This work presents a dual-purpose study. The first objective is to generate a realistic synthetic dataset for battery health monitoring. The second objective is to validate both the realism of the dataset and the performance of a novel neural architecture, termed the Set-Routed Neural Network (SetNet), for regression-based estimation of State of Health (SOH) in medical-grade energy storage devices. The dataset comprises 20 complete life cycles, each containing approximately 50,000-time steps with recorded electrical, thermal, and state parameters including Current (A), Voltage (V), Temperature (°C), State of Charge (SOC), and SOH. To ensure realism, statistical validation and frequency-domain analyses confirmed physically plausible behavior, with a mean voltage of 3.89 V ± 0.08, mean temperature of 25.08 °C ± 0.20, and SOC distributions consistent with operational ranges. In the learning phase, SetNet was benchmarked against a standard Multilayer Perceptron (MLP) using an 80/20 train-test split over all life cycles. Results show that SetNet achieved an RMSE of 0.0496, MAE of 0.0412, and an R2 score of 0.2639, outperforming the MLP baseline (RMSE 0.0872, MAE 0.0679, R2 0.275). The smooth convergence curves and stable mini-batch loss observed in SetNet indicate superior generalization and robustness. These findings highlight SetNet’s ability to route features selectively across modular subnetworks, enabling efficient modeling of multi-domain interactions (electrical–thermal–chemical) inherent in medical battery systems. Overall, the proposed architecture establishes a biologically inspired, interpretable foundation for predictive maintenance and reliability assessment in future medical energy systems.
- Open Access
- Article
Physics-Informed and Brain-Inspired Set-Routed Neural Networks for Lithium-Ion Battery Life-Cycle Assessment in Medical Devices
Author Information
Received: 30 Apr 2026 | Revised: 07 Jun 2026 | Accepted: 03 Aug 2026 | Published: 25 Aug 2026
Abstract
Keywords
battery health monitoring | bio-inspired computing | lithium-ion | medical devices | set-routed neural networks | state of health
References
- 1.
Zhang, T.; Li, Z.; Hou, W.; et al. Nanomaterials for Implantable Batteries to Power Cardiac Devices. Mater. Today Nano 2020, 9, 100070. https://doi.org/10.1016/j.mtnano.2019.100070.
- 2.
Xiao, Y.; Wen, J.; Yao, L.; et al. A Comprehensive Review of the Lithium-Ion Battery State of Health Prognosis Methods Combining Aging Mechanism Analysis. J. Energy Storage 2023, 65, 107347. https://doi.org/10.1016/j.est.2023.107347.
- 3.
Zhai, L.; Duan, J.; Lin, T.; et al. Recent Advances in Implantable Batteries: Development and Challenge. J. Alloys Compd. 2024, 979, 173551. https://doi.org/10.1016/j.jallcom.2024.173551.
- 4.
Berghout, T.; Benbouzid, M.; Amirat, Y.; et al. Lithium-Ion Battery State of Health Prediction With a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach. IEEE Trans. Transp. Electrif. 2023, 9, 4492–4502. https://doi.org/10.1109/TTE.2023.3237726.
- 5.
Shuvo, M.M.H.; Titirsha, T.; Amin, N.; et al. Energy Harvesting in Implantable and Wearable Medical Devices for Enduring Precision Healthcare. Energies 2022, 15, 7495. https://doi.org/10.3390/en15207495.
- 6.
Xiong, R.; Shen, W.; Li, C.; et al. Toward a Safer Battery Management System: A Critical Review. J. Power Sources 2020, 479, 228708. https://doi.org/10.1016/j.jpowsour.2020.228708.
- 7.
Lai, X.; Yao, J.; Jin, C.; et al. A Review of Lithium-Ion Battery Failure Hazards: Test Standards, Accident Analysis, and Safety Suggestions. Batteries 2022, 8, 248. https://doi.org/10.3390/batteries8110248.
- 8.
Chen, Y.; Kang, Y.; Zhao, Y.; et al. A Review of Lithium-Ion Battery Safety Concerns: The Issues, Strategies, and Testing Standards. J. Energy Chem. 2021, 59, 83–99. https://doi.org/10.1016/j.jechem.2020.10.017.
- 9.
Lakkireddy, D.; Knops, R.; Atwater, B.; et al. A Worldwide Experience of the Management of Battery Failures and Chronic Device Retrieval of the Nanostim Leadless Pacemaker. Heart Rhythm 2017, 14, 1756–1763. https://doi.org/10.1016/j.hrthm.2017.07.004.
- 10.
Zhang, M.; Yang, D.; Du, J.; et al. A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms. Energies 2023, 16, 3167. https://doi.org/10.3390/en16073167.
- 11.
Dini, P.; Colicelli, A.; Saponara, S. Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications. Batteries 2024, 10, 34. https://doi.org/10.3390/batteries10010034.
- 12.
Zhang, J.; Li, K. State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review. Energies 2024, 17, 5753. https://doi.org/10.3390/en17225753.
- 13.
Soyoye, B.D.; Bhattacharya, I.; Anthony Dhason, M.V.; et al. State of Charge and State of Health Estimation in Electric Vehicles: Challenges, Approaches and Future Directions. Batteries 2025, 11, 32. https://doi.org/10.3390/batteries11010032.
- 14.
Li, M.; Xu, W.; Zhang, S.; et al. State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning. Materials 2025, 18, 145. https://doi.org/10.3390/ma18010145.
- 15.
Tang, K.; Luo, B.; Chen, D.; et al. The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods, Development Trends and Challenges. World Electr. Veh. J. 2025, 16, 429. https://doi.org/10.3390/wevj16080429.
- 16.
Lipu, M.S.H.; Hannan, M.A.; Hussain, A.; et al. A Review of State of Health and Remaining Useful Life Estimation Methods for Lithium-Ion Battery in Electric Vehicles: Challenges and Recommendations. J. Clean. Prod. 2018, 205, 115–133. https://doi.org/10.1016/j.jclepro.2018.09.065.
- 17.
Tian, H.; Qin, P.; Li, K.; et al. A Review of the State of Health for Lithium-Ion Batteries: Research Status and Suggestions. J. Clean. Prod. 2020, 261, 120813. https://doi.org/10.1016/j.jclepro.2020.120813.
- 18.
Dineva, A. Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches. Batteries 2024, 10, 356. https://doi.org/10.3390/batteries10100356.
- 19.
Shan, R.; Wang, Y.; Guo, S.; et al. From Empirical Measurements to AI Fusion—A Holistic Review of SOH Estimation Techniques for Lithium-Ion Batteries in Electric and Hybrid Vehicles. Energies 2025, 18, 3542. https://doi.org/10.3390/en18133542.
- 20.
Che, Y.; Hu, X.; Lin, X.; et al. Health Prognostics for Lithium-Ion Batteries: Mechanisms, Methods, and Prospects. Energy Environ. Sci. 2023, 16, 338–371. https://doi.org/10.1039/D2EE03019E.
- 21.
Liu, Y.; Liu, C.; Liu, Y.; et al. Review on Degradation Mechanism and Health State Estimation Methods of Lithium-Ion Batteries. J. Traffic Transp. Eng. 2023, 10, 578–610. https://doi.org/10.1016/j.jtte.2023.06.001.
- 22.
Wei, Y. Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model. Sensors 2023, 23, 2587. https://doi.org/10.3390/s23052587.
- 23.
Bock, D.C.; Marschilok, A.C.; Takeuchi, K.J.; et al. Batteries Used to Power Implantable Biomedical Devices. Electrochim. Acta 2012, 84, 155–164. https://doi.org/10.1016/j.electacta.2012.03.057.
- 24.
Thomas, F.; Mills, G.; Howe, R.; et al. Lithium Battery Fires: Implications for Air Medical Transport. Air Med. J. 2012, 31, 242–248. https://doi.org/10.1016/j.amj.2011.12.003.
- 25.
Hu, C.; Ye, H.; Jain, G.; et al. Remaining Useful Life Assessment of Lithium-Ion Batteries in Implantable Medical Devices. J. Power Sources 2018, 375, 118–130. https://doi.org/10.1016/j.jpowsour.2017.11.056.
- 26.
Nawaz, M.; Ahmed, J.; Abbas, G. Energy-Efficient Battery Management System for Healthcare Devices. J. Energy Storage 2022, 51, 104358. https://doi.org/10.1016/j.est.2022.104358.
- 27.
Ali, J.B.; Azizi, C.; Saidi, L.; et al. Reliable State of Health Condition Monitoring of Li-Ion Batteries Based on Incremental Support Vector Regression with Parameters Optimization. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 2023, 237, 717–727. https://doi.org/10.1177/0959651820950849.
- 28.
Madani, S.S.; Shabeer, Y.; Allard, F.; et al. A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis. Batteries 2025, 11, 127. https://doi.org/10.3390/batteries11040127.
- 29.
Comanescu, C. Ensuring Safety and Reliability: An Overview of Lithium-Ion Battery Service Assessment. Batteries 2025, 11, 6. https://doi.org/10.3390/batteries11010006.
- 30.
Rosenbaum, C.; Klinger, T.; Riemer, M. Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning. arXiv 2017. arXiv:1711.01239.
- 31.
Chowdhury, I.M.; Su, K.; Zhao, Q. MS-NET: Modular Selective Network. Int. J. Mach. Learn. Cybern. 2021, 12, 763–781. https://doi.org/10.1007/s13042-020-01201-8.
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