Work

Comparative analysis of predictive models for SOC estimation in EV under different running conditions

Year: 2023

Type: article

Abstract: Electric vehicles have been introduced to address environmental concerns related to excessive burning of fuels in the automobile industry. The key component solely responsible for its preferences over other modes is the battery. The Charging and discharging states under different running model of vehicle is directly related to the health of battery and thereby the degree of hybridization of the series-parallel hybrid electric vehicle (SP-HEV) considered in this research. In this study, the state of charge of a series parallel hybrid electric vehicle was continuously modeled under different driving conditions, and two models were used to predict the charging states of the battery. A swarm-based prediction model resulted in a prediction error of 0.9114, while a modified differential evolutionary method reduced the error to 0.6672, indicating its effectiveness in estimating battery charge conditions. The prediction of battery SOC will enhance the power efficiency of the vehicle along with its SOH.

Source: e-Prime - Advances in Electrical Engineering Electronics and Energy

DOI: 10.1016/j.prime.2023.100207

Authors: Nand Lal Shah, Anil Kumar Kurchania

Institution: AISECT University

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,400