Data-driven state of health estimation for lithium-ion battery based on voltage variation curves

The state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for battery management system, but the accuracy and generalizability of the widely used data-driven methods are strongly dependent on the selection of LIBs health features (HFs). In this paper, four types of LIBs with different anode types from four datasets, including NASA dataset, CALCE dataset, Oxford dataset and UL-PUR dataset, were selected to extract the area of constant current charging and discharging voltage curves as two sets of HFs, and then the high correlation between the HFs and SOH is verified by their Pearson coefficient. Secondly, with the two sets of HFs, the SOH of selected batteries in the four datasets are evaluated under Gaussian Process Regression, Long and Short-Term Memory neural network and Back Propagation neural network respectively. With a training/test set ratio model of 50/50 and cross-validation method, all algorithms obtain accurate SOH estimation results. Finally, the estimation results are compared with reference data under the same dataset and training mode, and it is found that the proposed method shows better estimation accuracy and robustness than other evaluation methods by multiple HFs or even complex algorithms.