Using deep learning to predict parameters of batteries on electric vehicles

The batteries used to power electric vehicles have several key characterizing parameters, including voltage, temperature, and state of change (SOC). As battery faults are associated with abnormal fluctuations in these parameters, effectively predicting them is of vital importance to ensure that electric vehicles operate safely and reliably over time. Researchers at the Beijing Institute of Technology, the Beijing Co-Innovation Center for Electric Vehicles and Wayne State University have recently developed a new deep learning-based technique to synchronously predict multiple parameters of battery systems used for electric vehicles. The method they proposed, presented in a paper published in Elsevier's Applied Energy journal, is based on a long short-term memory (LSTM) recurrent neural network; a deep learning architecture that can process both single data points (e.g. "This paper investigates a new deep-learning enabled method to perform accurate synchronous multi-parameter prediction for battery systems using a long short-term memory (LSTM) recurrent neural network," the researchers wrote in their paper.