This paper studied the gases release of a graphite//NMC111(LiNi 1/3 Mn 1/3 Co 1/3 O 2 ) cell during cycle in the voltage ranges of 2.6-4.2V and 2.6-4.8V and the temperatures of at 25°C and 60°C. It was proved that the CO 2 , CO, and H 2 gases are released as a result of electrolyte decomposition. And it shows that the CO and H 2 gases evolution is a direct consequence of the electrochemical reaction of electrolyte decomposition, while the CO 2 generation is a consequence of the additional chemical reaction of interaction between the O 2 released from the cathode atomic lattice oxygen and CO released from the same place on the cathode (appearing because of the electrolyte decomposition). That is why at the same electrochemical reaction of electrolyte decomposition, the ratio CO 2 /CO varies in the wide range from 0.82 to 2.42 depending on cycling conditions (temperature and cutoff voltage). It was proved that a potential-independent H 2 evolution is a consequence of its adsorption in pores of powdered graphite on anode. There was proposed the mechanism of the electrolyte decomposition and the gases evolution in lithium-ion cells at their cycling, which corresponds quantitatively to all obtained experimental results.

In this study, it has been established experimentally that a probability of a thermal runaway in the nickel-cadmium accumulators KSX-25 grows with an increase of a constant-voltage charge as well as of an environmental temperature and an accumulator time in service. It was shown that the thermal runaway is initiated not because of an external current supply source but instead it is a result of a powerful internal exothermic reaction. A possible thermal runaway mechanism is proposed corresponding with all the received experimental results.

In this investigation, it was shown that a probability of thermal runaway in commercial lithium-ion cells of the type 18650 grows with number increase of charge/discharge cycles and increase of cells state of charge (SOC). Notably, experiments in an accelerating rate calorimeter (ARC) showed that with the number growth of cells charge/discharge cycles, it is observed a considerable decline of an initiation temperature of exothermic reactions of thermal runaway and increase of released energy. Additional ARC-experiments with the following analysis of the gas released showed that in a course of cells cycling in anode graphite, hydrogen is accumulated. It was proven in experiments that a recombination of released-from-graphite-anode atomic hydrogen is exactly that powerful exothermic reaction, which increases the released energy in the beginning of the thermal runaway and decreases the temperature of its initiation. Thus, the reason for the initiation of thermal runaway in lithium-ion cells is a powerful exothermic reaction of recombination of atomic hydrogen accumulated in anode graphite in a during of cells cycling. The possible mechanism of initiation thermal runaway has been proposed corresponding to all the experimental results obtained. At present, lithium-ion batteries are ones of the most promising electrochemical systems of energy storing. They are dominant energy sources in hand-hold devices: smartphones, notebooks, tablets, etc.

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In this study, an analytical model is represented for evaluation of remaining charge state of commercial automotive-grade lithium batteries. The model is elaborated for evaluation of battery remaining charge state under dynamic loads and various temperature conditions, which is typical for operation of batteries being parts of electric or hybrid electric vehicles. It is proved that use of the Peukert equation as a part of an analytical model leads to a restriction of field-of-use for those models as the Peukert equation is not applicable under small discharge rates. It has been also shown that in modern analytical models, the usually used temperature dependencies are not applicable at extremely low and extremely high temperatures of battery discharge. The reason is that they do not take into account neither the presence of a negative thermal critical point, under which the battery capacity output becomes equal to zero, nor a limitation of a capacity growth in conditions of a temperature increase. The represented analytical model solves such problems to a large extent and provides with a good prediction accuracy for battery remaining state of charge (relative error is not more than 4% Nowadays in connection with wide spread distribution of electric vehicles and hybrid electric vehicles, -the acute need has emerged in evaluation possibility of battery residual capacity. This problem matters a great deal both for the modern lithium batteries, which at the moment are mainly used ones as parts of electric vehicles, and for traditional batteries, for example nickel-cadmium or lead-acid ones, which are widely used in area of aviation, railway vehicles, monitoring systems, electric-light services, etc. 1-4For battery residual capacity evaluation, it is possible to use the most precise electrochemical models. They take into account a transport-component (of ions, electrons, neutral particles) as well as all the kinetic electrochemical processes. 5,6 Those models contain very many hardly defined battery characteristics such as electrode geometries, electrolyte concentration, diffusion coefficients, transfer coefficients, reaction rate coefficients, and other low-level phenomena. 5-11So while the existing electrochemical models would be likely more exact in their predictions and able to provide with better results, the creation, calibration and implementation of such models requires significant computational capabilities. This makes these simulations less suitable for on-road vehicular battery prediction. Additionally, despite of the extensive efforts having been spent on such models, they can quickly become inapplicable if the system in question requires a change in battery chemistry or configuration since calibration of the models is going on for a specific battery and pack configuration. As a result, the majority of these models requires either widespread calibration, exhaustive computational resources, or fails to tune the models for dynamic discharge conditions. Both factors limit their effectiveness in mobile applica...