A lithium-ion batteries SOH estimation method based on extracting new features during the constant voltage charging stage and improving BPNN.

Existing state of health (SOH) estimation methods for lithium-ion batteries predominantly extract health features (HF) from constant current (CC) and constant voltage (CV) charging phases. Nevertheless, CC charging phase feature extraction is susceptible to the randomness of the initial charging stage. By contrast, data during the constant voltage (CV) charging stage are preserved intact. The complexity and noise interference of battery data make it difficult to accurately extract health features, and it is necessary to develop effective methods to process the data and extract representative features. In response to this issue, this paper proposes an SOH estimation method for extracting HF at the end of the CV charging stage and optimizes the Backpropagation Neural Network (BPNN). Firstly, the current curve during the CV charging stage was transformed into the differential current curve (dQ/dI curve), from which two HFs were extracted. Secondly, addressing the issue of weight and threshold initialization in BPNN, the Coati Optimization Algorithm (COA) was employed to optimize the network (COA-BPNN). Finally, validation was conducted using two publicly available datasets. The experimental results demonstrate that the proposed method exhibits high accuracy in estimating the SOH of batteries under various environmental temperatures and charging rate conditions. Compared with the traditional BPNN method, the COA-BPNN method reduces the maximum root mean square error and average absolute error of the estimated results to 0.22% and 0.16%, respectively.