Work

Discharging Stage Prediction of Lithium Ion Battery Using CNN - DenseNet Architecture

Year: 2024

Type: conference-paper

Source: International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

DOI: 10.1109/icimcis63449.2024.10957422

Authors: Mukhidin, Akhmad Fauzan Ali, Muhammad Abdillah, Hayati Yassin, Erly Krisnanik, Wahyu Caesarendra

Institutions: Universiti Brunei Darussalam, Universitas Pertamina, Universitas Pembangunan Nasional Veteran Jakarta, Opole University of Technology

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: closed

OpenAlex ID: W4409573662

FWCI:

Cites: 15

Cited by:

Related to: 10