Wójcik Grzegorz, Przystałka Piotr
Department of Fundamentals of Machinery Design, Silesian University of Technology, 18a Konarskiego Street, 44-100 Gliwice, Poland.
DIP Draexlmaier Engineering Polska Sp. z o.o., 44-100 Gliwice, Poland.
Sensors (Basel). 2025 Feb 23;25(5):1369. doi: 10.3390/s25051369.
The rapid growth in the battery electric vehicle (BEV) market has brought lithium-ion battery (LIB) packs to the forefront due to their superior power and energy density properties. However, LIBs are highly susceptible to environmental factors, operating conditions, and manufacturing inconsistencies and operate within a narrow safety operating window. Battery faults pose significant risks, including potentially catastrophic thermal runaway, that can be initiated even by small faults, propagating further into a chain reaction cascade of failures. Aiming to improve the safety of such battery packs, this article presents the developed autoencoder-based fault detection method. The method, enhanced by computational intelligence and machine learning, is a result of extensive research into optical liquid detection systems (OLDSs) for immersion-cooled battery packs, where optical rather than electrical signals are used inside high-voltage areas. The performance was evaluated using recorded real-life datasets under faultless states and under simulated fault states through specific model performance indicators as well as detection performance indicators.
电池电动汽车（BEV）市场的快速增长使锂离子电池（LIB）组因其卓越的功率和能量密度特性而成为焦点。然而，锂离子电池极易受到环境因素、运行条件和制造不一致性的影响，并且在狭窄的安全运行窗口内运行。电池故障会带来重大风险，包括潜在的灾难性热失控，即使是小故障也可能引发热失控，并进一步演变成一连串的故障连锁反应。为了提高此类电池组的安全性，本文提出了一种基于自动编码器的故障检测方法。该方法通过计算智能和机器学习得到增强，是对浸没式冷却电池组的光学液体检测系统（OLDS）进行广泛研究的成果，在高压区域内使用的是光学信号而非电信号。通过特定的模型性能指标以及检测性能指标，使用记录的实际数据集在无故障状态和模拟故障状态下对性能进行了评估。
