Online Electrical Fault Diagnosis and Low-Cost State Estimation for Lithium-Ion Battery Pack Based Electric Drive System

The electrical fault diagnosis and state estimation are curial tasks for lithium-ion battery pack based electric drive system. The electrical fault of batteries will lead to abnormal state estimation. Considering the computational cost and limited memory sources of the on-board battery management system, a framework of online electrical fault diagnosis and low-cost state estimation for the lithium-ion battery pack is proposed. Firstly, an available capacity based macro-selection method is carried out to select a “representative cell” of the battery pack periodically. Secondly, the correlation coefficient between “representative cell” and non-representative cells is calculated based on an optimal moving window in dual time-scale, then the electrical fault can be compensated based on the correlation coefficient in time. Finally, the Kalman filter framework is adopted for robust closed-loop state estimation for the lithium-ion battery pack, in which the Gaussian process regression is developed for measurement equation and Ampere hour counting is established for state equation, then the low-cost state estimation can be achieved. Experimental tests on battery packs were carried out under several dynamic load conditions considering user habits. The validation results demonstrate the robustness and accuracy of the proposed approach even there exists multiple disturbances.