The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach
The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach
复制标题
使用新的广泛极限学习机方法进行锂离子电池的容量估计和循环寿命预测
DOI：
10.1016/j.jpowsour.2020.228581
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发表时间：
2020-11
影响因子：
9.2
通讯作者：
Zhen Peng
中科院分区：
文献类型：
--
作者：
Yaying Ma;Lifeng Wu;Yong Guan;Zhen Peng
锂离子电池已成为许多电子设备的主要电源。准确的锂离子电池容量估算和循环寿命预测对保证电子设备的可靠性具有重要意义。极限学习机（ELM）是一种学习速度快、泛化性能好的单隐层前向神经网络。针对深度学习的不足和电池数据集规模的不断增大，引入广义学习（BL）的思想，提出了一种新的ELM模型：广义学习-极限学习机（BL-ELM）。首先，构造ELM网络，并通过输入数据的特征映射产生特征节点。其次，对映射的特征执行增强操作以产生增强节点。接下来，将这两类节点全部合并，成为网络新的输入层，使模型能够快速、充分地从输入数据中获取有效的特征信息。最后，使用不同的电池数据集进行实验。结果表明，BL-ELM方法不仅可以保证估计和预测的精度，而且可以大大节省时间。与其他算法的比较表明，该模型具有更好的效率和竞争力。
Lithium-ion batteries have become the main power source of many electronic devices. Accurate capacity estimation and cycle life prediction of lithium-ion batteries are of great significance to ensure the reliability of electronic devices. Extreme learning machine(ELM) is a kind of single hidden layer feedforward neural network with fast learning speed and good generalization performance. Considering the shortcomings of deep learning and the increasing size of battery datasets, this paper introduces the idea of Broad Learning(BL) and develops a new ELM model: Broad Learning-Extreme Learning Machine(BL-ELM). First, an ELM network is constructed, and the feature nodes are produced by feature mapping of the input data. Second, the enhancement operation is performed on the mapped features to produce the enhancement nodes. Next, all these two kinds of nodes are merged to become the new input layer of the network, so that the model can quickly and fully obtain effective feature information from the input data. Finally, experiments are performed with different battery datasets. The results show that BL-ELM method can not only ensure the accuracy of estimation and prediction but also save time greatly. Further comparisons with other algorithms show that this novel model is more effective and competitive.
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影响因子：
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作者：
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影响因子：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/pedg.2019.8807678
发表时间：
2019-06
期刊：
2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG)
影响因子：
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作者：
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通讯作者：
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