过度拟合
辍学(神经网络)
计算机科学
电池(电)
人工智能
人工神经网络
荷电状态
卷积神经网络
一般化
机制(生物学)
噪音(视频)
模式识别(心理学)
功率(物理)
机器学习
数学
数学分析
哲学
物理
认识论
量子力学
图像(数学)
作者
Zongxiang Li,Liwei Li,Jing Chen,Dongqing Wang
出处
期刊:Energy
[Elsevier]
日期:2024-01-01
卷期号:286: 129504-129504
被引量:8
标识
DOI:10.1016/j.energy.2023.129504
摘要
A Convolution Neural Network and a Bidirectional Long Short-Term Memory network connected architecture with a Multi-Head Attention mechanism (CNN-BiLSTM-MHA) is studied for predicting state of charge (SOC) of lithium-ion batteries (LIBs). Firstly, an adaptive noise based complete ensemble empirical mode decomposition (AN-CEEMD) algorithm is adopted to catch the intrinsic features of measured battery signals by adding white noises. Secondly, a CNN-BiLSTM model with the MHA mechanism is developed to learn the mapping between processed input signals and battery SOC, it has three parts: 1) the CNN extracts features of the processed data, mine the relation among input signals, and promote estimation precision; 2) the BiLSTM has memory ability to catch battery dynamics, and the Swish activation function in the BiLSTM ensures unsaturated and reduces over-fitting due to its upper unbound and lower bound; 3) The multi-head attention mechanism uses several independent self-attention layers to associate input information and variables, extracts more associate information by adding weights; it reduces the overfitting risk of a single attention head, and improves the model generalization performance through joint learning of multiple heads. Finally, experiments and simulations are implemented under four operating conditions at five different temperatures, and the presented method is verified effective.
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