Adaptive Nonlinear Model-Based Fault Diagnosis of Li-ion Batteries

In this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion LiFePO4 cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

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

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-202

Model-Based Adaptive Fault Diagnosis in Lithium Ion Batteries: A Comparison of Linear and Nonlinear Approaches

In this paper, multiple-model adaptive estimation techniques have been successfully applied to fault detection and identification in lithium-ion batteries. The diagnostic performance of a battery depends greatly on the modeling technique used in representing the system and the associated faults under investigation. Here, both linear and non-linear battery modeling techniques are evaluated and the effects of battery model and noise estimation on the over-charge and over-discharge fault diagnosis performance are studied. Based on the experimental data obtained under the same fault scenarios for a single cell, the non-linear model based detection method is found to perform much better in accurately detecting the faults in real time when compared to those using linear model based method

A Lebesgue Sampling based Diagnosis and Prognosis Methodology with Application to Lithium-ion Batteries

Fault diagnosis and prognosis (FDP) plays an important role in the modern complex industrial systems to maintain their reliability, safety, and availability. Diagnosis aims to monitor the fault state of the component or the system in real-time. Prognosis refers to the generation of long-term predictions that describe the evolution of a fault and the estimation of the remaining useful life (RUL) of a failing component or subsystem. Traditional Riemann sampling-based FDP (RS-FDP) takes samples and executes algorithms in periodic time intervals and, in most cases, requires significant computational resources. This makes it difficult or even impossible to implement RS-FDP algorithms on hardware with very limited computational capabilities, such as embedded systems that are widely used in industries. To overcome this bottleneck, this proposal develops a novel Lebesgue sampling-based FDP (LS-FDP), in which FDP algorithms are implemented “as-neede”. Different from RS-FDP, LS-FDP divides the state axis by a number of predefined states (also called Lebesgue states). The computation of LS-based diagnosis is triggered only when the value of measurements changes from one Lebesgue state to another, or “event-triggered”. This method significantly reduces the computation demands by eliminating unnecessary computation. This LS-FDP design is generic and able to accommodate different algorithms, such as Kalman filter and its variations, particle filter, relevant vector machine, etc. This proposal first develops a particle filtering based LS-FDP for li-ion battery applications. To improve the accuracy and precision of the diagnosis and prognosis results, the parameters in the models are treated as time-varying ones and adjusted online by a recursive least square (RLS) method to accommodate the changing of dynamics, operation condition, and environment in the real cases. Uncertainty management is studied in LS-FDP to handle the uncertainties from inaccurate model structure and parameter, measurement noise, process noise, and unknown future loading. The extended Kalman filter implemented in the framework of LS-FDP yields a more efficient LS-EKF algorithm. The proposed method takes full advantage of EKF and Lebesgue sampling to alleviate computation requirements and make it possible to be deployed on most of the distributed FDP systems. All the proposed methods are verified by a study with the estimation of the state of health and RUL prediction of Lithium-ion batteries. The comparisons between traditional RS-FDP methods and LS-FDP show that LS-FDP has a much lower requirement on the computational resource. The proposed parameter adaptation and uncertainty management methods can produce more accurate and precise diagnostic and prognostic results. This research opens a new chapter for FDP method and make it easier to deploy FDP algorithms on the complicate systems build by embedded subsystem and micro-controllers with limited computational resources and communication band width

Detection and Isolation of Small Faults in Lithium-Ion Batteries via the Asymptotic Local Approach

This contribution presents a diagnosis scheme for batteries to detect and isolate internal faults in the form of small parameter changes. This scheme is based on an electrochemical reduced-order model of the battery, which allows the inclusion of physically meaningful faults that might affect the battery performance. The sensitivity properties of the model are analyzed. The model is then used to compute residuals based on an unscented Kalman filter. Primary residuals and a limiting covariance matrix are obtained thanks to the local approach, allowing for fault detection and isolation by chi-squared statistical tests. Results show that faults resulting in limited 0.15% capacity and 0.004% power fade can be effectively detected by the local approach. The algorithm is also able to correctly isolate faults related with sensitive parameters, whereas parameters with low sensitivity or linearly correlated are more difficult to precise.Comment: 8 pages, 2 figures, 3 tables, conferenc