Fuzzy Entropy-Based State of Health Estimation of LiFePO4 Batteries Considering Temperature Variation

Sample entropy (SE) has been used as a feature to estimate the state of health (SOH) of batteries as it can capture the voltage variation during battery degradation. However, the SE shows an obvious change when the battery is aged at different temperatures, leading to a decrease in the estimation accuracy. Therefore, the fuzzy entropy (FE), is proposed as a feature in terms of temperature variation. The FESOH is used as the input-output data pair of support vector machine, then the single-temperature model, full temperature model, and partial-temperature model are established. Compared with the SE-based method, FE-based method not only has better estimation accuracy, but also decreases the dependence on the size of training data. Finally, the effectiveness of the proposed method is verified by the experimental results.