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Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

机译：基于改进的蚂蚁狮子优化器的NMC细胞的参数识别与充电估计

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摘要

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by trial-and-error method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.
机译：对于锂电池，其广泛用于电动车辆（EVS）中的能量存储系统，准确估计电池参数和充电状态（SOC）对能量功率的预测具有显着影响，剩余的里程的估计和使用寿命的延伸。本文开发了一种改进的蚂蚁狮子优化器（IALO），其将混沌映射理论介绍到初始化和随机行走过程中，以提高人口均匀性和遍历性。在获得精英（最佳）个体之后，在精英个体上进行各个突变算子，进一步利用精英周围的区域，避免局部最佳。然后通过IALO算法优化电池模型参数。至于SOC估计，Unscented Kalman滤波器（UKF）是SOC估计的常见算法。然而，UKF的缺点是噪声信息始终未知，并且通常通过试验和误差方法手动调整，这是不规则和耗时的。本文通过IALO算法优化了噪声信息。用于解决协方差矩阵问题的无人变换过程中使用的奇异值分解（SVD）可能会失去积极的肯定。实验结果验证开发的IALO算法具有卓越的电池模型参数估计性能。通过IALO优化噪声信息后，UKF可以准确估计SOC，并且最大误差率小于1％。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第16期|4961045.1-4961045.18|共18页
  • 作者

    Zhang Kai; Ma Jian; Zhao Xuan; Liu Xiaodong; Zhang Yixi;

  • 作者单位

    Changan Univ Sch Automobile Xian Shaanxi Peoples R China;

    Changan Univ Sch Automobile Xian Shaanxi Peoples R China;

    Changan Univ Sch Automobile Xian Shaanxi Peoples R China;

    Changan Univ Sch Automobile Xian Shaanxi Peoples R China;

    Changan Univ Sch Automobile Xian Shaanxi Peoples R China;

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