首页|基于TimeGAN-GRU的镍镉蓄电池RUL预测

基于TimeGAN-GRU的镍镉蓄电池RUL预测

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镍镉蓄电池广泛用作我国高速列车的辅助电源,其性能可靠性直接关系到高速列车行车安全.蓄电池剩余使用寿命是指其性能从当前状态退化至失效的时长或可充放电次数,是表征电池性能的重要指标.目前,高速列车镉镍蓄电池寿命模型受限于小样本数据而存在精确性和泛化性差的问题.因此,从全新镍镉蓄电池寿命实验数据中提取电池退化特征,采取时序对抗生成网络对其进行增强从而提高数据规模和质量,并依据分类分数、预测分数、主成分分析、t-分布随机邻域嵌入分析方法对增强效果进行评价.其次,使用增强数据建立门控循环单元方法的高速列车镍镉蓄电池剩余使用寿命预测模型.最终,通过四级修镉镍蓄电池循环寿命实验数据进行不同预测起点验证,并对比时序对抗生成网络-门控循环单元融合模型、门控循环单元模型、长短期记忆模型的预测效果.研究结果表明:对于时序对抗生成网络数据增强效果,真实数据与模拟数据分布相近,平均绝对误差小,模拟数据质量较高;经四级修镍镉蓄电池数据验证的时序对抗生成网络-门控循环单元融合模型相比门控循环单元模型、长短期记忆模型,具有更高的泛化性能和预测精度.针对高速列车镍镉蓄电池在小样本数据限制下建立了具有较好精确性和泛化性的剩余寿命预测模型,为保障高速列车行车安全和优化制定检修方案提供了参考.
TimeGAN-GRU method for RUL prediction of Ni-Cd battery
Nickel-cadmium batteries are widely used as auxiliary power supply for high-speed trains in China,and their performance reliability is directly related to the safety of high-speed trains.The remaining service life of the battery refers to its performance from the current state of degradation to the failure of the length of time or the number of times it can be charged and discharged.It is an important indicator to characterize the performance of the battery.The current Ni-Cd battery life model for high-speed trains is limited by small sample data and has the problems of poor accuracy and generalization.Therefore,the battery degradation characteristics were extracted from the new Ni-Cd battery life experimental data,and the time-series adversarial generative network was used to enhance it so as to improve the scale and quality of the data.The enhancement effect was evaluated based on the classification scores,prediction scores,principal component analysis,and t-distributed stochastic neighborhood embedding analysis methods.Second,the prediction model of the remaining service life of Ni-Cd batteries for high-speed trains was established by using the enhanced data with the gated recurrent unit method.Finally,different prediction starting points were verified by the experimental data of cycle life of four-stage repair Ni-Cd batteries,and the prediction effects of the time-sequence adversarial generative network-gated cyclic unit fusion model,the gated cyclic unit model,and the long-and-short-term memory model were compared.The research results show that:for the data enhancement effect of time-series generation adversarial network,the distribution of real data and simulated data is similar,the average absolute error is small,and the simulated data quality is high;and the fusion model of time-series generation adversarial network with gated recurrent unit verified by the data of Ni-Cd battery repaired at the fourth level has higher generalization performance and prediction accuracy compared with the model of gated recurrent unit and the model of long-and-short-term memory.The research results for high-speed train Ni-Cd battery in the small sample data limitations could establish a better accuracy and generalization of the remaining life prediction model for high-speed trains to ensure the safety of high-speed trains and optimize the development of maintenance programs to provide a reference.

batterynickel-cadmium batteryremaining useful life predictiontime-series generative adversarial networkgated recurrent unit

于天剑、杨雨萌、刘海涛、伍珣、代毅、向超群

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中南大学 交通运输工程学院,湖南 长沙 410075

中车株洲电力机车研究所有限公司,湖南 株洲 412001

蓄电池 镍镉蓄电池 剩余寿命预测 时序对抗生成网络 门控循环单元网络

2024

铁道科学与工程学报
中南大学 中国铁道学会

铁道科学与工程学报

CSTPCD北大核心EI
影响因子:0.837
ISSN:1672-7029
年,卷(期):2024.21(12)