable power density of 124.5 mW·cm⁻², which is even higher than that of liquid batteries using noble-metal catalysts. This work has thrown new insight into development of high-performance and low-cost electrocatalysts for energy conversion and storage.

One of the crucial methods for adapting distributed PV generation is the microgrid. However, solar resources, load characteristics, and the essential microgrid system components are all directly tied to the optimal planning scheme for microgrids. This article conducts a collaborative planning study of grid-connected PV-storage microgrids under electric vehicle integration in various scenarios using HOMER 1.8.9 software. To be more specific, in multiple scenarios, we built capacity optimization models for PV modules, energy storage, and converters in microgrids, with several scenarios each accounting for the cleanliness, economic performance, and overall performance of microgrids. For multiple scenarios, this paper used the net present value cost and levelized cost of electricity as indicators of microgrid economics, and carbon dioxide emissions and the fraction of renewable energy were used as indicators of microgrid cleanliness. The optimal capacity allocation for economy, cleanliness, and a combination of economy and cleanliness were separately derived. Finally, on a business park in Wuhan, China, we conducted thorough case studies to compare and debate the planning performance under various scenarios and to undertake sensitivity analyses on the cases. The sensitivity analyses were conducted for the optimal configuration of microgrids in terms of the EV charging scale, carbon dioxide emissions, PV module unit cost, and storage unit cost. The results of the simulation and optimization show that the optimization approach could determine the ideal configuration for balancing economy and cleanliness. As the EV charging demand increased, the energy storage capacity required in the microgrid gradually increased, while the carbon dioxide emission limit was negatively correlated with the energy storage capacity demand. The unit investment cost of PV module units had a greater impact on the optimal system configuration than the cost of batteries.

Due to its porous structure and special reaction characteristics, the cathode-electrolyte interface in alkali metal-oxygen batteries (AMOBs) has a substantial impact on their electrochemical performance. However, in traditional sandwich-like battery structures, the reaction position in the cathode is restricted to the finite planar cathode-electrolyte interface, leading to AMOBs with limited performance. As a result, a growing number of research studies have sought to re-engineer the cathode-electrolyte interface to enhance the performance of AMOBs. This review summarizes the latest methods published in recent years in this field and compares a variety of different techniques. Regardless of the method used, the ultimate goal is to expand the cathode-electrolyte interface to create more triple reaction activity sites for ions, oxygen and electrons. The most important performance improvement of AMOBs is reflected by the increased specific capacity. Additional challenges valuable for the further development of alkali metal-oxygen batteries are also discussed

Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The parameter identification methods include the voltage response curve analysis method, the least squares method and so on. On this basis, the methods used for modeling and estimating the SOP of battery cells and battery packs are classified and elaborated, focusing on summarizing the research progress observed regarding the joint estimation method for multiple states of battery cells. In conclusion, future methods for estimating the SOP of lithium-ion batteries and their improvement targets are envisioned based on the application requirements for the safe management of lithium-ion batteries.

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.