Fractional-Order Observer-Based Current Sensor Fault Diagnosis for Lithium-Ion Battery Management System

• Tipo: Artigo em Revista Científica Internacional
• Data: 2026

Autores

Chen, LP (Autor) Outra; Zu, ZX (Autor) Outra; Ma, HL (Autor) Outra; António Mendes Lopes (Autor) FEUP; Chen, YQ (Autor) Outra

Revista

Título: IEEE Transactions on Automation Science and Engineering

Vol. 23

Páginas: 1272-1283

ISSN: 1545-5955

Editora: IEEE

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-01A-WJQ

DOI: 10.1109/tase.2025.3647502

Abstract (EN): The battery management system (BMS) is the cornerstone of the safe operation of electric vehicles (EVs), and its stable operation relies heavily on the accuracy of the battery current sensor. In this paper, a novel method for current sensor fault diagnosis in lithium-ion batteries is presented. First, a fractional-order (FO) battery model is proposed, and a hybrid particle swarm optimization algorithm with a cross-learning strategy is developed for model parameter identification. Second, a FO observer is designed for state of charge (SOC) estimation, and the stability of the observation error system is verified. Finally, by accurately estimating the change in SOC caused by a fault current and employing residual analysis, current sensor faults are identified. Experimental analysis, carried out under different operating conditions, reveals that the proposed method detects current sensor faults more accurately and quickly than the general open-circuit voltage method. Moreover, it can adapt to varying operating conditions and temperatures.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12