Estimation of Surface Temperature Distributions Across an Array of Lithium-Ion Battery Cells Using a Long Short-Term Memory Neural Network 2022-01-0713

As electric vehicles are becoming increasingly popular and necessary for the future mobility needs of civilization, further effort is continually made to improve the efficiency, cost, and safety of the lithium-ion battery packs that power these vehicles. To facilitate these goals, this paper introduces a data driven model to predict a distribution of surface temperatures for a lithium-ion battery pack: a long short-term memory (LSTM) neural network. The LSTM model is trained and validated with lithium-ion cells electrically connected to form a battery pack. Voltage, current, state of charge (SOC), and cell surface temperature from two arrays are used as inputs from a wide range of high and low temperature drive cycles. Additionally, ambient temperature is added as an input to the LSTM model. In summary the LSTM model can accurately characterize and predict a distribution of lithium-ion cell surface temperatures arranged in a battery pack under extreme conditions to an accuracy of 1.53°C. Furthermore, making use of an external sensor to measure the ambient temperature of the battery pack further increases the accuracy of the LSTM model. With this data driven model, fewer testing data is required to validate a battery pack during development, less sensors are needed in production to monitor the health of the battery pack, and data can be generated for a wide variety of applications which may or may not be possible in a lab environment.