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Open Access

ARTICLE

An Explainable Hybrid Opt-GRU-KAN Architecture for Lithium-Ion Battery Health Prediction

Riya Sharma1,*, Ashima Singh1, Anju Bala1, Mukesh Singh2
1 Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India
2 Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India
* Corresponding Author: Riya Sharma. Email: email
(This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082720

Received 30 March 2026; Accepted 08 May 2026; Published online 11 June 2026

Abstract

State of health (SoH) prediction of lithium-ion batteries is a critical yet challenging task due to the complex, highly non-linear, and time-dependent nature of degradation processes under diverse operating conditions. Variability in usage patterns, environmental factors, and electrochemical dynamics further limits the robustness and generalisation capability of conventional estimation models. This study proposes a hybrid deep learning framework that combines Gated Recurrent Units (GRUs) with Kolmogorov–Arnold Networks (KANs) to address these challenges. GRUs are employed to effectively capture temporal dependencies in sequential battery data, while KANs enhance the model’s ability to learn complex non-linear functional relationships. To improve model transparency and mitigate the black-box nature of deep learning approaches, Explainable Artificial Intelligence (XAI) techniques are integrated to provide interpretable insights into prediction outcomes. Additionally, Optuna-based hyperparameter optimisation is utilised to systematically identify optimal model configurations for enhanced predictive performance. The proposed model is trained using the Toyota Research Institute dataset and validated on real-time electric vehicle battery data collected at Thapar Institute of Engineering and Technology, along with the benchmark CALCE dataset, ensuring robustness across multiple data distributions. Experimental results demonstrate that the hybrid GRU–KAN framework outperforms existing state-of-the-art approaches in terms of accuracy and generalisation. Quantitative evaluation using RMSE, MSE, and MAE shows prediction errors within 1%, indicating high precision in SoH estimation. Module ablation studies further confirm the contribution of each component to the overall model performance.

Keywords

Battery management system; electric vehicle; gated recurrent unit; Kolmogorov Arnold network; lithium-ion battery; optuna optimiser; state of health

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