Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles

This paper discusses an ameliorated sample entropy-based capacity estimator for PHM (prognostics and health management) of Li-ion batteries in electrified vehicles. The aging datasets of eight cells with identical chemistry were used. The sample entropy of cell voltage sequence under the well-known HPPC (hybrid pulse power characterization) profile is adopted as the input of the health estimator. The calculated sample entropy and capacity of a reference Li-ion cell (randomly selected from the eight cells) at three different ambient temperatures are employed as the training data to establish the model by using the least-squares optimization. The performance and robustness of the estimator are validated by means of the degradation datasets from the other seven cells. The associated results indicate that the proposed health management strategy has an average relative error of about 2%.