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Parameter sensitivity analysis and simplification of equivalent circuit model for the state of charge of lithium-ion batteries

机译:锂离子电池充电状态等效电路模型的参数灵敏度分析及简化

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To ensure model accuracy, the model parameters in the equivalent circuit model (ECM) are updated frequently with varying state of health (SOH) and state of charge (SOC). In this work, the parameter sensitivity of the 2RC model with one-state hysteresis (2RCH) is investigated to determine the crucial parameters. Firstly, the model parameters of 2RCH is identified using particle swarm optimization under dynamic working conditions. Secondly, the sensitivity analysis of parameters in 2RCH for two types of batteries is qualitatively examined using the one-factor-at-a-time method. Thirdly, a simplified model, in which the crucial parameters with high sensitivities are updated with SOC and SOH, while the other parameters retain their initial values, is proposed to ensure model accuracy while reducing computational complexity greatly. Finally, SOC estimation based on the simplified ECM for two types of batteries over the whole SOC range under different SOHs is performed using the extended Kalman filter. The experimental results show that the SOC accuracy obtained by updating the crucial parameters is almost the same as that obtained by updating all parameters. The simplified model is beneficial to avoid unnecessary repeated calculation of model parameters for different SOC and SOH ranges in the SOC estimation. (C) 2019 Elsevier Ltd. All rights reserved.
机译:为了确保模型精度,等效电路模型(ECM)中的模型参数经常更新,具有不同的健康状态(SOH)和充电状态(SOC)。在这项工作中,研究了具有单态滞后(2RCH)的2RC模型的参数灵敏度以确定关键参数。首先,在动态工作条件下使用粒子群优化来识别2RCH的模型参数。其次,使用单因素 - at-at-time方法定性检查2RCH中参数的敏感性分析。第三,简化模型,其中具有高灵敏度的关键参数用SOC和SOH更新,而其他参数将保留其初始值,以确保模型精度,同时大大降低计算复杂度。最后,使用扩展卡尔曼滤波器执行基于在不同SOH的整个SOC范围内的两种电池的简化ECM的SOC估计。实验结果表明,通过更新关键参数获得的SOC精度几乎与通过更新所有参数获得的SOC精度。简化模型是有益的,避免在SOC估计中对不同SOC和SOH范围的不同SOC和SOH范围的模型参数的不必要的重复计算。 (c)2019 Elsevier Ltd.保留所有权利。

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