A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

Abstract

Abstract:For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries

“…The Thevenin model is composed of a resistance capacitance (RC) circuit in series with ohm internal resistance [47][48][49]. Its basic idea is to use ohm internal resistance to characterize the instantaneous change in terminal voltage in the charging and discharging process [50][51][52], and an RC parallel circuit to characterize the polarization effect of the battery in the using process [53,54]. This model can accurately characterize the dynamic characteristics of lithium battery in the working process.…”

Comparative study of lithium‐ion battery open‐circuit‐voltage online estimation methods

“…In addition, the curve's shape in Fig. 3 is affected by the selected fixed parameters λ min and ρ; the higher ρ is, the more sensitive λ(k) is to e(k) [28]. Therefore, in [28], the author has set these two parameters by minimising the following criterion:…”

Robust Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using Adaptive Forgetting Factor Recursive Least Squares

“…The covariance matrix P(k) are updated for every iteration by the obtained gain matrix G(k) Eq. (10).…”

“…To optimize their values, each parameter employs a separate parameter particle swarm and changes only one step in every cycle. In [10], FFRLS (forgetting factor recursive least squares) is applied to steadily refresh the parameters of a Thevenin model and a nonlinear Kalman filter is used to perform the recursive operation to estimate SOC (state of charge). In [11] an adaptive online estimation algorithm for fractional equivalent circuit model is proposed based on the theory of fractional order calculus and indirect Lyapunov method.…”