Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries
Autor: | Mattin Lucu, Kailong Liu, Yi Li, Xiaosong Hu, Widanalage Dhammika Widanage |
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Rok vydání: | 2020 |
Předmět: |
Battery (electricity)
Generalization Computer science TK 020208 electrical & electronic engineering Isotropy 02 engineering and technology computer.software_genre Accelerated aging Computer Science Applications Kernel (linear algebra) State of charge Control and Systems Engineering Robustness (computer science) Kriging Kernel (statistics) 0202 electrical engineering electronic engineering information engineering Relevance (information retrieval) Data mining Electrical and Electronic Engineering QA computer Information Systems |
Zdroj: | IEEE Transactions on Industrial Informatics |
ISSN: | 1941-0050 1551-3203 |
Popis: | Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This article derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and state-of-charge. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multistep prediction test, and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first-known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis. |
Databáze: | OpenAIRE |
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