State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

By Ephrem Chemali, P. Kollmeyer, Matthias Preindl, Ali Emadi. Journal of Power Sources, 2018

690 citations recorded by the source corpus.

Abstract Accurate State of Charge (SOC) estimation is crucial to ensure the safe and reliable operation of Li-ion batteries, which are increasingly being used in Electric Vehicles (EV), grid-tied load-leveling applications as well as manned and unmanned aerial vehicles to name a few applications. In this paper, a novel approach using Deep Feedforward Neural Networks (DNN) is used for battery SOC estimation where battery measurements are directly mapped to SOC. Training data is generated in the lab by applying drive cycle loads at various ambient temperatures to a Li-ion battery so that the battery is exposed to variable dynamics. The DNN's ability to encode the dependencies in time into the network weights and in the process provide accurate estimates of SOC is presented. Moreover, data recorded at ambient temperatures lying between −20 °C and 25 °C are fed into the DNN during training. Once trained, this single DNN is able to estimate SOC at various ambient temperature conditions. The DNN is validated over many different datasets and achieves a Mean Absolute Error (MAE) of 1.10% over a 25 °C dataset as well as an MAE of 2.17% over a −20 °C dataset.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/97c843f2cd2a02475ef96e9f0679a5e445238d43

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Citations in the corpus

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “In [13], the authors have shown that the FNN is capable of estimating battery SOC at different temperatures, including temperatures as low as − 20 ◦ C.”
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019, background): “[69] Journal of Power Sources Battery State-of-charge estimation”
Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries (2019, background): “The selection of average current and voltage signals as input refers to [24], in which better”