Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

TL;DR

This survey analyzes model-based fault diagnosis for lithium-ion batteries in battery management systems, detailing physics-based electrochemical models (EMs) and electrical equivalent circuit models (ECMs) as foundational frameworks. It presents a general state-space representation that augments states with fault signals, and reviews online and offline state observers, along with fault detection, identification, and estimation methods. The discussion covers modeling uncertainties, aging effects, and measurement outliers, and highlights practical challenges such as pack balancing, adaptive thresholds, multi-source fault identification, and cloud-based BMS. The work provides a structured, method-focused view to guide robust, timely fault diagnosis in LIB systems and points to avenues for future research and deployment.

Abstract

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods in advanced BMSs. This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs. First, the widely explored battery models in the existing literature are classified into physics-based electrochemical models and electrical equivalent circuit models. Second, a general state-space representation that describes electrical dynamics of a faulty battery is presented. The formulation of the state vectors and the identification of the parameter matrices are then elaborated. Third, the fault mechanisms of both battery faults (incl. overcharege/overdischarge faults, connection faults, short circuit faults) and sensor faults (incl. voltage sensor faults and current sensor faults) are discussed. Furthermore, different types of modeling uncertainties, such as modeling errors and measurement noises, aging effects, measurement outliers, are elaborated. An emphasis is then placed on the observer design (incl. online state observers and offline state observers). The algorithm implementation of typical state observers for battery fault diagnosis is also put forward. Finally, discussion and outlook are offered to envision some possible future research directions.

Figures (8)

• Figure 1: Battery models applied for fault diagnosis (from left to right: (a) physics-based EM; (b) FOM; (c) IOM)
• Figure 2: HPPC test: (a) Current profile; (b) Voltage response
• Figure 3: Battery faults: (a) overcharge; (b) overdischarge; (c) SC fault; (d) connecting fault
• Figure 4: Illustration of pointwise KF estimates and ellipsoidal set-valued estimates of the unknown true system state $x_k = [x_k^1, x_k^2]^T$
• Figure 5: Structure of the fault diagnosis procedure