Online capacity estimation for lithium-ion batteries through joint estimation method

Abstract

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∞ 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∞ filter. The capacity estimation error based on recursive least squares and adaptive H∞ 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.