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All Journal Mechatronics, Electrical Power, and Vehicular Technology Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Teknik dan Ilmu Komputer Sinergi Majalah Kedokteran Gigi Indonesia Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Techne : Jurnal Ilmiah Elektroteknika Journal of Robotics and Control (JRC)
Daniel Santoso
Universitas Kristen Satya Wacana
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Dynamic modeling of lithium-ion battery degradation using data-driven and physics-informed method Santoso, Daniel; Ashidqi, Muhamad Dzaky
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.013

Abstract

Accurate real‑time prediction of lithium‑ion battery (LIB) capacity degradation is essential for embedded battery‑management systems. Equivalent circuit models (ECMs) run quickly but lose accuracy over time, whereas purely data-driven networks achieve high precision at a high computational cost. This study introduces a physics‑informed neural network (PINN) that embeds the differential equations of a first‑order Thevenin ECM directly into the loss function. Using only terminal voltage and current as inputs, the network simultaneously estimates internal resistance, polarization resistance, polarization capacitance, open‑circuit voltage, and capacity loss. The model was trained and evaluated over 300 charge–discharge cycles of a 18650 lithium-ferrous phosphate (LFP) cell. The resulting capacity degradation estimation achieved a root mean squared error (RMSE) of 0.012 and a mean absolute percentage error (MAPE) of 0.974 %, surpassing a neural ordinary differential equation baseline with RMSE of 0.215. The trained network contains 261 parameters, requires 0.6 ms per sample for inference, and consumes 49 MB of memory. This computation cost is far lower than that of a long short‑term memory (LSTM) benchmark with comparable accuracy. In addition, the proposed model maintains its accuracy under limited dataset conditions. With a fourfold smaller training set, the PINN maintained an RMSE of 0.023, whereas the LSTM error increased to 0.72. The results demonstrate that lightweight neural networks guided by physics-based constraints can provide reliable, real-time health estimation on resource‑limited hardware.
Co-Authors Adi Gunawan Andika Sukma Deryawan Anton Suprayudi Ashidqi, Muhamad Dzaky Asno Azzawagama Firdaus Bob William Chandra Deddy Susilo Fransiscus Dalu Setiaji Heriyanti Amalia Kusuma Jati Wasesa Leonardus Heru Pratomo M.Th. Esti Tjahjanti Muhajir Yunus Muzakkir Pangri Oei Kurniawan Utomo Oyas Wahyunggoro Prapto Nugroho Prapto Nugroho Roni Wijaya Sanjaya, Kurnia Saptadi Nugroho Sriyono Sriyono Tri Handoko Wahid Munawar Yayi Suryo Prabandari Yonas Aditya Darmawan