Evaluation of Charging Methods for Lithium-Ion Batteries

Lithium-ion batteries, due to their high energy and power density characteristics, are suitable for applications such as portable electronic devices, renewable energy systems, and electric vehicles. Since the charging method can impact the performance and cycle life of lithium-ion batteries, the dev...

Full description

Bibliographic Details
Main Authors: Guan-Jhu Chen, Wei-Hsin Chung
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/12/19/4095
Description
Summary:Lithium-ion batteries, due to their high energy and power density characteristics, are suitable for applications such as portable electronic devices, renewable energy systems, and electric vehicles. Since the charging method can impact the performance and cycle life of lithium-ion batteries, the development of high-quality charging strategies is essential. Efficient charging strategies need to possess advantages such as high charging efficiency, low battery temperature rise, short charging times, and an extended battery lifespan. The challenges of charging algorithms encompass battery performance variation, temperature management, charging rate control, battery state estimation, and consideration of diverse charging requirements. Effective charging algorithms must strike a balance within these challenging conditions to ensure the battery’s longevity, high efficiency, and safety. This paper introduces and investigates five charging methods for implementation. These five charging methods include three different constant current–constant voltage charging methods with different cut-off voltage values, the constant loss–constant voltage charging method, and the constant power–constant voltage charging method. This paper will implement and compare the performance of the aforementioned five charging methods, including charging efficiency, battery temperature rise, charging time, and cycle life count, providing experimental data to enable users to choose a charging method more efficiently.
ISSN:2079-9292