谈发明, 赵俊杰, 李秋烨. 基于简化滞回OCV模型的锂电池SOC自适应估计策略[J]. 中国电机工程学报, 2021, 41(2): 703-714. DOI: 10.13334/j.0258-8013.pcsee.201465
引用本文: 谈发明, 赵俊杰, 李秋烨. 基于简化滞回OCV模型的锂电池SOC自适应估计策略[J]. 中国电机工程学报, 2021, 41(2): 703-714. DOI: 10.13334/j.0258-8013.pcsee.201465
TAN Faming, ZHAO Junjie, LI Qiuye. Adaptive SOC Estimation Strategy for Lithium Battery Based on Simplified Hysteresis OCV Model[J]. Proceedings of the CSEE, 2021, 41(2): 703-714. DOI: 10.13334/j.0258-8013.pcsee.201465
Citation: TAN Faming, ZHAO Junjie, LI Qiuye. Adaptive SOC Estimation Strategy for Lithium Battery Based on Simplified Hysteresis OCV Model[J]. Proceedings of the CSEE, 2021, 41(2): 703-714. DOI: 10.13334/j.0258-8013.pcsee.201465

基于简化滞回OCV模型的锂电池SOC自适应估计策略

Adaptive SOC Estimation Strategy for Lithium Battery Based on Simplified Hysteresis OCV Model

  • 摘要: 受锂电池滞回效应的影响,开路电压与荷电状态之间的关系复杂,给电池建模以及荷电状态的精确估计带来较大困难。以锰酸锂电池单体为研究对象,在通过实验对滞回特性分析的基础上,提出简化的滞回开路电压模型,该模型根据滞回主环路中开路电压差之间的荷电状态积累量大小来构建滞回因子,修正开路电压与荷电状态之间的关系,以提升电池等效电路模型的精度;其次,针对测量噪声异常扰动、模型发生变化及荷电状态初值存在偏差的情况,利用分阶段变换测量协方差及构建自适应因子方法对无迹卡尔曼滤波算法改进,以平衡荷电状态的估计精度和收敛速度。实验结果表明,简化滞回开路电压模型能较为地准确描述锂电池动静态特性,所提自适应无迹卡尔曼滤波算法估计荷电状态的性能有较大提升。

     

    Abstract: Due to the hysteresis effect of lithium battery, the relationship between the open-circuit voltage and state of charge is complicated, so it is difficult to model and estimate the state of charge accurately. With lithium manganate battery monomer as the research object, through the experiment based on analyzing the hysteresis characteristics, the simplified open-circuit voltage hysteresis model was put forward, the hysteresis factor was constructed according to the size of the state of charge accumulation between the open-circuit voltage difference in the hysteresis main loop, and corrected the relationship between the open-circuit voltage and the state of charge to improve the accuracy of the battery equivalent circuit model; Secondly, aiming at the abnormal disturbance of measurement noise, the change of model and the deviation of the initial value of the state of charge, the method of phased transformation to measure covariance and constructing adaptive factor was used to improve the unscented Kalman filter algorithm to balance the convergence speed and estimation accuracy of the state of charge. The experimental results show that the dynamic and static characteristics of the lithium battery are accurately described by the model, and the performance of the proposed adaptive unscented Kalman filter algorithm to estimate the state of charge is greatly improved.

     

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