Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks

Gelareh Javid, Djaffar Ould Abdeslam and Michel Basset
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Gelareh Javid: IRIMAS Laboratory, University of Haute Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
Djaffar Ould Abdeslam: IRIMAS Laboratory, University of Haute Alsace, 61 rue Albert Camus, 68093 Mulhouse, France
Michel Basset: IRIMAS Laboratory, University of Haute Alsace, 61 rue Albert Camus, 68093 Mulhouse, France

Energies, 2021, vol. 14, issue 3, 1-14

Abstract: The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.

Keywords: electric vehicles (EVs); Lithium-ion (Li-ion); state of charge (SOC); Recurrent Neural Network (RNN); Long Short-Term memory (LSTM); robust and adaptive online gradient learning method (RoAdam); robust adaptive online LSTM (RoLSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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