A Deep Learning Approach for State of Health Estimation of Lithium-Ion Batteries Based on Differential Thermal Voltammetry

Accurately estimating the actual capacity of the battery is crucial for stable battery operation and user safety. This paper combined feature extraction through differential thermal voltammetry analysis and long short-term memory for accurate state of health estimation. First, differential thermal voltammetry curves according to battery degradation were extracted for various cathode materials. Then, health indicators are collected from the differential thermal voltammetry curve for deep learning-based state of health estimation. In particular, to improve the state of health estimation performance, the integral value of the differential thermal voltammetry curve through the specific voltage ranges was additionally introduced along with the peak and valley. Second, the correlation between the extracted health indicators and capacity was analyzed using Pearson correlation analysis. Finally, a framework was developed to estimate the state of health of the battery using high-quality health indicators as inputs to the long short-term memory model. The state of health estimation performance of the proposed algorithm, which reflected the integral value of the differential thermal voltammetry curve, was compared with the case that did not reflect. From the result, the mean absolute error decreased by 11.6% and the root mean square error by 10.01% for the two battery data sets.