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Physics-based Machine Learning with Filtering for Failure Prognostics Partially Observable Dynamic Systems
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DOI：10.1109/RAMS51457.2022.9893922.png)
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Abstract
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This paper demonstrates a useful hybrid methodology for online capacity estimation of lithium-ion batteries. Essentially, a Kalman filter was embedded with a physics-informed neural network to optimize the connection between observable measurements and the hidden state. In this case, for the lithium-ion battery application, the hidden state is capacity, and the observable measurements are voltage. Fundamentally, the physics-informed neural network is a residual model, where the data-driven neural network models the error between the physics model and the noisy measurements. Overall, this structure performs well and has significant improvements over traditional Kalman filter frameworks. The paper is organized as follows: section 1 provides an introduction, section 2 explains the methodology, section 3 shows the results, and section 4 concludes with a discussion.
Keywords:
Kalman Filter
Physics-informed Machine learning
Neural Network
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Cited Papers
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OAAIShareSaveA Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different TemperatureENERGIESOAAI
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OAAIShareSaveA multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimationAPPLIED ENERGY
APPLIED ENERGYIF11
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