KEYWORDS: Batteries, Data modeling, Feature extraction, Feature fusion, Education and training, Model based design, Information fusion, Machine learning, Safety, Neural networks
To ensure the safety, efficiency, and stability of batteries, accurate estimation of battery health status is crucial. To address this, this paper proposes a method for estimating battery state of health by integrating interval local information. Firstly, the full-cycle discharge data of the battery are divided into voltage intervals, and the time differences within each voltage interval segment are extracted as features to characterize the battery's health status. Then, the features within each voltage interval segment are fused. Finally, a Long Short-Term Memory neural network algorithm is employed to construct a battery health status prediction model, achieving accurate estimation of the battery health status. Experimental results demonstrate that the proposed method can accurately estimate the health status of individual cells and battery modules, with R-squared values (coefficient of determination) exceeding 0.99 and root mean square errors within 1%.
KEYWORDS: Batteries, Ultrasonics, Lithium, Data modeling, Ultrasonography, Medium wave, Signal processing, Acoustics, Nondestructive evaluation, Electrodes
Due to the complex physicochemical properties of lithium-ion batteries, it is difficult to identify the internal changes that cause battery degradation and failure. Ultrasonic testing, as a non-destructive characterization method, has the advantages of high sensitivity, low cost, convenient use, and fast speed, and has great potential for application in battery characterization. Nowadays, the application of ultrasonic waves for state characterization of lithium-ion batteries has achieved initial success, but research on building capacity degradation models for lithium-ion batteries using ultrasonic testing is still limited. In this paper, a capacity degradation model for lithium-ion batteries is proposed based on ultrasonic non-destructive testing technology. The established capacity degradation model is used for extrapolation to reach the failure threshold, providing theoretical support for data-driven lithium-ion battery life prediction and health management.
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