Experimental and Theoretical Analysis of the Fast Charging Polymer Lithium-Ion Battery Based on Cuckoo Optimization Algorithm (COA)

Fast charging of the electric-vehicles is one of the paramount challenges in solar smart cities. This paper investigates intelligent optimization methodology to improvise the existing approaches in order to speed up the charging process whilst reducing the energy consumption without degradation in the light of the outrageous demand for lithium-ion battery in the electric vehicles (EVs). Two fitness functions are combined as the targeted objective function: energy losses (EL) and charging interval time (CIT). An intelligent optimization methodology based on Cuckoo Optimization Algorithm (COA) is implemented to the objective function for improving the charging performance of the lithium-ion battery. COA is applied through two main techniques: The Hierarchical technique (HT) and the Conditional random technique (CRT). The experimental results show that the proposed techniques permit a full charging capacity of the polymer lithium-ion battery (0 to 100% SOC) within 91 mins. Compared with the constant current-constant voltage (CCCV) technique, an improvement in the efficiency of 8% and 14.1% was obtained by the Hierarchical technique (HT) and the Conditional random technique (CRT) respectively, in addition to a reduction in energy losses of 7.783% and 10.408% respectively and a reduction in charging interval time of 18.1% and 22.45% respectively. Experimental and theoretical analyses are performed and are in good agreement on the polymer lithium-ion battery fast charging method.