Electrical lithium-ion battery models based on recurrent neural networks: A holistic approach

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


As an efficient energy storage technology, lithium-ion batteries play a key role in the ongoing electrification of the mobility sector. However, the required model-based design process, including hardware in the loop solutions, demands precise battery models. In this work, an encoder–decoder model framework based on recurrent neural networks is developed and trained directly on unstructured battery data to replace time consuming characterisation tests and thus simplify the modelling process. A manifold pseudo-random bit stream dataset is used for model training and validation. A mean percentage error (MAPE) of 0.30% for the test dataset attests the proposed encoder–decoder model excellent generalisation capabilities. Instead of the recursive one-step prediction prevalent in the literature, the stage-wise trained encoder–decoder framework can instantaneously predict the battery voltage response for 2000 time steps and proves to be 120 times more time-efficient on the test dataset. Accuracy, generalisation capability and time efficiency of the developed battery model enable a potential online anomaly detection, power or range prediction. The fact that, apart from the initial voltage level, the battery model only relies on the current load as input and thus requires no estimated variables such as the state-of-charge (SOC) to predict the voltage response holds the potential of a battery ageing independent LIB modelling based on raw BMS signals. The intrinsically ageing-independent battery model is thus suitable to be used as a digital battery twin in virtual experiments to estimate the unknown battery SOH on purely BMS data basis.


FachzeitschriftJournal of energy storage
PublikationsstatusVeröffentlicht - Feb. 2023

Externe IDs

Scopus 85144470026
Mendeley f3a9103f-2eda-3d3f-8c7a-d23547f432fd


Ziele für nachhaltige Entwicklung


  • Data-based battery model, Encoder–decoder architecture, Gated recurrent unit (GRU), Lithium-ion battery (LIB), Recurrent neural network (RNN)