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. (19th August 2021) - Qiao, Jialu; Wang, Shunli; Yu, Chunmei; Shi, Weihao; Fernandez, Carlos
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. Abstract : To accurately estimate the state‐of‐charge and state‐of‐health of in real time, a novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method is proposed. 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.