Abstract
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.
Highlights
L ITHIUM-ION (Li-ion) batteries are the promising candidates for electric vehicle (EV) applications, owing to their impressive features such as high energy density, high efficiency, and environmental friendliness [1]
Two comparisons are first conducted to quantify the improvement by using Gaussian process regression (GPR)+automatic relevance determination (ARD) model for calendar aging prediction
We can conclude that with the same calendar aging dataset, the training performance can be improved by using ARD-based kernel functions
Summary
L ITHIUM-ION (Li-ion) batteries are the promising candidates for electric vehicle (EV) applications, owing to their impressive features such as high energy density, high efficiency, and environmental friendliness [1]. In [13], by taking the initial surface layer caused by cell formation into account, an extended semiempirical model was proposed to improve the calendar aging predictive ability These referred works belong to open-loop models without strong generalization abilities; in a way, their performance highly depends on the quality of test experiments. In the light of this, it could be a promising way through developing an improved GPR technique with the multidimensional kernel structure to capture the battery capacity degradation dynamics under different temperature and SOC storage conditions. Based on our dataset, the prediction performance of our proposed GPR model is investigated in terms of different kernel functions, and compared with a regression calendar-life (RCL) model This is the first known data-driven application by utilizing GPR with ARD kernel to handle battery calendar aging predictions.
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