Abstract:

 [Objectives] With the widespread application of energy storage devices such as batteries and super capacitors globally, real-time online monitoring of their performance has become increasingly critical, and the importance of battery sensing systems is also becoming more evident. Traditional sensors are easily affected by electromagnetic interference, while optical sensors have the advantages of reducing electromagnetic interference, small size, and light weight, which can significantly improve the accuracy of estimating SOC and SOH. [Methods] The working principles and application examples of fiber evanescent wave sensors, fiber Bragg grating sensors, and fiber local surface plasmon resonance sensors are introduced in detail. In addition, it also explores how to use advanced data processing technologies and algorithms to extract valuable information from a large amount of raw data to further optimize battery performance, predict failures and improve overall system efficiency. [Results] By applying advanced data analysis technologies, such as feature selection, pattern recognition, and predictive modeling, it is possible to effectively improve the understanding of battery performance and identify potential problems in advance, thereby enhancing the overall performance and safety of the system. [Conclusions]. Future research should focus on further improving the performance of the sensor itself, including sensitivity, stability and cost-effectiveness; at the same time, it should also strengthen the development of data processing algorithms to better adapt to rapidly changing market needs.

Key words:

text-align:justify, "> batter, fiber sensing technology, battery management system, state of charge (SOC), state of health(SOH), fiber bragg grating, data processing technology