SOC estimation algorithm of power lithium battery based on AFSA‐BP neural network
Qiuxia Wang, Peizhou Wu, Jialing Lian
Abstract
The non‐linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (SOC) more accurately, the artificial fish swarm algorithm‐back propagation (AFSA‐BP) neural network structure was designed based on AFSA and BP neural network theory. According to the test parameters of power lithium battery, the related mathematical model was established. The flow charts of optimising BP neural network with AFSA algorithm and estimating SOC value by AFSA‐BP algorithm are given. The specific implementation steps are elaborated. Using the 48 V, 50 Ah lithium iron phosphate (LiFePO4) power battery as experimental object, through the periodic charging and discharging experiments and software simulation, the correctness, validity and accuracy of the application of AFSA‐BP neural network in estimating SOC value of the power lithium battery are verified.