Capacity Estimation of Lithium-Ion Batteries for Various Aging States Through Knowledge Transfer

In this study, we propose a new capacity estimation scheme for various aging states of lithium-ion batteries based on an inverted bottleneck network (IBN) that learns electrochemical knowledge. Most existing capacity estimation schemes that employ simple models have limitations in accuracy because they cannot reflect the complex aging states inside the batteries. An electrochemical model is sufficiently sophisticated to estimate the capacity of lithium-ion batteries accurately; however, it is computationally expensive. Therefore, we transfer the knowledge of an electrochemical model that deals with the physicochemical behavior of the batteries to a small-sized IBN. Then, the proposed neural network that learns both the synthetic and experimental data can estimate the capacity of different aging states accurately with a lower computational time. Furthermore, the specific structure of the IBN allows the neural network to extract visible information, called an attention map, which represents the decision basis of the neural network when estimating capacity. We propose an estimation score for determining the reliability of the capacity estimation result by analyzing the attention map and input voltage data. We measured capacity data from 12 real 37-Ah standard batteries with different aging states to validate the efficacy of the proposed approach. The resulting estimation errors were about 0.445 Ah, which corresponds to only 1.202% errors based on the 37-Ah capacity.