电子测量与仪器学报

陈庆斌,杨耿煌,耿丽清,苏娟,尚春虎.基于合作博弈策略和DBO-BiLSTM-Attention的电动汽车充电桩故障预测[J].电子测量与仪器学报,2025,39(4):163-171

基于合作博弈策略和DBO-BiLSTM-Attention的电动汽车充电桩故障预测

Fault prediction of electric vehicle charging stations based oncooperative game strategy and DBO-BiLSTM-Attention

DOI：

英文关键词:charging pile cooperative game dung beetle optimization algorithm bidirectional long short-term memory network attention mechanism

基金项目:国家重点研发计划(2022YFB2403002)、天津市科技计划项目(23YDTPJC00320，24YDTPJC00740)资助

作者	单位
陈庆斌	天津职业技术师范大学自动化与电气工程学院天津300222
杨耿煌	1.天津职业技术师范大学自动化与电气工程学院天津300222；2.天津市信息传感与智能控制重点实验室天津300222
耿丽清	1.天津职业技术师范大学自动化与电气工程学院天津300222；2.天津市信息传感与智能控制重点实验室天津300222
苏娟	中国农业大学信息与电气工程学院北京100083
尚春虎	天津职业技术师范大学自动化与电气工程学院天津300222

Author	Institution
Chen Qingbin	School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Yang Genghuang	1.School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China; 2.Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin 300222, China
Geng Liqing	1.School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China; 2.Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin 300222, China
Su Juan	School of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Shang Chunhu	School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China

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中文摘要:

针对电动汽车充电桩故障率较高的问题，提出一种基于合作博弈策略和蜣螂优化算法双向长短期记忆网络注意力机制(DBO-BiLSTM-Attention)的电动汽车充电桩故障预测方法。首先，通过参数统计分布处理异常值，通过均值填充处理缺失值，对处理后的数据归一化操作；其次，从不同角度出发，选取主观评价方法层次分析法、客观评价方法CRITIC权重法和机器学习算法中的随机森林依次计算特征权重，采用合作博弈策略对上述特征权重进行组合，得到新特征权重，并对参数特征矩阵进行放大；然后，分别引入蜣螂优化算法和注意力机制，搭建DBO-BiLSTM-Attention模型，在仿真实验下，所提模型训练集和测试集的准确率、F1系数分别为0.89、0.89、0.90和0.90；最后，构建相关对比实验。结果表明，相比于不进行特征放大的模型，测试集准确率和F1系数分别提高了5%和6%；相比于不采用合作博弈策略的模型，测试集准确率和F1系数分别提高了2%和3%，验证所提模型的有效性和合理性。

英文摘要:

Aiming at the problem of the high failure rate of electric vehicle charging piles, a fault prediction method of electric vehicle charging piles based on cooperative game strategy and dung beetle optimization algorithm-bidirectional long-term and short-term memory network-attention mechanism (DBO-BiLSTM-Attention) is proposed. Firstly, abnormal values are processed through parameter statistical distribution, missing values are processed through mean imputation, and the processed data is normalized. Secondly, from different perspectives, subjective evaluation methods such as analytic hierarchy process, objective evaluation method CRITIC weighting method, and machine learning algorithm random forest are selected to calculate feature weights in sequence. Cooperative game strategy is used to combine the above feature weights to obtain new feature weights, and the parameter feature matrix is enlarged. Then, the beetle optimization algorithm and attention mechanism were introduced separately to build the DBO BiLSTM Attention model. Under simulation experiments, the accuracy and F1 coefficient of the training and testing sets of this model were 0.89, 0.89, 0.90, and 0.90, respectively. Finally, relevant comparative experiments were conducted, and the results showed that compared to the model without feature amplification, the accuracy and F1 coefficient of the test set were improved by 5% and 6%, respectively; Compared with the model that does not adopt cooperative game strategy, the accuracy and F1 coefficient of the test set have been improved by 2% and 3% respectively, verifying the effectiveness and rationality of the proposed model.

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