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.

Fluorinated amorphous carbon thin films (a-C:F) are deposited using inductively coupled plasma chemical vapor deposition with various flow-rate ratios of CH 4 :CF 4 gases for ultralarge-scale integrated intermetal dielectric applications. The accurate composition of the thin films are quantitatively analyzed using elastic recoil detection-time of flight. The incorporation of fluorine is saturated at about 25 atom % by increasing the CF 4 flow rate. The dielectric constant decreases to 2.4 and the refractive index of the film is reduced to 1.35 as the CF 4 flow rate increases. Also, it is observed that the C-F bonding configuration changes from an unsaturated C-F bond to C-F 2 and C-F 3 bonds with growing CF 4 flow rate. Thus, the reduction mechanism of the dielectric constant can be obtained by variation of the C-F X bonding configuration as well as the incorporation of fluorine.

Amorphous silicon nitride (a-SiNs) thin films are deposited at low temperature by remote-type inductively coupled plasma enhanced chemical vapor deposition (ICP-CVD) using N,/SiH4 gases as reactant gases to obtain low hydrogen content in the films. Refractive index, deposition rate, stoichiometry, hydrogen content, and hydrogen configuration in the films are analyzed with the varation of deposition parameters. As RF power and N, flow rate increase, refractive index decreases due to the decrease of Si/N ratio, total hydrogen content is constant with N-H changing hydrogen bond configurations (Si-H, N-H) reversely. However, as substrate temperature increases, refractive index increases due to the reduction of Si/N ratio, and total hydrogen content as well as both hydrogen bond configurations (Si-H, N-H) decrease. In remote-type ICP-CVD using N,/SiH4 gases, N-rich a-SiNs films with low refractive index and density are deposited due to efficient dissociation of N, gas by high density plasma, and hydrogen content in the films is greatly reduced.

Accurate state of health (SoH) estimation of rechargeable batteries is important for the safe and reliable operation of electric vehicles (eVs), smart phones, and other battery operated systems. We propose a novel method for accurate SoH estimation which does not necessarily need full charging data. Using only partial charging data during normal usage, 10 derived voltage values (v sei) are collected. the initial v sei point is fixed and then for every 1.5% increase in the Coulomb counting, other points are selected. The difference between the v sei values (Δv sei) and the average temperature during the charging form the feature vector at different SOH levels. The training data set is prepared by extrapolating the charging voltage curves for the complete SOH range using initial 400 cycles of data. The trained artificial neural network (Ann) based on the feature vector and SoH values can be used in any battery management system (BMS) with a time complexity of only O n () 4. Less than 1% mean absolute error (MAe) for the test cases has been achieved. the proposed method has a moderate training data requirement and does not need any knowledge of previous SoH, state of charge (Soc) vs. ocV relationship, and absolute Soc value.

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...