Extraction of Open Circuit Voltage-State of Charge Curve for Lithium-Ion Batteries Based on Smoothness Optimization

Abstract:

Open circuit voltage （OCV） is an important variable for accurately estimating the State of Charge （SoC） of lithium-ion batteries in electric vehicles （EV）. Since the OCV-SoC mapping relationship changes continuously as battery ages， the OCV-SoC function determined at a specific aging stage cannot be applied for SoC estimation throughout the battery's entire lifecycle， thus necessitating regular OCV testing and calibration. However， traditional OCV testing can typically take several days to obtain one or more complete charge-discharge-cycle data due to the hysteresis phenomenon of the OCV-SoC curve， making real-time OCV test and calibration impractical in real EV operation scenarios. Here， we proposed a fast and flexible OCV-SoC extraction method based on the smoothness hypothesis of the OCV-t curve during the battery discharge process. The non-dominated sorting genetic algorithm （NSGA-II） was utilized to extract the OCV-SoC relationship based on arbitrary current-voltage measurement data. Meanwhile， the present method was validated using the extended Kalman filter （EKF） based on WLTC and UDDS driving cycles. The results show that the OCV-SoC curve can be effectively constructed based on the smoothness hypothesis， where the maximum estimation error of SoC is 2% and will not be affected by inaccurate initial values.

Key words:lithium-ion batteries;open circuit voltage;State of charge;OCV-SoC relationship;genetic algorithm;smoothness hypothesis