面向充放电工况的LiFePO4电池迟滞性建模及SOC估计
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上海理工大学机械工程学院

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Hysteresis modeling and SOC estimation for LiFePO4 batteriesunder charge and discharge conditions
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College of Mechanical Engineering,University of Shanghai for Science and Technology

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    摘要:

    针对磷酸铁锂电池(LiFePO4)平坦的开路电压(open circuit voltage,OCV)与荷电状态(state of charge,SOC)滞回特性,在充放电切换的工况下,用传统的等效电路模型(equivalent circuit model,ECM)估计OCV存在精度较低的问题,提出对电池开展迟滞建模。为了突出LiFePO4电池考虑滞回特性的必要性,比较了3种电池模型,对其复杂性、准确性和适用性进行综合评价。结果表明,一阶RC模型不考虑滞回的影响,只适用纯充电或者纯放电的工况;一阶RC滞回模型在一阶RC模型的基础上加上一个滞回量,虽然考虑了滞回特性的影响,但是滞回量受参数辨识影响较大,OCV估计存在波动;Preisach模型对存在充放电切换的工况精度较好,但是训练数据及时间成本较高。因此,实际应用可以择优选取。NEDC(New European Driving Cycle)充放电工况下,对不同模型结合算法开展SOC估计,估计误差均在5%以内,其中Presisach误差在3%以内。

    Abstract:

    In view of the hysteresis characteristics of flat OCV-SOC of LiFePO4 battery, the OCV estimated by using the traditional Equivalent Circuit Model (ECM) has the problem of low accuracy under the charge-discharge switching condition, So hysteresis modeling of battery is proposed. In order to highlight the necessity of considering hysteretic characteristics of LiFePO4 batteries, three battery models were compared to evaluate their complexity, accuracy and applicability. The results show that the first-order RC model is only suitable for pure charge or discharge conditions without considering the influence of hysteresis. The first-order RC hysteresis model adds a hysteresis on the basis of the first-order RC model. Although the influence of hysteresis characteristics is considered, the hysteresis is greatly affected by parameter identification and the OCV estimation fluctuates. The Preisach model has good accuracy for charging and discharging switching conditions, but the training data and time cost are high. Therefore, practical applications can be selected according to different application scenarios. Under NEDC charging and discharging conditions, state of charge (SOC) estimation is carried out for different models combined with algorithms and the estimation error is within 5%, among which the Presisach error is within 3%.

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  • 收稿日期:2021-12-26
  • 最后修改日期:2022-03-06
  • 录用日期:2022-03-09
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