SSA(BBO)-optimized neural networks for remaining useful life estimation and health monitoring of lithium-ion batteries
- 1College of Automotive Technology, Zhejiang Agricultural Business College
- 2Department of Logistics Management, School of Economics and Management, Zhejiang Shuren University
Abstract
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is crucial for advancing battery health management, enhancing safety, and improving the efficiency of electric vehicles and energy storage systems. This study introduces a novel hybrid deep learning framework that integrates a deep neural network (DNN) with advanced optimization algorithms, including Biogeography-Based Optimization (BBO) and the Improved Sparrow Search Algorithm (ISSA). The framework further employs a competitive adversarial learning mechanism utilizing a linear Support Vector Machine (SVM) as a discriminator. Unlike conventional approaches that primarily rely on past cycle data under constant conditions, the proposed model incorporates real operating parameters—such as variable charge/discharge C-rates, load profiles, ambient temperature, depth of discharge (DoD), and driving cycle statistics—as input features. This significantly reduces uncertainty and enhances the model’s generalizability across diverse operating conditions. Within this framework, the DNN acts as the RUL predictor (generator), while the SVM formulates a min-max optimization problem to penalize physically implausible predictions. Validation on NASA-based datasets augmented with realistic variable operating profiles demonstrates that the proposed ISSA-optimized DNN-SVM model achieves excellent performance with RMSE of 2.25 ± 0.12 cycles, MAE of 1.68 ± 0.09 cycles, and R² of 0.995 ± 0.002 (mean ± std. dev. over 10 independent runs). These results represent a substantial improvement of 58–72% in RMSE compared to standard methods (including the baseline Autoencoder-DNN, LSTM, and CNN-LSTM). The framework also significantly outperforms the BBO-optimized variant, confirming the effectiveness of ISSA in global parameter optimization. These findings underscore the proposed approach’s high robustness under real-world variable operating conditions and its strong potential for real-time deployment in battery management systems (BMS) after offline training, marking a significant step toward extending the lifespan and reliability of lithium-ion batteries.
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BibTeX
@article{tao2026ssabbooptimized,
title = {{SSA(BBO)-optimized neural networks for remaining useful life estimation and health monitoring of lithium-ion batteries}},
author = {Wenlong Tao and Chao Lu and Yichao Jin},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62566-4},
url = {https://doi.org/10.1038/s41598-026-62566-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - SSA(BBO)-optimized neural networks for remaining useful life estimation and health monitoring of lithium-ion batteries
AU - Wenlong Tao
AU - Chao Lu
AU - Yichao Jin
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62566-4
UR - https://doi.org/10.1038/s41598-026-62566-4
AB - Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is crucial for advancing battery health management, enhancing safety, and improving the efficiency of electric vehicles and energy storage systems. This study introduces a novel hybrid deep learning framework that integrates a deep neural network (DNN) with advanced optimization algorithms, including Biogeography-Based Optimization (BBO) and the Improved Sparrow Search Algorithm (ISSA). The framework further employs a competitive adversarial learning mechanism utilizing a linear Support Vector Machine (SVM) as a discriminator. Unlike conventional approaches that primarily rely on past cycle data under constant conditions, the proposed model incorporates real operating parameters—such as variable charge/discharge C-rates, load profiles, ambient temperature, depth of discharge (DoD), and driving cycle statistics—as input features. This significantly reduces uncertainty and enhances the model’s generalizability across diverse operating conditions. Within this framework, the DNN acts as the RUL predictor (generator), while the SVM formulates a min-max optimization problem to penalize physically implausible predictions. Validation on NASA-based datasets augmented with realistic variable operating profiles demonstrates that the proposed ISSA-optimized DNN-SVM model achieves excellent performance with RMSE of 2.25 ± 0.12 cycles, MAE of 1.68 ± 0.09 cycles, and R² of 0.995 ± 0.002 (mean ± std. dev. over 10 independent runs). These results represent a substantial improvement of 58–72% in RMSE compared to standard methods (including the baseline Autoencoder-DNN, LSTM, and CNN-LSTM). The framework also significantly outperforms the BBO-optimized variant, confirming the effectiveness of ISSA in global parameter optimization. These findings underscore the proposed approach’s high robustness under real-world variable operating conditions and its strong potential for real-time deployment in battery management systems (BMS) after offline training, marking a significant step toward extending the lifespan and reliability of lithium-ion batteries.
SN - 2045-2322
ER -