A data-driven state of health （SOH） assessment platform for vehicle power batteries

doi:10.19799/j.cnki.2095-4239.2022.0186

Abstract

Abstract:

With the rapid development and popularization of electric vehicles (EVs), accurate evaluation of the state of health (SOH) of vehicle power batteries has become a pressing issue. To address the problem, this study adopts a machine learning approach based on the Light Gradient Boosting Machine (LightGBM) framework. This approach involves collecting data, processing characteristics, carrying out training, and finally constructing a data-driven battery SOH analytic system. As a first step, six characteristics are extracted from the original data: the time of minimum discharge voltage; the 75th percentile of load voltage; the average discharge voltage; the 25th percentile of discharge load voltage; the 25th percentile of discharge voltage; and the standard deviation of discharge voltage. Secondly, the data are further processed to reduce memory consumption and computing cost. This processing utilizes LightGBM's key algorithms: histogram-based decision tree learning, Gradient-Based One-Side Sampling, and Exclusive Feature Bundling. Finally, system function is verified and compared with similar work, using data from the NASA Ames Prognostics Center of Excellence. Results demonstrate that the SOH platform can deliver high predictive accuracy (a root-mean-square error of 0.0103). The system represents significant progress in vehicle battery SOH prediction methodology and has high potential for practical application.

Key words: power battery, SOH, evaluation platform, LightGBM

CLC Number:

CHANG Zeyu, ZHANG Zhiqi, ZHANG Xiaodong, LI Li, YU Yajuan. A data-driven state of health （SOH） assessment platform for vehicle power batteries[J]. Energy Storage Science and Technology, 2022, 11(6): 1847-1853.

Figures/Tables 7

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指标	LightGBM	GBDT	XGBoost	LSTM
RMSE	0.0103	0.0139	0.011	0.0153
R²	99.5	98.98	98.6	—

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