Deep Neural Networks for Improved Terminal Voltage and State-of-Charge Estimation of Lithium-Ion Batteries for Traction Applications

Abstract

The growing interest in more electrified vehicles has been pushing the industry and academia to pursue new and more accurate ways to estimate the xEV batteries State-of-Charge (SOC). The battery system still represents one of the many technical barriers that need to be eliminated or reduced to enable the proliferation of more xEV in the market, which in turn can help reduce CO2 emissions. Battery modelling and SOC estimation of Lithium-ion batteries (Li-ion) at a wide temperature range, including negative temperatures, has been a challenge for many engineers. For SOC estimation, several models configurations and approaches were developed and tested as results of this work, including different non-recurrent neural networks, such as Feedforward deep neural networks (FNN) and recurrent neural networks based on long short-term memory recurrent neural networks (LSTM-RNN). The approaches have considerably improved the accuracy presented in the previous state-of-the-art. They have expanded the application throughout five different Li-ion at a wide temperature range, achieving error as low as 0.66% Root Mean Square Error at -10⁰C using an FNN approach and 0.90% using LSTM-RNN. Therefore, the use of deep neural networks developed in this work can increase the potential for xEV application, especially where accuracy at negative temperatures is essential. For Li-ion modelling, a cell model using LSTM-RNN (LSTM-VM) was developed for the first time to estimate the battery cell terminal voltage and is compared against a gated recurrent unit (GRU-VM) approach and a Third-order Equivalent Circuit Model based on Thevenin theorem (ECM). The models were extensively compared for different Li-ion at a wide range of temperature conditions. The LSTM-VM has shown to be more accurate than the two other benchmarks, where could achieve 43 (mV) Root Mean Square Error at -20⁰C, a third when compared to the same situation using ECM. Although the difference between LSTM-VM and GRU-VM is not that steep. Finally, throughout the work, several methods to improve robustness, accuracy and training time have been introduced, including Transfer Learning applied to the development of SOC estimation models, showing great potential to reduce the amount of data necessary to train LSTM-RNN as well as improve its accuracy.