Abstract
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.
Highlights
The lithium-ion battery is becoming the backbone of most of the popular energy storage systems worldwide [1]
Modeling of Lithium-ion batteries could be divided into two main categories: 1) the first category is Electrochemical model that describes the electrochemical reaction occurring in the battery [7], and 2) the second category is its electronic equivalent circuit that is based on the characteristics of the lithium-ion battery and can be branched into Rint model, PNGV model, Thevenin model, RC first-order transient model and RC second-order transient that is called Dual Polarization (DP) model [9]–[13]
Multi-stage fast charging methodologies have been implemented on the polymer lithium-ion battery to reach full capacity (SOCλ = 100%) as illustrated in Fig. 6, which can be categorized into two main scenarios: the first scenario is the standard constant current-constant voltage (CCCV) methodology and the second scenario is Multi-Stage Charging Current methodology (MSCC) based on Cuckoo Optimization Algorithm (COA)
Summary
The lithium-ion battery is becoming the backbone of most of the popular energy storage systems worldwide [1]. Many algorithms and techniques have been implemented for multi-stage constant current charging of the lithium-ion battery in Table 1 such as Particle Swarm Optimization (PSO) based Fuzzy Logic, Consecutive Orthogonal Arrays, Correcting Slope Iteratively, Taguchi Approach, Ant Colony algorithm (ACA), Optimal charge pattern (OCP), Balance of Internal Consumption and Charging Speed, Particle Swarm Optimization (PSO), Negative pulse, Boost-charging, and Dynamic programming algorithm. The analysis of several techniques based on the Cuckoo optimization algorithm (COA) to optimize the total energy consumption and battery charging interval time to reach the full capacity limit is obtained. The RC second-order transient model consists of three main sectors [25]–[28]: open circuit voltage OCV , which depends on the battery state of charge, internal resistances including the ohmic internal resistance (Ri), the electrochemical polarization internal resistance (Rα) and the concentration polarization internal resistance (Rβ ) and lastly, the internal capacitances such as the electrochemical polarization capacitance (Cα) and the concentration polarization capacitance (Cβ ).
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