一种对抗性的权重注意力机制Seq2Seq模型估算SOC

doi:10.19595/j.cnki.1000-6753.tces.231338

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摘要

锂电池荷电状态（SOC）准确估算是新能源技术发展中的一项关键技术问题,由于难以直接获取SOC准确数值,而面对此长序列预测问题,采用传统深度学习方法,其估算效果不佳。对此,本文提出一种对抗性的权重注意力序列到序列(AWAS)模型,用于估算SOC,其中权重注意力机制(Weighed Attention)通过引入额外的线性变换增强了注意力机制提取长序列依赖的能力。该模型由门控循环单元(Gated Recurrent Units,GRU)作为编码器和解码器的基本构建模块。首先利用编码器提取特征间的相关信息,然后将包含特征信息的隐藏向量交由权重注意力处理,以深化特征间的关联性学习,随后由GRU进行解码,最后与生成对抗网络(Generative Adversarial Network ,GAN)中的鉴别器(Discriminator）联合,提高模型估算能力。通过多步SOC估算任务的测试实验,本文提出的模型估算SOC的均方根误差以及平均绝对百分比误差分别达到0.169 5%和0.209 6%;同时,在不同数据集的单步估算任务测试验中,平均绝对误差和均方根误差达到0.141 2%和0.109 4%;相比稀疏化Informer模型在平均绝对误差评估指标上降低了45.7%。

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	陈治铭
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关键词 ：锂电池荷电状态, 序列到序列模型, 对抗生成网络, 稀疏化Informer, 注意力

Abstract：

In practical use, lithium-ion batteries often face extreme temperatures, and the behavior of different battery systems varies under different discharge conditions. Traditional State of Charge (SOC) prediction methods, based on physical models, often overlook these differences, resulting in significant prediction errors. In recent years, as deep learning techniques, including Recurrent Neural Networks (RNNs) and Transformers, have gained attention in time series forecasting, most of these methods have shown limited progress in improving prediction accuracy. To address these challenges, this paper introduces an Adversarial Weighted Attention Sequence-to-Sequence (AWAS) model to enhance SOC prediction.
First, features are extracted from the lithium-ion battery dataset to create an input matrix. This matrix is then sent through an encoder with Gated Recurrent Units (GRU) to capture feature correlations.Next, on top of the multi-head attention mechanism, an additional linear layer is introduced to compute a weight matrix W related to the number of attention heads. The output of this linear layer matches the number of attention heads, facilitating extra linear transformations of queries, keys, and values, and mapping them into the weight space of the multi-head attention. This enhances the computational flexibility of the attention mechanism.Then, the relevant information extracted by the GRU is input into the improved weight attention mechanism and is then passed to the GRU in the decoder. This process strengthens the extraction of correlated information among the features.Finally, the concept of adversarial training is introduced, using a three-layer convolutional layer as the core of the discriminator. The output of the decoder's GRU is considered as sequence 1, while the corresponding SOC values for the given time period are treated as sequence 2. The truthfulness of sequence 1 and sequence 2 is evaluated using the Sigmoid function. Adversarial training mitigates the issue of gradient vanishing in the GRU, resolving long-term dependencies. Ultimately, the encoder's output is processed through a fully connected layer to obtain the SOC prediction. The results show that for single-step prediction tasks, our proposed model achieved significantly reduced Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) on the LG-HG2 dataset, with values of 0.141 2% and 0.109 4% respectively. On the Panasonic dataset, the errors further decreased to 0.101 3% and 0.080 3%. The Sparse Informer model, which outperformed others in control experiments, achieved errors of 0.260 2%, 0.218 2%, 0.380 1%, and 0.278 2%. In the case of a 12-step prediction task, our model achieved the lowest average MAE of 0.108 7% and Mean Absolute Percentage Error (MAPE) of 0.173 4% on the Panasonic dataset at -20℃.The results of ablation experiments indicated that the average MAPE for 12-step predictions decreased to 0.347 2%, while the average RMSE and MAE for single-step predictions reduced to 0.096 6% and 0.083 7%, respectively. These findings validate the effectiveness of our model architecture.
The experiments lead to these conclusions:(1) Compared to numerous RNN and Transformer models, the incorporation of adversarial training into the Seq2Seq model effectively mitigates the issues of gradient vanishing and exploding. Across various prediction tasks, the error evaluations for the three different datasets consistently yielded the lowest values. Thus, the introduction of adversarial training is deemed appropriate.(2)Conventional multi-head attention mechanisms exhibit a substantial increase in MAPE as the lithium battery temperature rises. In contrast, the proposed weight attention mechanism reduces errors and curbs the upward trend in errors. From the experimental results, it is evident that the weight attention mechanism is more practical.

Key words： State of Charge Sequence-to-Sequence Generative Adversarial Network Informer Attention

收稿日期: 2023-08-17

PACS:

TM912

基金资助:

国家自然科学基金（62172095）、福建省自然科学基金（2023J01349）和福建省创新资金项目（2022C0022）资助

通讯作者: 刘建华男,1967年生,教授,博士,硕士生导师,研究方向为机器学习、智能计算、自然语言处理等。E-mail：656095080@qq.com

作者简介: 陈治铭男,1998年生,硕士研究生,研究方向为时间序列预测和机器学习。E-mail：c1198832099@163.com

引用本文:

陈治铭, 刘建华, 柯添赐, 陈可纬. 一种对抗性的权重注意力机制Seq2Seq模型估算SOC[J]. 电工技术学报, 0, (): 231338-231338. Chen Zhiming, Liu Jianhua, Ke Tianci, Chen Kewei. An Adversarial Weighted Attention Seq2Seq model for predicting SOC. Transactions of China Electrotechnical Society, 0, (): 231338-231338.

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