Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
نویسندگان
چکیده
Accurate forecasting of the lifetime and degradation mechanisms lithium-ion batteries is crucial for their optimization, management, safety while preventing latent failures. However, typical state estimations are challenging due to complex dynamic cell parameters wide variations in usage conditions. Physics-based models need a tradeoff between accuracy complexity vast parameter requirements, machine-learning require large training datasets may fail when generalized unseen scenarios. To address this issue, paper aims integrate physics-based battery model machine learning leverage respective strengths. This achieved by applying deep framework called physics-informed neural networks (PINN) electrochemical modeling. The charge health cells predicted integrating partial differential equation Fick’s law diffusion from single particle into network process. results indicate that PINN can estimate with root mean square error range 0.014% 0.2%, has 1.1% 2.3%, even limited data. Compared conventional approaches, less still incorporating laws physics process, resulting adequate predictions, situations.
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ژورنال
عنوان ژورنال: Batteries
سال: 2023
ISSN: ['2313-0105']
DOI: https://doi.org/10.3390/batteries9060301