A closed-loop degradation-aware self-healing battery framework for ultra-long-duration energy storage

Binggui Lu
Faculty of Education, Shinawatra University, Pathum Thani 12160, Thailand
Manisha Sagar Pawar
Department of Engineering Sciences and Humanities, Vishwakarma Institute of Technology, Pune 411037, India
Budigi Prabhaka
Department of Physics, Noida International University, Greater Noida 203201, India
Simranjeet Nanda
Center for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India
Tusha
Quantum University Research Center, Quantum University, Roorkee 247667, India
Kasturi Pohini
Centre for Multidisciplinary Research, Anurag University, Hyderabad 500088, India
Yagna B. Adhyaru
Faculty of Engineering, Gokul Global University, Siddhpur 384151, India

Article ID: 4571

Keywords: degradation-aware electrodes; self-healing batteries; long-duration energy storage; remaining useful life prediction; hybrid predictive modeling; battery degradation; intelligent energy storage

Abstract

Degradation in lithium-ion batteries employed for ultra-long duration energy storage (LDES) greatly restricts the performance, dependability, and remaining useful life (RUL) predictability of such devices. Current methods tend to emphasize either material degradation or data-driven prognostic approaches and seldom incorporate the notions of self-healing and intelligent prognosis. A novel paradigm of degradation-aware self-healing electrodes is introduced by combining a composite core-shell electrode, a reversible self-healing matrix, embedded multi-modal sensing, and a physics-informed hybrid predictive model. It enables monitoring of mechanical stress, impedance increase, temperature, and capacity degradation, leading to a closed loop of degradation detection, self-healing, and adaptive RUL prediction. The approach was analyzed via simulations under representative long-duration battery operation and contrasted with the traditional lithium-ion electrode. The suggested framework demonstrated substantial improvements in electrochemical durability and prediction performance. The cycle life of the battery has been extended to 2,100 charge-discharge cycles from 1,200 charge-discharge cycles, and capacity retention after 1,000 cycles rose from 68% to 86%. The impedance increase was significantly decreased by approximately 40%, and the normalized stress increase decreased from 1.00 to 0.62. The capacity fade rate dropped from 1.8% to 0.9% per 100 cycles. Also, the hybrid prediction framework lowered the error rate of RUL prediction from 18.4% to 6.7%, outperforming traditional predictive frameworks. The statistical analysis conducted across 10 simulation runs proved the significance of the observed changes (two-tailed paired t-test, p < 0.001). The suggested degradation-aware self-healing framework proves the potential of combining autonomous recovery and hybrid prediction in order to improve both the battery's durability and its prognostic performance at once. This closed-loop system is able to prevent the negative effects caused by degradation on battery performance and improve reliability.

Published

2026-09-02

How to Cite

Lu, B., Sagar Pawar, M., Prabhaka, B., Nanda, S., Tusha, Pohini, K., & Yagna B. Adhyaru. (2026). A closed-loop degradation-aware self-healing battery framework for ultra-long-duration energy storage. Energy Storage and Conversion, 4(2). https://doi.org/10.59400/esc4571

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Vol. 4 No. 3 (2026): In Progress

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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