State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence

Saleh Mohammed Shahriar (), Erphan A. Bhuiyan, Md. Nahiduzzaman, Mominul Ahsan and Julfikar Haider ()
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Saleh Mohammed Shahriar: Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
Erphan A. Bhuiyan: Department of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
Md. Nahiduzzaman: Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
Mominul Ahsan: Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
Julfikar Haider: Department of Engineering, Manchester Metropolitan University, Chester St., Manchester M1 5GD, UK

Energies, 2022, vol. 15, issue 21, 1-26

Abstract: Enhancing the accuracy of the battery state of charge (SOC) estimation is essential in developing more effective, dependable, and convenient electric vehicles. In this paper, a hybrid CNN and gated recurrent unit-long short-term memory (CNN-GRU-LSTM) approach, which is a recurrent neural network (RNN) based model with an explainable artificial intelligence (EAI) was used for the battery SOC estimation, where the cell parameters were explicitly synchronized to the SOC. The complexed link between the monitoring signals related to current, voltage, and temperature, and the battery SOC, was established using the deep learning (DL) technique. A LG 18650HG2 li-ion battery dataset was used for training the model so that the battery was subjected to a dynamic process. Moreover, the data recorded at ambient temperatures of −10 °C, 0 °C, 10 °C, and 25 °C are fed into the method during training. The trained model was subsequently used to estimate the SOC instantaneously on the testing datasets. At first, the training process was carried out with all temperature data to estimate the SOC by the trained model at various ambient temperatures. The proposed approach was capable to encapsulate the relationships on time into the network weights and, as a result, it produced more stable, accurate, and reliable estimations of the SOC, compared to that by some other existing networks. The hybrid model achieved a mean absolute error (MAE) of 0.41% to 1.13% for the −10 °C to 25 °C operating temperatures. The EAI was also utilized to explain the battery SOC model making certain decisions and to find out the significant features responsible for the estimation process.

Keywords: state of charge (SOC); lithium-ion; battery management system (BMS); electric vehicle (EV); deep learning; explainable AI; gated recurrent unit (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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