Energy management of hybrid energy storage system in electric vehicle using hybrid methodology

Abstract

ABSTRACT Electric vehicles operate in a dynamic environment with constantly changing driving conditions, such as varying speeds, terrains, and traffic patterns. Adapting an energy management (EM) strategy to these conditions to maximise efficiency is a significant challenge. Achieving optimal energy management must also consider the cost implications. This manuscript proposes a hybrid technique for the optimum charging capability of electric vehicles (EVs) with a hybrid energy storage system (HESS), such as an electric vehicle, battery, and supercapacitor (SC). The objective is to maximise energy utilisation efficiency, extend the lifespan of energy storage devices, improve overall vehicle performance, and minimise charging costs. The WSO is incorporated to generate the dataset of possible input parameters of HESS. Using the accomplished WSO dataset, the SNN is trained and forecasts the optimum input parameters of Hybrid ESS. The proposed method is implemented in the MATLAB platform and compared with existing methods. The proposed method supercapacitor power is 4.3 × 104W, proposed battery power is 7 × 104 W, supercapacitor current is 90 A and the proposed battery current is 20 A. The proposed method is higher than other existing methods like RFA, WHO and DCNN methods.