A Review of Artificial Intelligence-Based Prognostic
Through this comprehensive review, the paper underscores the significant advancements made in the past decade concerning AI
Accurate and reliable battery data acquisition is a crucial step in developing data-driven li-ion battery PHM systems. However, obtaining comprehensive battery data is both time-consuming and resource-intensive.
The state-of-health (SOH) is the present health divided by the initial health of an energy storage device . Health is measured differently in different technologies, but energy capacity is the most commonly used proxy parameter. At some critical SOH, the battery becomes unusable or unreliable for given applications and should be replaced.
PHM provides critical insights into battery parameters, including charge, health, safety, and remaining life [10, 11, 12, 13]. The academic community has extensively studied these aspects of battery behavior, providing detailed mechanisms and comprehensive analyses.
The integration of battery PHM, in particular, raises significant concerns related to data privacy and security. These systems, which rely on continuous data collection and analysis, must protect sensitive information while ensuring operational integrity and system functionality.
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