A lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm

Abstract

The demand for new energy will continue to expand as the environment changes and fossil energy decreases. However, the instability of new energy has slowed down the development of new energy. The joint use of new energy and energy storage modules effectively solves the shortcomings of new energy. The article proposed a lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm. Firstly the life model based on the battery capacity ( C ) , charging current ( I c ) , and discharge current ( I d ) is built. Secondly, the deep learning method is used to improve the step length and speed change of artificial fish-school algorithm. Finally, the simulation platform detects the optimized parameters ( I c , I d , C ) . The simulation results show that optimized parameters can help extend the life of the energy storage module.