An Adaptive Optimization Algorithm in LSTM for SOC Estimation Based on Improved Borges Derivative

This article presents an optimization algorithm in the long short-term memory (LSTM) network for state-of-charge (SOC) estimation based on the fractal derivative. The improved Borges derivative as a fractal derivative is introduced to the parameter optimization in LSTM networks, and the integer-order derivatives in the adaptive momentum estimation (Adam) algorithm are generalized to the improved Borges derivatives. To take advantage of the orders, we analyze the relative speed of the improved Borges derivative. Meanwhile, a tuning method for the orders is presented to flexibly adjust the training speed of parameters. The Adam algorithm with the improved Borges derivative (Adam-IB) is designed to estimate the SOC of lithium-ion batteries, and the training speed for SOC estimation is flexibly adjusted via the Adam-IB algorithm. The experiments are carried out under different working conditions. Compared with the Adam algorithm, the Adam-IB algorithm is obviously satisfactory to estimate the SOC.