Bayesian Estimation of Model Parameters of Equivalent Circuit Model for Detecting Degradation Parts of Lithium-ion Battery
Bayesian Estimation of Model Parameters of Equivalent Circuit Model for Detecting Degradation Parts of Lithium-ion Battery
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DOI：
10.1109/access.2021.3131190
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Tamon Miyake;Tomoyuki Suzuki;Satoshi Funabashi;Namiko Saito;Mitsuhiro Kamezaki;Takahiro Shoda;T. Saigo;S. Sugano
中科院分区：
文献类型：
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作者：
Tamon Miyake;Tomoyuki Suzuki;Satoshi Funabashi;Namiko Saito;Mitsuhiro Kamezaki;Takahiro Shoda;T. Saigo;S. Sugano
如今，电动汽车的使用不断增加，导致对更有效地使用锂离子电池的需求不断增长。之前的研究已经估算了充电状态（SOC），以优化电池的能量管理。为了更有效地利用电池，检测退化非常重要。然而，传统方法很难区分包括不同时间常数的模型参数的影响。识别多个 RC 并联支路的模型参数（代表更宽频率范围的阻抗）是检测零件退化的必要要求。在本研究中，我们提出了一种估计多个 RC 并行分支模型参数的方法。我们通过设置搜索范围限制和移动窗口设计了马尔可夫链蒙特卡罗算法，能够估计不同时间常数的并行分支的模型参数。通过基于仿真的算法验证，估算出的三阶电路模型参数误差在15.2%以内。此外，阻抗是使用真实电池数据集根据测试中估计的模型参数计算得出的。从 0.01 到 100 Hz，阻抗误差小于 10%，这足以监测由于退化而引起的参数变化。由于0.1 Hz以上的高频段阻抗更容易因退化而发生变化，因此所提出的方法可用于监测因退化而变化的模型参数。
Nowadays, the use of electric vehicles is increasing leading to a growing demand for more efficient use of lithium-ion batteries. The state-of-charge (SOC) has been estimated in previous studies to optimize energy management of batteries. For more efficient battery utilization, detecting degradation is important. However, it is difficult for conventional methods to distinguish the effect of the model parameters including different time constants. Identifying model parameters of multiple RC parallel branches, which represent the impedance of wider frequency ranges, is a necessary requirement to detect the degradation of parts. In this study, we present a method for estimating the model parameters of multiple RC parallel branches. We designed the Markov Chain Monte Carlo algorithm by setting a search range limit and moving window, which enable estimation of the model parameters of parallel branches of different time constants. Through validation of the algorithm based on simulation, the model parameters of a third-order circuit were estimated to be within the error range of 15.2 %. In addition, impedance was calculated from the estimated model parameters in the test using a real battery dataset. The error of impedance was less than 10 % from 0.01 to 100 Hz which was sufficiently low to monitor the change of the parameters owing to degradation. As the impedance in the high-frequency band above 0.1 Hz is more likely to change because of degradation, the proposed method can be used to monitor the model parameters that change as a result of degradation.