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As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery's state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. [ABSTRACT FROM AUTHOR]

Combining spectral and morphological information to classify hyperspectral data offers a considerable advantage over methods based solely on spectral or morphological data. Previously, a classification method was proposed that concatenated extended morphological profile (EMP) and spectral information into one feature vector for each pixel of an image. Although this method has the merit of simultaneously using spectral and morphological information, it runs the risk of generating new spectral constituents (not present in the original image). In this letter, an improved classification method based on fusing extended multivariate morphological profile (EMMP) and spectral information was proposed. A new vector ordering method based on spectral purity-based criterion was adopted to overcome the problem of generating new constituents in EMP. A feature selection algorithm was employed to improve the efficiency of EMMP. Experiments were carried out on a hyperspectral data set collected by NASA's Airborne Visible-Infrared Imaging Spectrometer sensor. Experimental results showed that EMMP was effective at describing morphological information in hyperspectral data, and that this letter's method was superior to the previous method in terms of classification accuracy but inferior to the previous method in terms of time consumption. [ABSTRACT FROM AUTHOR]