Rapid assessment of power battery states for electric vehicles oriented to after-sales maintenance

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. In order to further improve the efficiency and accuracy of this method, the performance of various optimization algorithms is compared. Finally, we utilized two kinds of lithium-ion power battery, including 117Ah and 156Ah capacity levels, to verify the proposed method. The experimental results indicate that the estimation error of capacity and SOC was controlled within 2.3%, and the estimation error of internal resistance was within 10%. The proposed state estimation method provides a solution for quickly assessing EVs batteries' state of life and remaining utilization value.