Detection of Lithium-ion Battery Failure and Thermal Runaway
Cai, Ting
2021
Abstract
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.Deep Blue DOI
Subjects
Lithium-ion Battery Battery Safety Thermal Runaway Fault Detection
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