Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

    •  Tu, H., Moura, S., Wang, Y., Fang, H., "Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries", Applied Energy, DOI: 10.1016/​j.apenergy.2022.120289, Vol. 329, December 2022.
      BibTeX TR2022-155 PDF
      • @article{Tu2022dec,
      • author = {Tu, Hao and Moura, Scott and Wang, Yebin and Fang, Huazhen},
      • title = {Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries},
      • journal = {Applied Energy},
      • year = 2022,
      • volume = 329,
      • month = dec,
      • doi = {10.1016/j.apenergy.2022.120289},
      • url = {}
      • }
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  • Research Areas:

    Machine Learning, Multi-Physical Modeling


Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high- precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable voltage predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. The experiments show that the model has high voltage predictive accuracy throughout a LiB’s cycle life.