Summary Lithium ion cells, when cycled, exhibit a two‐stage degradation behavior characterized by a first linear stage and a second nonlinear stage where degradation is rapid. The multitude of degradation phenomena occurring in lithium ion batteries complicates the understanding of this two‐stage degradation behavior. In this work, a simple and intuitive model is presented to analyze the coupled effect of resistance growth and the shape of the state of charge (SOC)‐open circuit voltage (OCV) relationship in representing the complete degradation behavior. The model simulations demonstrate that a single resistance that increases linearly on cycling can capture the transition from slow to fast degradation, primarily due to the shape of the SOC‐OCV curve. Further, the model simulations indicate that the shape of the degradation curve depends strongly on the magnitude of current at the end of discharge of the cycling protocol. To verify these observations, specific experiments are designed with minimal capacity loss but with shrinking operating voltage ranges that result in shrinking operating OCV range. The results of the experiments validate the observations of model simulations. Further, long‐term cycling experiment with a commercial lithium ion cell shows that the operating OCV range shrinks substantially with aging and is a major reason for the observed accelerated degradation. The analysis of the present work provides significant insights towards developing simple semiempirical models suitable for battery life management in microcontrollers.

With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. internal short circuit is a very critical issue that is often ascribed to be a cause of many accidents involving Li-ion batteries. A novel method that can detect the internal short circuit in real time based on an advanced machine leaning approach, is proposed. Based on an equivalent electric circuit model, a set of features encompassing the physics of Li-ion cell with short circuit fault are identified and extracted from each charge-discharge cycle. The training feature set is generated with and without an external short-circuit resistance across the battery terminals. To emulate a real user scenario, internal short is induced by mechanical abuse. the testing feature set is generated from the battery charge-discharge data before and after the abuse. A random forest classifier is trained with the training feature set. the fault detection accuracy for the testing dataset is found to be more than 97%. The proposed algorithm does not interfere with the normal usage of the device, and the trained model can be implemented in any device for online fault detection.Nowadays, smart phones, electric vehicles and most of consumer electronics use Li-ion batteries (LiBs) due to their high energy density, long cycle life and extended calendar life. Due to the wide spread applicability, LiBs are subjected to mechanical abuse of varying intensities. As illustrated in Fig. 1, mechanical abuse to the battery may lead to internal short circuit (ISC) due to the damage of the insulating separator, deflection of the electrodes, etc. 1 . Such ISC causes internal heating and further damage of the battery, that may cause smoke, fire or an explosion. Thermal runaway of the battery is a serious threat to user safety.The effects of the mechanical abuse on the LiB and the mechanisms of thermal runaway have been studied extensively in the literature by modelling and experiments. The models have been developed by combining the mechanical, electrochemical, and thermal behaviour of the LiBs under various types of mechanical abuses 2-9 . Most of the reported models have been validated with experimental data. The dynamic and quasi-static mechanical abuse tests studied by several researchers are indentation test 4,10,11 , punch test 12 , nail penetration test 8 , pinch-torsion test 13 , compressive test [14][15][16] , drop test or impact test 17 , etc. The mechanism of ISC development from the pinch and pinch-torsion types of mechanical abuse has been modelled in 18 and stated that pinch-torsion is more effective than the pure pinch in puncturing the separator and creating ISC. Fracturing of the separator due to the ground impact of an electric vehicle battery which leads to ISC formation has been modelled using global finite element 19 . The crush test has been performed 20 on the whole battery pack of four cells and the short circuit current has been measured. The short circuit resistance has been esti...

This work describes the architecture for developing physics of failure models, derived as a function of machine sensor data, and integrating with data pertaining to other relevant factors like geography, manufacturing, environment, customer and inspection information, that are not easily modeled using physics principles. The mechanics of the system is characterized using surrogate models for stress and metal temperature based on results from multiple non-linear finite element simulations. A cumulative damage index measure has been formulated that quantifies the health of the component. To address deficiencies in the simulation results, a model tuning framework is designed to improve the accuracy of the model. Despite the model tuning, un-modelled sources of variation can lead to insufficient model accuracy. It is required to incorporate these un-modelled effects so as to improve the model performance. A novel machine learning based model fusion approach has been presented that can combine physics model predictions with other data sources that are difficult to incorporate in a physics framework. This approach has been applied to a gas turbine hot section turbine blade failure prediction example.

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