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With the continuous popularization of electric vehicles (EVs), ensuring the best performance of EVs has become a significant concern, and lithium-ion power batteries are considered as the essential storage and conversion equipment for EVs. Therefore, it is of great significance to quickly evaluate the state of power batteries. This paper investigates a fast state estimation method of power batteries oriented to after-sales and maintenance. Based on the battery equivalent circuit model and heuristics optimization algorithm, the battery model parameters, including the internal ohmic and polarization resistance, can be identified using only 30 minutes of charging or discharging process data without full charge or discharge. At the same time, the proposed method can directly estimate the state of charge (SOC) and maximum available capacity of the battery without knowing initial SOC information.

The application of Li(Ni0.8Co0.1Mn0.1)O2 (NCM811) cathode-based lithium-ion batteries (LIBs) has alleviated electric vehicle range anxiety. However, the subsequent thermal safety issues limit their market acceptance. A detailed analysis of the failure evolution process for large-format LIBs is necessary to address the thermal safety issue. In this study, prismatic cells with nominal capacities of 144Ah and 125Ah are used to investigate the thermal runaway (TR) characteristics triggered by lateral overheating. Additionally, TR characteristics under two states of charge (SoCs) (100% and 5%) are discussed. Two cells with 100% SoC exhibit similar characteristics, including high failure temperature, high inhomogeneity of temperature distribution, multi-points jet fire, and significant mass loss. Two cells with 5% SoC demonstrate only a slight rupture of the safety valve and the emission of white smoke.

As the core component of electric vehicles (EVs), batteries attach increasingly general attention along with the rapid expansion of electric vehicle market. Battery performance effect directly the safety and reliability of the EVs, so its managing technologies are more and more crucial. Among them, the methods of estimating the state of health (SoH) and predicting remaining useful life become the focuses, which are essential to ensure their dependability and optimum performance over time. This paper mainly focuses on impedance modeling and aging research (aging diagnosis and life prediction) of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS) technique is used to obtain impedance characteristic of batteries. On the one hand, equivalent circuit modeling (ECM) can be motivated by EIS, with the goal to fit measured impedance data using circuit elements.