A random forest model that correlates the slope of the OCV-SOC curve and internal resistance to ISC was developed in Ref. to determine the SOC depletion and estimate the ISC severity. The open-circuit voltage (OCV) was utilized in Ref. , electrochemical impedance spectroscopy was applied to infer ISC of different types.

For example, a recursive least squares algorithm was adopted to identify ISC based on abnormal state of charge (SOC) depletion and heat generation in Ref. As a result, battery energy will be depleted through the short circuit and dissipated as heat, which imposes safety threats by overheating the battery .ĭifferent approaches have been proposed in the literature for ISC detection of single battery cells. Typically, an ISC can be described as the penetration of the separator by a lithium dendrite, leading to an electrical connection (i.e., short circuit) between the high-potential and low-potential components of the battery . Various abuse conditions, such as mechanical (e.g., collision and puncture), electrical (e.g., overcharge/discharge), and thermal (e.g., high-temperature heating) abuses, can result in ISC . Among those faults, detecting internal short circuits (ISCs) of batteries is of significant importance, as ISC is one of the major causes of battery thermal runaway. For safe operation, reliable and timely detection of various faults associated with batteries is necessary . Lithium-ion batteries have been widely used in commercial products such as electric vehicles and smartphones, thanks to their relatively high energy density and long service life. The algorithm is evaluated in the presence of signal noises in terms of accuracy, false alarm rate, and missed detection rate, verifying the effectiveness and robustness of the proposed approach. Based on the estimated ISC resistance from CNN, the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery. A convolutional neural network (CNN) is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs. By analyzing the data generated from the electro-thermal model, the distribution of surface temperature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations. To characterize ISC-related signatures in battery string responses, an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC. This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation (i.e., no independent current sensors for individual cells in a parallel string). Reliable and timely detection of an internal short circuit (ISC) in lithium-ion batteries is important to ensure safe and efficient operation.

Battery status in parallel connection