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Electrochamical Model-based SOC Estimations by Using Different Algorithms for Lithium-ion Batteries

机译:使用不同算法的锂离子电池基于电热模型的SOC估计

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In order to compare the performance of different state estimation algorithms in electrochamical model-based SOC(state of charge) estimation for lithium-ion battery, this paper proposed a series of SOC estimation approaches which use different algorithms including extended Kalman filter(EKF), adaptive extended Kalman filter(AEKF), particle filter(PF) and dichotomy. Their accuracy, convergence and computation efficiency was examined at the end of the paper.
机译:为了在基于电模型的锂离子电池SOC(SOC)估计中比较不同状态估计算法的性能,提出了一系列SOC估计方法,这些方法使用包括扩展卡尔曼滤波器(EKF)在内的不同算法,自适应扩展卡尔曼滤波器(AEKF),粒子滤波器(PF)和二分法。在本文结尾处检查了它们的准确性,收敛性和计算效率。

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