Inconsistency identification for battery energy storage systems based on clustering-embedded low-rank representation

Key Points

• Inconsistency identification shows enhanced accuracy for battery energy storage systems.
• The method achieved a significant accuracy improvement of 15% over conventional techniques.
• Analysis employed a clustering-embedded low-rank representation approach for data evaluation.
• This approach may highlight the need for better robustness in battery performance monitoring.