• Energy Research

Sensor fault diagnosis is of great significance to ensure safe battery operation. This paper proposes a novel sensor fault diagnosis method that achieves the simultaneous fault detection, fault source and type identification, and fault estimation in a comprehensive way. Specifically, a set-valued observer, featuring a state predictor and a state estimator, is first constructed and designed to guarantee the inclusion of the unavailable actual battery state due to unknown modeling errors and noises at every instant of time. Compared with the traditional observers, a distinct feature of the proposed one lies in that the calculated state predictions and estimations of the battery system at each time step are ellipsoidal sets in state space rather than single vectors. The boundedness of state prediction and estimation errors is formally proved, and the tractable design criteria for determining the real-time optimal prediction and estimation ellipsoids are also derived. As for diagnosis algorithm, fault detection is implemented based on the intersection between the prediction and estimation ellipsoids. Then, a two-layer Pearson correlation coefficient analysis mechanism is developed to identify the source and type of sensor faults. Another set-valued observer based on an augmented battery model is further designed to estimate the fault level. Finally, experimental studies of a battery cell under different sensor fault sources, types and values are elaborated to verify the effectiveness of the proposed method.

In electric vehicle (EV) applications, constant current constant voltage (CCCV) charging has been widely used for battery charging. Based on the current analysis in constant voltage (CV) charging phase, this article proposes a novel soft short-circuit (SC) fault diagnosis algorithm that achieves simultaneous fault detection and estimation for EV batteries. The proposed algorithm can accurately estimate SC resistance with the limited CV charging data under unknown battery model parameters. It consists of two parts: online parameter identification during the discharging phase and SC fault estimation during the CV charging phase. Specifically, a set-valued ellipsoidal observer is designed to guarantee the inclusion of the actual battery parameters in the equivalent circuit model (ECM) from the EV operation data at every instant of time. Then, the current model during the CV charging phase is established to iteratively update the SC resistance until the absolute value of the error between the estimated current and measured current is smaller than the predefined threshold. Finally, experimental studies of various types of batteries are conducted under different SC resistances to verify the effectiveness of the proposed algorithm.

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. Comment: Submitted to Renewable and Sustainable Energy Reviews on 09-Jan-2024