Chuanyu Bie

The exponential surge in global demand for clean energy has significantly propelled the extensive deployment of lithium‐ion batteries (LIBs), which serve as pivotal energy storage components. As the initial generation of commercial LIBs progressively reaches their end‐of‐life stage, the recycling of spent lithium iron phosphate batteries (SLFPs) and the associated resource regeneration and utilization have emerged as critical concerns within the industry. This review compiles and evaluates the most recent advancements in the direct regeneration of SLFPs cathode materials. A comprehensive analysis is conducted to elucidate the fundamental causes of material degradation and the attendant technical challenges. Additionally, an in‐depth summary of the prevalent direct regeneration methodologies employed in recent years is presented, encompassing the solid‐phase sintering technique, hydrothermal process, molten salt method, and electrochemical approach. Furthermore, the investigation delves into the underlying mechanisms by which surface modification techniques and ion‐doping strategies influence the performance characteristics of regenerated materials. From an economic perspective, a comparative assessment of the costs and revenues associated with various regeneration technologies is performed. Conclusively, in light of the existing limitations in current research, prospective directions are proposed, including the establishment of a comprehensive recycling and utilization framework, the facilitation of industrial implementation, and the refinement of characterization techniques for spent batteries.

In order to solve the problems of long-time consumption and high energy consumption in existing capacity detection methods of retired Ni-MH batteries, a fast and reliable capacity prediction method for retired Ni-MH batteries by multi-parameter driven analysis was proposed in this paper. This method mainly obtains several parameters through short-time measurement and pulse rapid nondestructive testing. Then, Pearson correlation coefficient and KS-test were used to analyze the correlation between the two parameters and verify the same distribution. Finally, SVR was used to predict the battery discharge capacity. The results show that the volume expansion thickness difference Δd, AC internal resistance R, terminal voltage U of the battery, charge and discharge polarization internal resistance R_f1 and R_f2 and pulse charging power P₂ of the battery are strongly negatively correlated with the discharge capacity, and these characteristic parameters can effectively and reliably reflect the internal structural characteristics of the battery. Additionally, the mean relative error of the established capacity model is 5.87%, and the lowest error is 1.32%. The prediction effect is good, which provides a certain reference value for the subsequent consistent sorting method.