Energy management of hybrid energy storage system in electric vehicle based on hybrid SCSO-RERNN approach

Abstract

In this manuscript, a hybrid technique is proposed for the energy management (EM) of hybrid energy storage systems (HESS) in electric vehicles (EVs). The proposed technique, named SCSO-RERNN combines the Sand cat swarm optimization (SCSO) and recalling enhanced recurrent neural network (RERNN) to optimize the energy allocation and control strategy of HESS in EVs. The objective of the proposed system is to regulate the direct current (DC) bus voltage and track the battery and super-capacitor (SC) with desired references under various load conditions. The SCSO-RERNN technique optimizes the SC reference voltage, SC voltage, battery current magnitude, fuel cell (FC) voltage, and battery power and current variations. By utilizing this hybrid energy management unit, optimal EM of HESS in EVs is achieved. The proposed approach significantly reduces computation time and algorithm complexity while maintaining dc-bus voltage regulation and power balance. The performance of the Hybrid ESS is measured by comparing it with existing approaches. The result shows that the proposed hybrid method shows best solution with the least amount of computing time. The execution of the proposed method is carried out in MATLAB, and further it is validating its effectiveness and feasibility.