UC Irvine

Carbon dioxide (CO2) emissions from fossil fuels are a major concern, which is widely regarded as a major cause of climate change. Electrochemical energy systems, such as hydrogen PEM fuel cells and batteries, can potentially address carbon dioxide emissions. PEM fuel cells operate at low temperatures with high power density, making them a promising candidate as the next-generation engine for vehicles, while Lithium-ion (Li-ion) batteries are widely used due to their high energy density, low and falling cost, and long lifetimes. However, major challenges exist in the future development of these electrochemical technologies, such as water management in PEM fuel cells and thermal management of Li-ion batteries.

In PEM fuel cells, improper water management will cause electrode “flooding” and consequently performance loss. To resolve such problems with rapid processing and on-line monitoring, a machine learning approach was developed to analyze neutron radiography images using the convolutional neural network (CNN). The CNN model for water detection in PEM fuel cells was trained on labeled radiography images constructed from a contour legend containing information on the water areal mass density. Image enhancement was carried out to generate additional data for CNN training. The properly trained CNN model is then applied to radiography images to obtain the corresponding average water areal mass density and water spatial variation under various current densities, relative humidity (RH), and flow fields. Compared with conventional pixelation methods, the proposed CNN method performs better in speed for high-resolution images.

In Li-ion batteries, thermal management can significantly impact battery performance, safety, and lifespan in applications. Once the operating temperature exceeds the safety range, Li-ion batteries may subject to fast degradation and safety concerns such as burning and explosion. To improve battery thermal management, the second approach proposed a CNN integrated with a Long Short-Term Memory (LSTM) network to investigate the State of Charge (SOC) and the average temperature of a battery. A physics-based thermally coupled battery (TCB) model was applied to study the physical processes during battery operation and generate the large dataset for the network training. Effects of the heat transfer coefficient, operating temperature, and material thermal conductivities were analyzed for 18650 cylindrical batteries. The TCB model and the machine learning network were validated against both experimental and literature data. The results show that the proposed network achieves better performance compared with CNN and LSTM model.

In addition to water management and thermal management of PEM fuel cells and Li-ion batteries, the developed tools can be used for integration with control algorithms to improve system operation and for image analysis for other energy systems.