Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO₄ batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (ε_n, D_s,n) and electrolyte (D_e, C_e) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition.

The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods.

The second-use applications of retired batteries from electric vehicles have considerable economic benefits, but the bottleneck of the large-scale applications lies in long detection time and inaccuracy estimation of retired batteries performance. In this study, a hybrid method based on the mechanism and data-driven model that only uses the partial charging voltage profile to achieve fast and accurate residual capacity estimation with two main contributions. First, the partial voltage corresponding to the single-phase region ① of the LiFePO4 electrode is selected by analyzing the phase transition feature in the LiFePO4 electrode, and then the Levenberg-Marquardt method is used to obtain the three health indicators that characterize the battery capacity loss mechanism. Based on the three health indicators, the support vector regression model is employed to estimate the battery residual capacity. 920 retired batteries are used to verify the proposed method, the results show that the root mean square error of the proposed method is within 2.67% using only the first 10% of the data, which is close to that based on the full voltage, and the corresponding residual capacity detection time only 6–10 min. Second, through observability analysis, the three health indicators are independent of the C-rate and only related to the negative potential. Results show that the high estimation accuracy of 1C high C-rate based on partial voltage can be provided, which can further shorten the battery detection time by appropriately increasing the C-rate. These advantages demonstrate the proposed method shortens the large-scale applications process of retired batteries.