Work

A Brief Overview of Modeling Estimation of State of Health for an Electric Vehicle’s Li-Ion Batteries

Year: 2025

Type: article

Abstract: The current literature highlights several state-of-health (SOH) prediction models for lithium-ion (Li-ion) batteries used in electric vehicles (EVs). However, a thorough comparative analysis remains absent. This study addresses this gap by conducting a comprehensive evaluation of SOH prediction methods for Li-ion batteries in EV applications, encompassing direct measurement techniques, physics-based approaches, and data-driven methodologies. The analysis identifies the strengths, limitations, and applicability of each modeling method. Additionally, this study explores key indicators of SOH, influential variables affecting battery health, and publicly available datasets that support SOH modeling. By synthesizing these insights, the research provides recommendations for improving existing models and outlines prospective directions for enhancing the accuracy and efficiency of SOH estimation in EV applications. This work aims to contribute to the development of robust, accurate, and practical SOH models, thereby advancing the reliability and sustainability of Li-ion battery systems in the growing EV industry.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj16020073

Authors: Ehab H. E. Bayoumi, Michele De Santis, Hilmy Awad

Institutions: University of Botswana, University of Campania "Luigi Vanvitelli", Helwan University

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,689