State of Health Estimation for Lithium-Ion Batteries Based on Transferable Long Short-Term Memory Optimized Using Harris Hawk Algorithm

Guangyi Yang (), Xianglin Wang, Ran Li and Xiaoyu Zhang
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Guangyi Yang: Engineering Research Center of Automotive Electronics Drive Control and System Integration, Harbin University of Science and Technology, Ministry of Education, Harbin 150080, China
Xianglin Wang: School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China
Ran Li: Engineering Research Center of Automotive Electronics Drive Control and System Integration, Harbin University of Science and Technology, Ministry of Education, Harbin 150080, China
Xiaoyu Zhang: College of Artificial Intelligence, Nankai University, Tianjin 300110, China

Sustainability, 2024, vol. 16, issue 15, 1-19

Abstract: Accurately estimating the state of health (SOH) of lithium-ion batteries ensures the proper operation of the battery management system (BMS) and promotes the second-life utilization of retired batteries. The challenges of existing lithium-ion battery SOH prediction techniques primarily stem from the different battery aging mechanisms and limited model training data. We propose a novel transferable SOH prediction method based on a neural network optimized by Harris hawk optimization (HHO) to address this challenge. The battery charging data analysis involves selecting health features highly correlated with SOH. The Spearman correlation coefficient assesses the correlation between features and SOH. We first combined the long short-term memory (LSTM) and fully connected (FC) layers to form the base model (LSTM-FC) and then retrained the model using a fine-tuning strategy that freezes the LSTM hidden layers. Additionally, the HHO algorithm optimizes the number of epochs and units in the FC and LSTM hidden layers. The proposed method demonstrates estimation effectiveness using multiple aging data from the NASA, CALCE, and XJTU databases. The experimental results demonstrate that the proposed method can accurately estimate SOH with high precision using low amounts of sample data. The RMSE is less than 0.4%, and the MAE is less than 0.3%.

Keywords: lithium-ion battery; battery aging mechanism; state of health; model training; long short-term memory neural network; Harris hawk optimization; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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