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A comparative study of integral order and fractional order models for estimating state-of-charge of lithium-ion battery

机译:用于估计锂离子电池的充电状态的整体秩序和分数级模型的比较研究

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摘要

Battery state estimation is a key technology for battery management systems for electric vehicles, and state-of-charge (SOC) estimation of battery is the basis for numerous state estimations. In this paper, five fractional order equivalent circuit models are compared and evaluated based on a LiNMC cell. First of all, the particle swarm optimisation (PSO) is used to identify the parameters of the fractional order models, and the fractional Kalman filter algorithm is further adopted to estimate the SOC and compared with the SOC estimation obtained by the integral order models. The results indicate that the fractional battery model has higher accuracy, especially in the low SOC interval. Through comparative analysis of several fractional order models, it is found that the fractional order model with the Warburg component can be better describe the battery characteristics in the low SOC interval. From the perspective of model accuracy and computational cost, the addition of the Warburg element to the fractional second-order RC model is the best choice.
机译:电池状态估计是用于电动车辆的电池管理系统的关键技术,电池的充电状态(SOC)估计是许多状态估计的基础。在本文中,比较了五个分数顺序等效电路模型,并基于Linmc小区进行了评估。首先,粒子群优化(PSO)用于识别分数阶模型的参数,并且进一步采用分数卡尔曼滤波器算法来估计SOC并与由积分阶模型获得的SOC估计进行比较。结果表明,分数电池模型具有更高的精度,特别是在低SOC间隔中。通过对几种分数型型号的比较分析,发现与Warburg组件的分数阶模型可以更好地描述低SOC间隔中的电池特性。从模型准确性和计算成本的角度来看,向分数二阶RC模型添加Warburg元素是最佳选择。

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