The Library
Li-ion battery temperature estimation based on recurrent neural networks
Tools
Tools
Tools
Jiang, YuHeng, Yu, Yifei, Huang, JianQing, Cai, WeiWei and Marco, James (2021) Li-ion battery temperature estimation based on recurrent neural networks. Science China Technological Sciences, 64 (6). pp. 1335-1344. doi:10.1007/s11431-020-1736-5 ISSN 1674-7321.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1007/s11431-020-1736-5
Abstract
The monitoring of Li-ion battery temperatures is essential to ensure high efficiency and safety. In this work, two types of recurrent neural networks (RNNs), which are long short-term memory-RNN (LSTM-RNN) and gated recurrent unit-RNN (GRU-RNN), are proposed to estimate the surface temperature of 18650 Li-ion batteries during the discharging processes under different ambient temperatures. The datasets acquired from the Prognostics Center of Excellence (PCoE) of NASA are used to train, validate and test the networks. In previous work, temperature has been set as the output of the networks; however, here the temperature difference along the time axis is adopted as the output. The net heat generated results in net temperature change, which is more closely aligned with electrochemical and thermodynamic laws. Extensive simulation results show that the two RNNs can achieve accurate real-time battery temperature estimation. The maximum absolute error in temperature estimation is approximately 0.75°C and the correlation coefficient between the estimated and measured temperature curves is greater than 0.95. The influences of three crucial parameters, which are the number of hidden neurons, initial learning rate and maximum number of iterations, are also assessed in terms of training time, root mean square error and mean absolute error.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Journal or Publication Title: | Science China Technological Sciences | ||||||||
| Publisher: | Springer Verlag | ||||||||
| ISSN: | 1674-7321 | ||||||||
| Official Date: | June 2021 | ||||||||
| Dates: |
|
||||||||
| Volume: | 64 | ||||||||
| Number: | 6 | ||||||||
| Page Range: | pp. 1335-1344 | ||||||||
| DOI: | 10.1007/s11431-020-1736-5 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Is Part Of: | 1 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
