Data-driven and feature-sifting based state-of-health estimation for li-ion batteries using KPCA and NRBO-optimized transformer-LSTM hybrid model
- 1State Grid Neijiang Power Supply Co., Ltd., Neijiang, Sichuan, China
- 2College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, Hubei, China
Abstract
Electrochemical energy storage technologies, such as lithium-ion batteries, are widely used in various scenarios, but their capacity, lifespan, and state-of-health (SOH) are susceptible to numerous influencing factors. To improve the accuracy of state-of-health (SOH) prediction for lithium-ion battery cells, this paper builds up a feature-based analysis framework, and proposes an enhanced predicting model, which integrates the principles of incremental capacity analysis (ICA) and differential voltage analysis (DVA), and the ideas of kernel principal component analysis (KPCA), and Pearson correlation analysis. The former ones are used to extract a set of health related features (HFs) from the filtered incremental capacity (IC) and differential voltage (DV) curves of batteries, while the latter ones are employed to eliminate redundant features. Furthermore, this work bridges the gap between the charging data of the cells and their SOH by building up a Newton-Raphson-based optimization Transformer-LSTM (NRBO-Transformer-LSTM) model, where NRBO algorithm is introduced to find out the optimized number of attention heads, learning rates and the regularization coefficient. Experimental validation on the NASA and CALCE battery datasets shows that, with 70% of the data used as the training set, the proposed method achieves maximum mean absolute errors (MAE) of 0.0084 and 0.0071, and root mean square errors (RMSE) of 0.0091 and 0.0089, respectively, significantly outperforming multiple benchmark algorithms in both prediction accuracy and stability.
Related
- Handling Capacity Regeneration Effects for Accurate SOH Estimation in Lithium-Ion Batteries via Optimized Transformer
- State of Charge Estimation for Lithium-Ion Batteries Under Wide Temperature Ranges Using Error Feedforward of Sparrow-Optimized Back Propagation Neural Network
- Fast State-of-Health Estimation of Lithium-Ion Batteries Using Short-Term Relaxation-Voltage Features Integrated with a Differential Evolution-Optimized Extreme Learning Machine Algorithm
- Multi-Stage State-of-Health Estimation for Lithium-Ion Batteries Based on an Adaptive SHAP-Guided Dual-Attention LSTM
- Physics-Informed Explainable Machine Learning for Hybrid State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries
BibTeX
@article{luo2026datadriven,
title = {{Data-driven and feature-sifting based state-of-health estimation for li-ion batteries using KPCA and NRBO-optimized transformer-LSTM hybrid model}},
author = {Xiangcai Luo and Jinwei Chen and Haijiang Zhang and Chunhua Hu and Jinyong Zhao and Fei Xu and Rui Zhen and Chengchen Deng and Xi Chen},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359015},
url = {https://doi.org/10.1371/journal.pone.0359015},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Data-driven and feature-sifting based state-of-health estimation for li-ion batteries using KPCA and NRBO-optimized transformer-LSTM hybrid model
AU - Xiangcai Luo
AU - Jinwei Chen
AU - Haijiang Zhang
AU - Chunhua Hu
AU - Jinyong Zhao
AU - Fei Xu
AU - Rui Zhen
AU - Chengchen Deng
AU - Xi Chen
PY - 2026
DA - 2026/10/05
JO - PLOS One
DO - 10.1371/journal.pone.0359015
UR - https://doi.org/10.1371/journal.pone.0359015
AB - Electrochemical energy storage technologies, such as lithium-ion batteries, are widely used in various scenarios, but their capacity, lifespan, and state-of-health (SOH) are susceptible to numerous influencing factors. To improve the accuracy of state-of-health (SOH) prediction for lithium-ion battery cells, this paper builds up a feature-based analysis framework, and proposes an enhanced predicting model, which integrates the principles of incremental capacity analysis (ICA) and differential voltage analysis (DVA), and the ideas of kernel principal component analysis (KPCA), and Pearson correlation analysis. The former ones are used to extract a set of health related features (HFs) from the filtered incremental capacity (IC) and differential voltage (DV) curves of batteries, while the latter ones are employed to eliminate redundant features. Furthermore, this work bridges the gap between the charging data of the cells and their SOH by building up a Newton-Raphson-based optimization Transformer-LSTM (NRBO-Transformer-LSTM) model, where NRBO algorithm is introduced to find out the optimized number of attention heads, learning rates and the regularization coefficient. Experimental validation on the NASA and CALCE battery datasets shows that, with 70% of the data used as the training set, the proposed method achieves maximum mean absolute errors (MAE) of 0.0084 and 0.0071, and root mean square errors (RMSE) of 0.0091 and 0.0089, respectively, significantly outperforming multiple benchmark algorithms in both prediction accuracy and stability.
SN - 1932-6203
ER -