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With the widespread use of lithium-ion batteries (LIBs) in electric vehicles, it is essential for battery management systems (BMS) to accurately estimate the state of health (SOH) of the battery. This study constructs a regression estimation model applicable to the SOH of LIBs by combining multifeature signal analysis with a data-driven approach. First, the collected data were smoothed using advanced filtering methods to extract input features characterizing capacity degradation properties from differential thermal voltammetry (DTV) curves, incremental capacity analysis (ICA) curves, meantime decay (MTD) curves, and terminal thermal analysis (TTA) curves. Then, the extracted features are subjected to feature fusion using principal component analysis (PCA) to obtain strongly correlated features with a cumulative contribution of 98% or more as inputs to the data-driven model and the hyperparameters of the group method of data handling (GMDH) network are optimized using simulated annealing genetic algorithm (SAGA). Finally, model construction, validation, and comparison are carried out using NASA and Oxford battery datasets. The root mean square error (RMSE) between the actual measured value and the estimated value is obtained to be less than 0.3%, and the $R^{2}$ value is as high as 0.99. The results demonstrate that the approach can efficiently catch the phenomenon of capacity regeneration. It is valid and robust for SOH estimation for different types of LIBs.

Carbon monoxide (CO) is a harmful gas with significant impacts on human health and the environment. Its timely detection, especially in the event of thermal runaway in automotive lithium batteries, is crucial to prevent casualties. This paper reviews the progress in the development of efficient, sensitive, and reliable CO sensors, focusing on electrochemical, optical, and resistive sensing materials. Low-dimensional materials have a large specific surface area, providing an abundant number of active sites, which has drawn extensive attention from researchers. According to the different sensor signals, we categorized these sensors into electrical and optical signal sensors. We hope that by systematically introducing the sensing mechanism and sensing performance of these two kinds of sensors, appropriate CO sensors can be developed in different application scenarios so as to realize early warning and monitoring to the maximum extent, reduce industrial losses, and ensure the life and health of personnel.