Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II:Convolutional neural network and long short-term memory integration

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering high-performance and safe EVs.

Water spatial distribution in polymer electrolyte membrane fuel cell: Convolutional neural network analysis of neutron radiography

Polymer electrolyte membrane(PEM)fuel cells produce water as byproduct,which may cause electrode“flooding”and reduce cell performance.In operation,water usually builds up downstream in the gas flow channel due to the water production by the oxygen reduction reaction(ORR),leading to a water spatial dis-tribution.In this study,a convolutional neural network(CNN)is presented to analyze neutron radiography images to obtain water spatial variation under various operating conditions.5 and 10 segments of a fuel cell are analyzed for spatial variations.Image pre-processing treatments are carried out to improve the convolutional neural network accuracy to 96.6%.The results show that liquid water emerges at a position around 55%downstream for 50%relative humidity while the entire cell is subject to two-phase flow for 100%relative hu-midity under a co-flow configuration.Large water content is present in most of the segments and the near-outlet segment for the counter-flow and co-flow configurations,respectively.In addition,the quad-serpentine cell exhibits more water accumulation than the single serpentine one in most downstream segments.The convolu-tional neural network results agree well with the data obtained from a pixelation image processing method with an accuracy of 91.8%.Compared with conventional pixelation methods,the convolutional neural network method performs better in speed for high-resolution images.It also shows that the current CNN tool fails to predict local water for small spatial scales,such as 10 segments,which leads to a large error(>27%)in prediction.

Deep learning from three-dimensional lithium-ion battery multiphysics model part I:Data development

Fast growing demands for electric vehicles require better longevity,safety and reliability for next-generation high-energy battery technologies.A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics.Nowadays,Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance,health and safety,but is hurdled by a scarcity of data.To mitigate this issue,this study presents one of the first studies for data development through both experimental studies and three-dimensional(3-D)multi-physics modeling to underpin a deep learning framework with indepth examination for battery performance and thermal risk prediction.Specifically,PartⅠfocused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model ac-curacy by two steps:firstly,we validated the multi-physics model against two commercial Lithium-ion batteries,i.e.,Panasonic NCR18650B and 18650BD;Then,the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model,such as voltage evolution and maximum local temperature(hot spot).The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory(CNN-LSTM)in partⅡ.