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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...

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.

The magnetic properties of the ternary aluminide TbFe2Al10 have been studied with the help of magnetization measurements. From the temperature and field dependence of magnetization, a detailed magnetic phase diagram of TbFe2Al10 has been constructed. While the high- and low-temperature phases (in low fields) of TbFe2Al10 are paramagnetic and antiferromagnetic respectively, the signature of a field-induced ferromagnetic phase is obtained in the magnetization results in the intermediate temperature regime. While it was already known that TbFe2Al10 has a ferrimagnetic phase in between the low-field antiferromagnetic and the high-field ferromagnetic phases, the present results indicate the presence of a second intermediate-field-induced ferrimagnetic phase in the compound, in between the first ferrimagnetic and the high-field ferromagnetic phases. The possible magnetic structure for this second ferrimagnetic phase is proposed on the basis of existing neutron diffraction results. The successive field-induced or metamagnetic transitions in TbFe2Al10 are found to be induced by temperature as well, when the applied magnetic field is appropriate. The present magnetization results also indicate the presence of short-range magnetic correlations in TbFe2Al10 well inside the paramagnetic regime. Owing to the presence of successive temperature and field-induced magnetic phase transitions, TbFe2Al10 is found to exhibit a moderate magneto-caloric effect with a maximum of 7.86 J kg−1K−1 at 18.5 K. The magneto-caloric effect is found to persist well inside the paramagnetic regime because of the presence of short-range magnetic correlations at these temperatures. This leads to a substantial refrigerant capacity in the material, which could be useful information for future technology.

The dehydrogenation kinetics of LiBH(4) dispersed on multi-walled carbon nanotubes (MWCNTs) by the solvent infiltration technique has been studied. Commercial MWCNTs were ball-milled for different milling times in order to increase the specific surface area (SSA) as measured by the BET technique. Thermal programmed desorption measurements have been performed using a Sievert's apparatus on samples with different SSA of MWCNTs and different LiBH(4) to MWCNT ratio. Pressure composition isotherms (PCI) have been obtained at different temperatures in order to estimate the DeltaH and DeltaS of dehydrogenation. It has been observed that the dispersion of LiBH(4) on MWCNTs leads to a lower dehydrogenation temperature compared to pure LiBH(4). Moreover, the dehydrogenation temperature further decreases with increasing MWCNT surface area. An interpretation of the kinetic effect is proposed.