Journal of Energy Storage, Volume 79, 2024, 110189, ISSN 2352-152X, DOI: doi.org/10.1016/j.est.2023.110189.

Deep Learning Model for State of Health Estimation of Lithium Batteries Based on Relaxation Voltage


Runze Wanga, Junfu Lia, Xinyu Wanga, Siyi Wanga, and Michael G. Pechtb
aSchool of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, Shandong, China
bCenter for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA

For more information about this article and related research, please contact Prof. Michael G. Pecht.

Abstract:

Lithium-ion batteries have been widely used in many fields due to their high energy density, long cycle life and low self-discharge rate. However, most existing SOH estimation methods require a lengthy amount of time to determine features, the acquisition of multi-dimensional raw data is challenging, and the application scenarios are limited, making SOH estimation algorithms less practical in developing battery management systems (BMS). To face the challenge, this paper develops a new battery SOH estimation method and investigates the transferability of machine learning models under multiple operating conditions using transfer learning. First, features are extracted from the 15 s relaxation voltage using tsfresh, a Python package for time series feature extraction. Subsequently, four statistical features were obtained using a feature selection method based on the Pearson correlation coefficient. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) model is established and hyperparameters are adjusted using Bayesian optimization. The root mean square error (RMSE) and the mean absolute percentage error (MAPE) of battery SOH estimation on battery data set are 1.210 % and 1.225 %, respectively. Finally, based on transfer learning, the Bi-LSTM model is applied to battery datasets under multiple operating conditions, reducing the model's dependence on original training data while ensuring model accuracy. Considering the easy accessibility of short-term relaxation battery data, the battery SOH estimation method proposed in this paper is expected to facilitate the estimation of battery SOH under various operating conditions.

This article is available online here and to CALCE Consortium Members for personal review.

[Home Page] [Articles Page]
Copyright © 2024 by CALCE and the University of Maryland, All Rights Reserved