TY  - EJOU
AU  - Wang, Qi 
AU  - Gu, Yandong 
AU  - Zhu, Tao 
AU  - Ge, Lantian 
AU  - Huang, Yibo 

TI  - SOH Estimation of Lithium Batteries Based on ICA and WOA-RBF Algorithm
T2  - Energy Engineering

PY  - 2024
VL  - 121
IS  - 11
SN  - 1546-0118

AB  - Accurately estimating the State of Health (SOH) of batteries is of great significance for the stable operation and safety of lithium batteries. This article proposes a method based on the combination of Capacity Incremental Curve Analysis (ICA) and Whale Optimization Algorithm-Radial Basis Function (WOA-RBF) neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries. Firstly, preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage (Q-V) curve, convert the Q-V curve into an IC curve and denoise it, analyze the parameters in the IC curve that may serve as health features; Then, extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features, and perform correlation analysis using Pearson correlation coefficient method; Finally, the WOA-RBF algorithm was used to estimate the battery SOH, and the training results of LSTM, RBF, and PSO-RBF algorithms were compared. The conclusion was drawn that the WOA-RBF algorithm has high accuracy, fast convergence speed, and the best linearity in estimating SOH. The absolute error of its SOH estimation can be controlled within 1%, and the relative error can be controlled within 2%.
KW  - Lithium-ion batteries; ICA; WOA; RBF; SOH estimation

DO  - 10.32604/ee.2024.053758