Li-ion battery temperature estimation based on recurrent neural networks

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 degrees 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.

Automated summary

The study proposes using two types of recurrent neural networks to estimate the surface temperature of lithium-ion batteries under different ambient temperatures, showing that the two RNNs can achieve accurate real-time battery temperature estimation with a maximum temperature estimation error of approximately 0.75 degrees Celsius and a correlation coefficient greater than 0.95 between estimated and measured temperature curves.