Estimation of Surface Temperature Distributions Across an Array of Lithium-Ion Battery Cells Using a Long Short-Term Memory Neural Network
2022-01-0845
03/29/2022
- Event
- Content
- 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 one possible type of 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 one array are used as inputs from a wide range of high and low temperature drive cycles. Additionally, a second model is considered where ambient temperature is added as an input to the LSTM model and the resulting predictions are compared to the original 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 three degrees Celsius. 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, it is possible 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.
- Citation
- Campbell, J., ZHU, D., and Cho, G., "Estimation of Surface Temperature Distributions Across an Array of Lithium-Ion Battery Cells Using a Long Short-Term Memory Neural Network," SAE Technical Paper 2022-01-0845, 2022, .