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All Journal Sinergi Advance Sustainable Science, Engineering and Technology (ASSET)
Ashidqi, Muhamad Dzaky
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Physics-Informed Neural Network with Thevenin Equivalent Circuit for Accurate SOC Li-ion Battery Estimation Apribowo, Chico Hermanu Brillianto; Ashidqi, Muhamad Dzaky; Arifin, Zainal; Santoso , Henry Probo
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2613

Abstract

Accurate state of charge (SOC) estimation is essential for the safety, performance, and longevity of lithium-ion batteries. Physics-based models such as equivalent circuit models (ECMs) are computationally efficient but struggle under nonlinear and time-varying conditions, whereas purely data-driven approaches often lack interpretability. This study proposes a hybrid framework that integrates a physics-informed neural network (PINN) with a first-order Thevenin ECM for dynamic SOC estimation using only terminal voltage and current inputs. The method incorporates physically derived parameters including open-circuit voltage (OCV), polarization resistance, and capacitance identified through pulse testing. An eighth-order OCV–SOC polynomial regression optimized with a genetic algorithm (GA) enables nonlinear mapping, while the Newton–Raphson (NR) method is applied for final SOC estimation. Experimental validation was performed on 18 Ah lithium iron phosphate (LFP) cells over 300 charge–discharge cycles at 20 °C, extended up to 2000 cycles under 1C/2C rates with cut-off voltages of 3.7 V and 2.7 V. Comparative analysis with extended kalman filters (EKF) and standard neural networks (NN) demonstrates the superiority of the proposed method, achieving a root mean squared error (RMSE) of 0.103, mean absolute percentage error (MAPE) of 0.702%, and coefficient of determination (R²) of 0.998. By embedding physical constraints into the learning process, the PINN enhances accuracy, robustness, and generalizability, while reducing estimation uncertainty, thereby offering a scalable and interpretable solution for real-time battery management systems (BMS) in electric vehicles (EVs) and battery energy storage systems (BESS).
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 Apribowo, Chico Hermanu Brillianto Daniel Santoso Santoso , Henry Probo Zainal Arifin