基于Transformer神经网络的锂电池热失控多数据融合探测

Thermal runaway multi-data fusion detection of lithium battery based on Transformer neural network

为满足对锂离子电池热失控高效准确探测的需求,设计了一种锂电池热失控试验平台,并利用STM32F103ZET6单片机连接了CO、CO_(2)、H2和热敏电阻NTC共4种传感器,实时采集特征参量。同时,利用PyroSim模拟试验环境,生成高质量的模拟数据,以补充试验数据。基于PyTorch平台,设计了一个Transformer神经网络,能够输出锂电池的正常、预警和热失控3种状态。通过使用试验数据和模拟数据进行训练,实现了对锂电池热失控的融合探测,相比于其他算法有一定的优势。

In order to meet the demand for efficient and accurate detection of lithium ion battery thermal runaway,this study designed a lithium battery thermal runaway experimental platform.STM32F103ZET6 single chip microcomputer was used to connect four sensors such as carbon monoxide,carbon dioxide,hydrogen and NTC to collect characteristic parameters in real time.At the same time,PyroSim is used to simulate the experimental environment and generate high-quality simulation data to supplement the experimental data.Based on the pytorch platform,we designed a Transformer neural network that can output the normal,early warning and thermal runaway states of lithium batteries.By using experimental data and simulation data for training,we successfully achieved fusion detection of thermal runaway data of lithium batteries,and verified the effectiveness of the algorithm.