Investigation of Voc and SoH on Li-ion batteries with an electrical equivalent circuit model using optimization algorithms

Çarkıt, Taner; Alçı, Mustafa

doi:10.1007/s00202-021-01484-2

Cited by 7 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current studies have divided the techniques for evaluating the SOH into two categories: model-based and data-driven methodologies [17,18]. To perform model-based SOH estimation, researchers usually build models of lithium-ion batteries utilizing electrochemical models [19,20], equivalent circuit models [21][22][23], or empirical models [24,25]. This estimation identifies and estimates battery characteristic parameters by constructing physicochemical models through the computational processing of experimental data, thereby achieving a practical SOH estimation [17].…”

Section: Introductionmentioning

confidence: 99%

State-of-Health Estimation of Lithium-Ion Battery Based on Constant Voltage Charging Duration

Chen,

Han

et al. 2023

Batteries

View full text Add to dashboard Cite

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.

show abstract