Abstract
Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.
Highlights
Due to high power density, low self-discharge rate, and long service life, lithium-ion batteries are widely used as energy storage devices for various applications such as smart grids, electric vehicles, etc
To improve stabilization and prevent severe accidents through the use of lithium-ion batteries, good battery management systems (BMSs) for safety monitoring and timely maintenance are in great demand [1,2]
We proposed a battery capacity estimation approach based on a semisupervised convolutional neural network (SS-CNN)
Summary
Due to high power density, low self-discharge rate, and long service life, lithium-ion batteries are widely used as energy storage devices for various applications such as smart grids, electric vehicles, etc. Model-based methods yield estimation by identifying the model parameters of the battery (e.g., equivalent circuit model and electrochemical model, etc.) [5,6,7]. This these methods require precise models that are not trivial in practice. Data-driven methods attempt to estimate the capacity of batteries using a two-step fashion, feature extraction and machine-learning based regression [8,9]. Data-driven methods reduce the dependence on precise battery models. They still have potential in terms of their generalization ability and capacity estimation accuracy
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