Overview of model- and non-model-based online battery management systems for electric vehicle applications: A comprehensive review of experimental and simulation studies
Document Type
Article
Publication Date
12-1-2022
Abstract
The online battery management system (BMS) is very critical for the safe and reliable operation of electric vehicles (EVs) and renewable energy storage applications. The primary responsibility of BMS is data assembly, state monitoring, state management, state safety, charging control, thermal management, and information management. The algorithm and control development for smooth and cost-effective functioning of online BMS is challenging research. The complexity, stability, cost, robustness, computational cost, and accuracy of BMS for Li-ion batteries (LiBs) can be enhanced through the development of algorithms. The model-based and non-model-based data-driven methods are the most suitable for developing algorithms and control for online BMS than other methods present in the literatures. The performance analysis of algorithms under different current, thermal, and load conditions have been investigated. The objective of this review is to advance the experimental design and control for online BMS. The comprehensive overview of present techniques, core issues, technical challenges, emerging trends, and future research opportunities for next-generation BMS is covered in this paper with experimental and simulation analysis.
Keywords
lithium-ion battery, battery management system (BMS), electrical vehicle (EV), battery charging, battery modeling, states estimation and fault diagnosis
Divisions
fsktm,sch_ecs
Publication Title
Sustainability
Volume
14
Issue
23
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND