Data Availability Statement
The data presented in this study are openly available in reference number [42] Zhu, J.; Wang, Y.; Huang, Y.; Bhushan Gopaluni, R.; Cao, Y.; Heere, M.; Mühlbauer, M.J.; Mereacre, L.; Dai, H.; Liu, X.; et al. Data-Driven Capacity Estimation of Commercial Lithium-Ion Batteries from Voltage Relaxation. Nat. Commun. 2022, 13, 2261..It is imperative to determine the State of Health (SOH) of lithium-ion batteries precisely to guarantee the secure functioning of energy storage systems including those in electric vehicles. Nevertheless, predicting the SOH of lithium-ion batteries by analyzing full charge–discharge patterns in everyday situations can be a daunting task. Moreover, to conduct this by analyzing relaxation phase traits necessitates a more extended idle waiting period. In order to confront these challenges, this study offers a SOH prediction method based on the features observed during the constant voltage charging stage, delving into the rich information about battery health contained in the duration of constant voltage charging. Innovatively, this study suggests using statistics of the time of constant voltage (CV) charging as health features for the SOH estimation model. Specifically, new features, including the duration of constant voltage charging, the Shannon entropy of the time of the CV charging sequence, and the Shannon entropy of the duration increment sequence, are extracted from the CV charging phase data. A battery’s State-of-Health estimation is then performed via an elastic net regression model. The experimentally derived results validate the efficacy of the approach as it attains an average mean absolute error (MAE) of only 0.64%, a maximum root mean square error (RMSE) of 0.81%, and an average coefficient of determination (R2) of 0.98. The above statement serves as proof that the suggested technique presents a substantial level of precision and feasibility for the estimation of SOH.Guided (Key) Projects for Industry in Fujian Province under grant 2022H0046
