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All Journal Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics Jurnal Neutrino : jurnal fisika dan aplikasinya JFA (Jurnal Fisika dan Aplikasinya) Journal of Natural Sciences and Mathematics Research Journal of Physics and Its Applications Jurnal Ilmu Fisika Jurnal Teori dan Aplikasi Fisika Physics Education Research Journal Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Journal of Multiscale Materials Informatics
Sheilla Rully Anggita
Universitas Islam Negeri Walisongo Semarang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Other

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Journal : Journal of Multiscale Materials Informatics

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Framework for Early Prediction of Lithium-Ion Battery Lifetime: A Hybrid Quantum-Classical Approach Anggita, Sheilla Rully; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.15055

Abstract

Accurately predicting the lifetime of lithium-ion batteries during early charge–discharge cycles remains a significant challenge due to the nonlinear and weakly expressed degradation dynamics in the initial stages of operation. Classical machine learning (ML) models—although effective in pattern recognition—often face limitations in modeling complex correlations within small, high-dimensional datasets. To address these challenges, this study proposes a Hybrid Quantum–Classical Machine Learning (HQML) framework that integrates a Variational Quantum Circuit (VQC) as a quantum feature encoder with a Gradient Boosting Regressor (GBR) as the classical learner. The proposed approach is implemented using the Qiskit Aer simulator on the MIT Battery Degradation Dataset (124 cells, 42 engineered features). By encoding multi-source degradation descriptors (voltage, capacity, temperature, internal resistance) into Hilbert space via amplitude and angle encoding, the HQML model captures intricate nonlinear feature interactions that are inaccessible to conventional kernels. Experimental results demonstrate that the hybrid model achieves an RMSE of 93 cycles and an R² of 0.94, outperforming the best classical baseline (SVM + Wrapper selection, RMSE = 115, R² = 0.90). Furthermore, quantum observables analysis reveals interpretable correlations between entanglement strengths and physical degradation indicators. These results highlight the potential of quantum machine learning as a powerful paradigm for high-fidelity battery prognostics in the early-life regime.
Co-Authors Affa Ardhi Saputri Akrom, Muhamad Alvania Nabila Tasyakuranti Anshori Huroeroh Ari Bawono Arsini -, Arsini Azizah, Tasya Salsabila Baehaqi Fachrizal Rian Pratama Fahira Septiani Fariyani, Qisthi Habik Setiawan Hadi Kusuma, Hamdan Hartono Hartono Heri Susanto Heri Susanto Heri Sutanto Isnawati, Nurul Embun Istikomah Khotijah Khotijah Khotijah Khotijah Prahesti, Arinda Yukashima Putri Prahesti, Arinda Yukashima Putri Prastyo, Irman Said Renanda Fasha Devarasalya Sumarti, Heni Susilawati Susilawati