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

Computational study of self-heating ignition of Lithium-ion batteries during storage: effects of heat transfer and multi-step kinetics

Fire safety is a serious concern when storing a large number of Lithium-ion batteries (LIBs) stacked as an ensemble. Many such fires reported in recent years have caused severe damage to industrial facilities, public property, and even loss of life. It is crucial to understand the mechanisms and causes of these storage fires to provide insights for prevention. While previous studies mostly focused on the chemistry of LIBs and ignition while charging or discharging, this thesis explores the possibility of another fundamental cause of such fires driven by heat transfer, self-heating ignition. Three major challenges are identified for the modelling of self-heating ignition of LIB ensembles: large sizes, multi-dimensional heat transfer, and multiple chemical reactions. In this thesis, a typical LiCoO2 (LCO) battery with four-step reaction kinetics is chosen for analysis and modelling the fundamentals of self-heating ignition. Four numerical models based on COMSOL Multiphysics are developed to deal with these challenges. The numerical results show that the critical ambient temperature triggering self-heating ignition decreases significantly with the size of the battery ensemble, from 155℃ for a single cell to 45℃ for a rack of cells. The spacing and packaging materials used to separate LIBs in storage can promote self-heating ignition further decreasing the critical temperature. The increase in size and the presence of packaging materials result in slower internal heat transfer, which allows the cells to self-ignite at lower ambient temperatures. The heat from self-discharge, which is often neglected in the literature, is predicted to have minor effects on small LIB ensembles but to be dominating for a shelf of LIBs, indicating a substantial change in important chemical mechanism for different sizes. The differences resulting from different numerical models are investigated by a benchmarking analysis using two simulation tools: COMSOL and Gpyro. This thesis provides insights on the fundamental mechanism of self-heating ignition of LIBs during open-circuit storage and scientifically proves that self-heating ignition can be a cause of fires when LIBs are stacked to large sizes.Open Acces

Detection of Lithium-ion Battery Failure and Thermal Runaway

Li-ion battery failure and thermal runaway are serious safety concerns for electric vehicles and energy storage devices. For electric vehicle accidents in recent years, battery thermal runaway events have occurred under unpredictable circumstances, including when vehicles are at rest, not actively being charged or driven. The immediate detection of battery failure within seconds is highly important since the hazard conditions from a single cell thermal runaway can propagate to neighboring cells and the whole system. From a regulation perspective, the proposed global technical regulation No. 20 from the United Nations on Electric Vehicle Safety requires a five-minute advanced warning prior to hazardous conditions caused by a thermal runaway event. To achieve this detection goal for thermal runaway, a robust and sensitive detection methodology is required. The existing methods for fault detection and diagnosis in the battery pack utilize temperature, voltage, and current measurements. For an automotive battery pack with cells connected in parallel, the current measurements for individual cells are not available, so detection methods relying on individual cell current will not work. Due to the parallel connection of cells, the methods using voltage cannot effectively detect a single cell failure due to a low signal-to-noise ratio. Temperature-based detection methods, due to the sparse temperature measurements in a large pack, are slow in fault detection, with detection speeds usually on the scale of minutes or hours depending on sensor and fault locations. Fast and high confidence fault detection methods are needed to enable a more effective battery management system that can quickly alert and guide emergency response. Most thermal runaway events are associated with battery internal short circuit (ISC), so ISC will be the focus of this dissertation's study to better understand the cause and the evolution of battery failure. A model of the battery ISC event that predicts temperature, gas generation, and the resulting cell swelling in the early stage of ISC evolution is developed. By monitoring the battery expansion force and adopting an adaptive threshold, an ISC event can be identified before cell venting. Furthermore, by reviewing literature about the composition of the gas expelled from the battery during a venting event in different battery chemistries and states-of-charge, we identify CO2 as the ideal target gas species for gas detection. Based on the cell swelling and gas release in battery failure, the dissertation presents fault detection methods using expansion force measurements to capture the abnormal force increase due to battery swelling and Non-Dispersive Infrared (NDIR) CO2 sensor to detect venting events from battery failure. By adopting the proposed fault detection method using expansion force and gas sensing, fault detection for a parallel-connected battery module achieves a high signal-to-noise ratio. At the same time, high confidence detection of ISC events can be achieved in seconds, and the methodology can be extended to large battery packs in electric vehicles and stationary energy storage systems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169734/1/tingcai_1.pd