基于平滑性假设的锂电池开路电压-荷电状态映射提取

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

摘要:

开路电压（OCV）是准确估计电动汽车锂离子电池荷电状态（SoC）的重要参数。由于OCV-SoC的映射关系随着电池老化而持续变化，因此在某一特定阶段确定的OCV-SoC函数无法适用于电池全生命周期内的SoC估计，由此需要对OCV进行定期测试及老化校准。受OCV-SoC曲线迟滞现象的影响，传统的OCV测试通常需要数天时间才能获得一个或多个完全充放电周期的数据，因此从电动车实际运行的维度上缺乏OCV实时测试和校准的可实现性。本文提出了一种快速灵活的OCV-SoC提取方法，主要基于锂电池放电过程的OCV-t曲线平滑性假设，利用非支配排序遗传算法（NSGA-II）实现基于任意电流-电压测量数据的OCV-SoC关系提取；随后在UDDS工况下结合拓展卡尔曼滤波（EKF）进行了SoC验证。结果表明，基于平滑性假设可以有效地构建OCV-SoC的映射关联，其中SoC的最大估计误差为2%，且不受滤波器SoC初值的影响。

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.