Online capacity estimation for lithium-ion batteries through joint estimation method
发表时间:2020-12-31     阅读次数:     字体:【


摘要

Accurate capacity estimation of lithium-ion batteries is a crucial challenge, especially in the presence of noise in the acquisition sensors. This paper developed an online capacity estimation technique based on the joint estimation algorithms for lithium-ion batteries. The recursive least squares algorithm is used for parameter identification, and the adaptive H-infinity filter is responsible for capacity estimation. In order to solve the problem that the capacity and state of charge will affect each other and cause the convergence speed to slow down, the open circuit voltage at the current sampling instant is expressed as the equation of open circuit voltage and capacity at the previous sampling instant. Therefore, the capacity can be treated as a state, as well as the open circuit voltage, rather than state of charge to be estimated through the adaptive H-infinity filter. The capacity estimation error based on recursive least squares and adaptive H infinity filter is also deduced in this study. The simulation results indicate that the estimated capacity can quickly converge to the reference capacity in case the initial parameter values are inaccurate. Moreover, the erroneous initial parameters have a greater impact than the sensor noises on the capacity estimation error.


部分图片:

图1 Capacity estimation based on RLS-AHIF joint estimation method.


图2 DST test profiles: (a) current; (b) terminal voltage; (c) SOC.


引文信息

Yu Q , Xiong R , Yang R , et al. Online capacity estimation for lithium-ion batteries through joint estimation method[J]. Applied Energy, 2019, 255:113817-. (下载链接)

其他相关论文

1. Rui Xiong,Fengchun Sun,Hongwen He,Trong Duy Nguyen. A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles[J]. Energy,2013,63.(下载链接


2. Sun F , Xiong R , He H . Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions[J]. Journal of Power Sources, 2014, 259(aug.1):166-176.下载链接



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