Silicon-based anode is a promising candidate for all-solid-state batteries (ASSBs). However, it must be further improved because of its tremendous volume change. In this study, various interface treatment strategies for SiO/carbon composite anodes in ASSBs were investigated using a multiphysics modeling framework. By evaluating the effects of active (carbon) and inactive coating materials, as well as the geometric and mechanical parameters, this research provides critical insights into optimizing their electrochemical performance and mechanical stability. Computational results indicate that carbon coatings can greatly enhance lithiation kinetics by regulating the interfacial electrochemical potential gradients, reducing the residual lithium concentration, and homogenizing the lithium-ion distribution compared with uncoated or inactive-coated configurations. In addition, thinner carbon coatings further improve capacity retention and stress management by balancing shorter lithium diffusion pathways with mitigated interfacial stress accumulation. Despite their ability to mechanically stabilize the anode, inactive coatings exhibit tradeoffs between lithium transport kinetics and stress modulation, with optimal performance achieved at lower Young’s moduli. Mechanical analyses highlight distinct failure mechanisms at the anode–electrolyte (shear driven) and particle-coating (tension driven) interfaces, emphasizing the need for tailored adhesion strategies. These findings provide actionable guidelines for designing robust SiO-based anodes, emphasizing the interplay among electrochemical efficiency, stress regulation, and interfacial durability in ASSBs.

Electric Vertical Take-off and Landing (eVTOL) aircraft is one of the significant trends in the development of renewable low-altitude aircraft. To meet the requirements for safe and reliable operation of eVTOL, it is necessary for the battery management system to accurately and efficiently estimate the State-of-Health (SoH) of the on-board lithium-ion battery system based on the characteristics of the flight profile. At present, most SoH estimation algorithms for eVTOL applications only consider the battery capacity loss, and lack the ability to comprehensively and precisely estimate battery degradation in both energy and power based on the data recorded during the specific flight phase. To address the aforementioned issue, the battery current and voltage measured during the take-off phase of the eVTOL aircraft are selected as the inputs for the estimation algorithm, and the battery capacity, the ohmic resistance, and the polarization resistance are utilized as the health indicators to comprehensively characterize the battery degradation. To improve the training efficiency and estimation accuracy of the model for multi-indicator estimation, a multi-output least squares support vector regression-based method for joint estimation of multiple battery health indicators is proposed, which can achieve rapid and accurate estimation of the battery capacity and resistance by considering the coupling relationship among output parameters. Finally, experimental verification is conducted based on the eVTOL Battery Dataset. The results of the comparative study demonstrate that based on the proposed method, the mean absolute percentage error of the estimated battery capacity and internal resistance are overall below 2.5% and the model training time is within 0.05 s, successfully realizing a comprehensive, efficient, and accurate estimation of lithium-ion battery health indicators for eVTOL applications.

Lithium-ion batteries are widely used in electric vehicles because of their high energy density and long cycle life. However, the spontaneous combustion accident of electric vehicles caused by thermal runaway of lithium-ion batteries seriously threatens passengers' personal and property safety. This paper expounds on the internal mechanism of lithium-ion battery thermal runaway through many previous studies and summarizes the proposed lithium-ion battery thermal runaway prediction and early warning methods. These methods can be classified into battery electrochemistry-based, battery big data analysis, and artificial intelligence methods. In this paper, various lithium-ion thermal runaway prediction and early warning methods are analyzed in detail, including the advantages and disadvantages of each method, and the challenges and future development directions of the intelligent lithium-ion battery thermal runaway prediction and early warning methods are discussed.