A novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method for accurate state‐of‐charge and state‐of‐health co‐estimation of lithium‐ion batteries

Abstract

State‐of‐charge and state‐of‐health of power lithium‐ion batteries are two important state parameters for battery management system monitoring. To accurately estimate the state‐of‐charge and state‐of‐health of in real time, the ternary lithium‐ion battery is taken as the research object, and a novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method is proposed innovatively. The noise variance estimation is introduced to compensate the parameters identified by the general least square method to realize the accurate identification. The estimation value is corrected by using the residual and Kalman gain at multiple times, and different weights are configured for each residual according to the amount of information contained. The data of different complex conditions are used to verify the feasibility of the proposed algorithm, the results show that the root‐mean‐square error of bias compensation recursive least square‐multiple weighted dual extended Kalman filtering under dynamic stress test and Beijing bus dynamic stress test condition can be controlled within 1.62% and 2.70% in state‐of‐charge estimation, 0.17% and 0.81% in state‐of‐health estimation, which verifies that the proposed algorithm in this research has good running effect. The novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method lays a theoretical foundation for the safe operation of electric vehicles.

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

“…[9][10][11][12][13] In the BMS, the accurate estimation of the state-ofcharge (SOC) is very important, which is a key factor for safe charging-discharging and accurate life estimation of the battery pack. [14][15][16][17] Lithium-ion batteries mostly work in complex environments [18][19][20][21][22] ; these working environments and sensor measurement errors will increase the difficulty of accurate SOC estimation. [23][24][25][26][27] As a state variable, SOC cannot be directly measured by sensors but can only be obtained through the calculation of related parameters.…”

“…Lithium‐ion batteries mostly work in complex environments 18–22 ; these working environments and sensor measurement errors will increase the difficulty of accurate SOC estimation 23–27 . As a state variable, SOC cannot be directly measured by sensors but can only be obtained through the calculation of related parameters.…”

A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

“…The objective function J(θ) is taken in RLS; the purpose of the algorithm is to find b θ; when θ ¼ b θ, J(θ) takes the minimum value. The objective function and estimated parameter values of the system are shown in Equation (4).…”

“…With the rapid development of the emerging intelligent industry, the pollution problem facing the world has become more and more severe 1–4 at present. The energy crisis caused by excessive energy consumption has attracted widespread attention from all countries in the world 5–7 .…”

An improved adaptive spherical unscented Kalman filtering method for the accurate state‐of‐charge estimation of lithium‐ion batteries

“…The EKF approximately linearizes the nonlinear model at first and then uses the Kalman filter, but EKF usually ignores the high‐order derivative term of the nonlinear function and adopts the first‐order derivative term and constant term, which leads to large errors in SOC estimation 16–19 . Unscented Kalman filter (UKF) uses an unscented transformation to process data, weighted reconstruction of original data, keeping the mean and variance of data unchanged, and realizing the estimation of the parameters of the nonlinear system 20–24 . To improve the accuracy of lithium‐ion battery SOC estimation, Zhang et al 25 proposed the idea of using ampere‐hour integration method and open‐circuit voltage method combined with EKF and estimated the SOC of lithium‐ion battery by intelligent calculation circuit model; this method has high accuracy and good anti‐interference ability.…”