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This paper studies the storage life of ICR18650 lithium-ion batteries at different temperatures and different SOC (stage of charge). The author establishes a storage life prediction model. Based on the empirical model, the author analyzed the relationship between temperature and SOC about the influencing factors through experimental data, and used nonlinear regression fitting to determine the model parameters, and the storage life prediction model of irreversible capacity loss with respect to temperature, SOC, and time is finally established. Based on the differences of monomers and measurement errors during test, an error model was established, and a Monte Carlo simulation test was conducted on the battery storage life. Then we build a SVR model based on the obtained data. According to the SVR model, we can analyze the health state of the battery pack based on the given parameters of battery pack in storage. Thus, we can make a prognosis about the SOH of battery pack quickly and accurately. The innovation of the research is that based on the storage test of battery cells combined with data analysis and deductive simulation, we can predict the health state of the whole battery pack. The operation can greatly save experimental resources. [ABSTRACT FROM AUTHOR]

The capacity of lithium batteries varies under different temperature conditions. However, many studies still neglect the influence of temperature on battery capacity. Besides, some studies only considered the combination of the heat and electricity of the battery monomer and failed to study the performance of the battery pack in groups. This paper investigates a new method for dynamic situations that takes the influence of temperature on battery capacity into consideration. After acquiring the parameter through experiment, we can substitute the performance parameters into the thermoelectric coupling model and use MATLAB to realize the iterative calculation of thermoelectric coupling. By combining the grouping mode with the model, we can simulate the thermodynamic and electrical properties of the battery cell in the pack. The novelty is evident because the paper focused on the battery pack instead of a single cell and the lumped parameter thermal model is adopted. Besides, the method proposed in this paper considered the influence of temperature on battery capacity. The program of calculation process can be solidified on the vehicle system. After the simulation, we can get the available capacity of the battery pack at different initial temperatures, with different grouping modes and different inconsistency of parameters. The results predicted by MATLAB model are compared with the results of MATLAB‐FLUENT thermoelectric coupling simulation. The result reflects that the prediction by the MATLAB model about the available capacity, temperature, and residual electricity is of good precision. [ABSTRACT FROM AUTHOR]