Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866913475997990912 |
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| author | Tu, Hao Moura, Scott Wang, Yebin Fang, Huazhen |
| author_facet | Tu, Hao Moura, Scott Wang, Yebin Fang, Huazhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_12979 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries Tu, Hao Moura, Scott Wang, Yebin Fang, Huazhen Computational Engineering, Finance, and Science Machine Learning Systems and Control 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. |
| title | Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries |
| topic | Computational Engineering, Finance, and Science Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2112.12979 |