Integrated Framework for Accurate State Estimation of Lithium-ion Batteries Subject to Measurement Uncertainties

The effectiveness of a battery management system (BMS) in Lithium-ion Batteries (LIBs) is significantly dependent on the accuracy of battery sensors. However, owing to the highly nonlinear nature of LIBs, detecting small uncertainties in sensor measurements, which can lead to high estimation errors, poses a remarkable challenge. Moreover, in conventional BMS, sensor uncertainty detection and state estimation are treated separately which greatly reduces its effectiveness. Hence, this paper presents an integrated framework for fast bias detection and state estimation to ensure reliable and safe operations. First, extensive experimentation is performed to statistically analyze the effects of different sensor uncertainties on multiple chemistries of LIBs. Second, a fast bias detection algorithm is proposed to identify small sensor offsets effectively. Third, upon successful bias detection, a hybrid extended nonlinear observer is proposed for simultaneously estimating all states, in which bias is treated as an augmented state. Lastly, the observability of the proposed design is investigated across various practical scenarios. Validation results, using 58Ah NMC and 25Ah LiFePO4 LIB cells, underscore the potential of this integrated strategy to enhance the safety of LIBs in commercial applications.