Remaining capacity prediction of Li-ion batteries based on ultrasonic signals

Abstract

ABSTRACT The estimation of Li-ion battery degradation performance is the key to the safe and effective operation. The current estimation methods for Li-ion battery degradation performance are mainly based on indirect electrical characteristic parameters such as voltage and current, which lack the direct characterization of internal materials, which will affect the accuracy and real-time performance of the prediction. Ultrasonic wave can directly characterize the changes in internal material properties of Li-ion battery during charging-discharging cycles. At present, the correlation between ultrasonic signal and Li-ion battery degradation process has been proven, and the relevant ultrasonic characteristics have been analyzed and established. On the above basis, this paper designs a battery degradation performance detection platform based on ultrasonic, which obtains the ultrasonic response signal and further selects four characteristics of battery aging. Aiming at the situation that the prediction starting point cannot be determined due to the possible loss of process data during the experiment, this paper uses random sampling to establish a test set to simulate the unknown prediction starting point, and establishes a battery remaining capacity prediction method based on the combination of the main model and residual correction model. Finally, the experiment proves that the proposed model has high accuracy.