Work

Battery Prognostics and Health Management: AI and Big Data

Year: 2024

Type: article

Abstract: In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj16010010

Authors: Di Li, Jinrui Nan, Andrew F. Burke, Jingyuan Zhao

Institutions: Hubei University of Arts and Science, Beijing Institute of Technology, University of California, Davis

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $1,591