Artificial Neural Network–Based Multisensor Monitoring System for Collision Damage Assessment of Lithium‐Ion Battery Cells,Energy Technology

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Artificial Neural Network–Based Multisensor Monitoring System for Collision Damage Assessment of Lithium‐Ion Battery Cells
Energy Technology ( IF 3.6 ) Pub Date : 2020-03-17 , DOI: 10.1002/ente.202000031
Jian Zhang _{1,

2} , Dian Lv ₁ , Alessandro Simeone ₁

Affiliation

The sales of electric vehicles (EVs) have seen a significant upward trend in recent years. The occurrence of severe collision accidents has raised awareness concerning safety issues of lithium‐ion batteries (LIBs) which are the core components of EV energy storage systems. When collision accidents of vehicles occur, in most cases, the battery cells will immediately burn and even explode, whereas in some cases, despite not directly showing visual damages, the battery cells may lead to delayed catastrophic failures. Herein, the assessment of the battery cells collision damage based on sensor signal data is focused on by identifying potentially unsafe cells. A campaign of collision experimental tests and a series of electrical performance tests are conducted on single‐cell specimens. A cell damage characterization procedure is proposed and implemented on the battery cells after the collision tests. The impact force and z‐axis acceleration signal features and respective damage classes are input to an artificial neural network (ANN) pattern classifier to train a model to assess the battery cell collision damage.

中文翻译：

基于人工神经网络的多传感器监测系统，用于锂离子电池单元碰撞损伤评估

近年来，电动汽车（EV）的销售呈现出显着的上升趋势。严重的碰撞事故的发生已经引起人们对锂离子电池（LIB）安全问题的认识，而锂离子电池是EV储能系统的核心组件。在发生车辆碰撞事故时，大多数情况下，电池会立即燃烧甚至爆炸，而在某些情况下，尽管未直接显示出视觉损坏，但电池可能会导致灾难性故障的延迟。在此，通过识别潜在的不安全电池来集中于基于传感器信号数据的电池单元碰撞损坏的评估。在单电池样品上进行了碰撞实验测试和一系列电气性能测试。提出并在碰撞测试后在电池单元上实施电池单元损伤特征描述程序。冲击力和z轴加速度信号特征和相应的损坏类别被输入到人工神经网络（ANN）模式分类器中，以训练模型来评估电池碰撞的损坏。

更新日期：2020-03-17

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