State of Health Estimations for Lithium-Ion Batteries Based on MSCNN

Jiwei Wang (), Hao Li, Chunling Wu, Yujun Shi, Linxuan Zhang and Yi An
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Jiwei Wang: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Hao Li: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Chunling Wu: School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
Yujun Shi: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Linxuan Zhang: Department of Automation, Tsinghua University, Beijing 100084, China
Yi An: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China

Energies, 2024, vol. 17, issue 17, 1-21

Abstract: Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions.

Keywords: lithium-ion battery; state of health; health indicator; MSCNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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