State of Charge Estimation using Recurrent Neural Networks with Long Short-Term Memory for Lithium-Ion Batteries

Title data

Bockrath, S. ; Rosskopf, A. ; Koffel, S. ; Waldhör, Stefan ; Srivastava, K. ; Lorentz, Vincent:
State of Charge Estimation using Recurrent Neural Networks with Long Short-Term Memory for Lithium-Ion Batteries.
2019
Event: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society , Oct. 14–17, 2019 , Lisbon, Portugal.
(Conference item: Conference , Speech with paper )
DOI: https://doi.org/10.1109/IECON.2019.8926815

Project information

Abstract in another language

This paper presents an accurate state of charge (SOC) estimation algorithm using a recurrent neural network with long short-term memory (LSTM) for lithium-ion batteries (LIB) performing under real conditions. With its self-learning ability, this data-driven approach is able to model the highly non-linear behavior of LIB due to changes of environment and working conditions all along the battery lifetime. It is shown that the LSTM approach outperforms common physical-based models using Extended Kalman Filters (EKF) regarding accuracy and stability. To demonstrate this benefit for real-world applications, the provided network is trained and tested with data gathered from commercial industry applications in the domain of energy storage. The LSTM is evaluated and compared with an equivalent circuit model (ECM) using EKF under different working conditions. For dynamic loading profiles, the ECM-EKF achieves an error (RMSE) of 9.5% whereas the LSTM achieves an error (RMSE) of 5.0%.