Year: 2024
Type: conference-paper
Abstract: Due to the increasing degree of substation automation and intelligence and the promotion of unattended operation, the role of batteries is becoming more and more important. Battery life assessment methods usually rely on physical models and experimental data. However, due to the complexity and high cost of the experimental process, as well as the complexity of the model and the limitations of the hypothesis, these methods are often limited. By collecting a large number of battery operation data, machine learning and statistical analysis techniques are used to construct a prediction model, so as to more accurately evaluate the battery life and improve the evaluation efficiency.
DOI: 10.1117/12.3044866
Authors: Ziming Fan, Jun Yang, Hua Xun
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402888912