A Review on Power Electronic Converters for Modular BMS with Active Balancing

Miranda, João; Barros, Luis A. M.; Pinto, J. G.

doi:10.3390/en16073255

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…The Pearson coefficient represents the covariance ratio between two elements equal to the product of their respective standard deviations, as derived in Equations ( 8) and (9).…”

Section: Feature Correlation Analysismentioning

confidence: 99%

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

Chen,

Han

et al. 2023

Batteries

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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.

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“…The Pearson coefficient represents the covariance ratio between two elements equal to the product of their respective standard deviations, as derived in Equations ( 8) and (9).…”

Section: Feature Correlation Analysismentioning

confidence: 99%

“…In order to ensure the efficient and safe operation of lithium-ion battery energy storage systems, the Battery Management System (BMS) is an indispensable component [3,[9][10][11][12]. Furthermore, accurately estimating the SOH holds significant importance in BMS to diagnose the degree of battery life decay.…”

Section: Introductionmentioning

confidence: 99%