The transition to clean energy demands efficient and scalable electrochemical energy storage (EES) systems, including metal-ion and metal–sulfur batteries, as well as supercapacitors. Carbon materials are essential for EES owing to their exceptional electrical conductivity, electrochemical stability, and structural versatility, but their complex structures hinder structure–property mappings, thereby limiting traditional trial-and-error design. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by uncovering hidden structure–property relations and accelerating material discovery. Although various EES materials have been benefited from these advances, the study of carbon materials using this new paradigm is in its early stages. This review provides a comprehensive evaluation of AI-assisted methods applied to carbon materials across diverse EES systems, including metal-ion and metal–sulfur batteries, as well as supercapacitors. We systematically summarize core ML workflows in materials science, spanning data acquisition, feature engineering, and predictive modeling while highlighting AI-driven theoretical simulations, such as ML interatomic potentials. Finally, we outline emerging paradigms and prospects, emphasizing the integration of large language models, active learning to address data scarcity, and the development of autonomous self-driving laboratories for next-generation carbon electrode design. This review also presents challenges and limitations of applying AI-assisted methods to research on carbon materials for EES.

By considering that a spectral and radiation environment similar to space can be obtained at an altitude of 35 km and above, as well as the convenience, economy, and accuracy of solar cell calibration using high-altitude balloons, a high-altitude balloon calibration method for solar cells at 35 km altitude is regarded as the best in-situ calibration method for space solar cells at present. Aiming at the poor consistency in calibration results caused by variations in calibration conditions for each group of data in the actual tests of solar cells using the high-altitude balloon calibration method, this paper analyzed the influence of solar cell temperature, irradiance, solar incidence angle, spectral mismatch, and other factors on high-altitude balloon calibration. A data correction model for this calibration method was proposed and validated using the data obtained from the flight calibration test. Moreover, the uncertainty of the calibration data of solar cells using a high-altitude balloon was analyzed, and a relative expanded uncertainty of 1.4% (k = 2) was achieved for short-circuit current. This paper can provide theoretical support for the subsequent data correction of high-altitude balloon calibration tests for solar cells. Additionally, it verified the feasibility of the high-altitude balloon calibration test proposed by the Chinese Academy of Sciences from the perspective of measurement uncertainty.