A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles

Siyi Tao, Bo Jiang (), Xuezhe Wei and Haifeng Dai ()
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Siyi Tao: School of Automotive Studies, Tongji University, Shanghai 201804, China
Bo Jiang: School of Automotive Studies, Tongji University, Shanghai 201804, China
Xuezhe Wei: School of Automotive Studies, Tongji University, Shanghai 201804, China
Haifeng Dai: School of Automotive Studies, Tongji University, Shanghai 201804, China

Energies, 2023, vol. 16, issue 4, 1-17

Abstract: The precise estimation of the state of charge (SOC) is fundamental to the reliable operation of lithium-ion batteries. The development of deep learning techniques makes it possible to employ advanced methods to estimate a battery’s SOC. In order to better utilize a recurrent neural network (RNN) for battery SOC estimation, this paper conducts a comparative study of SOC estimation methods based on different RNN models. First, a general framework for deep-learning-based SOC estimation is undertaken, followed by the description of four kinds of RNNs employed in the estimation. Then, the estimation performances of these RNN models are compared under three scenarios, including the SOC estimation accuracy, the adaptability against different battery aging statuses, and the robustness against measurement uncertainties, in which the estimation performances of different RNN models are quantitively evaluated. Finally, a multiple-criteria decision-making method based on the analytic hierarchy process (AHP) is utilized to reflect the comprehensive performance of each RNN model, and the model with the highest score could be chosen for online SOC estimation during actual applications. This paper provides an in-depth analysis of RNN models in battery SOC estimation and could help battery management engineers develop the most appropriate estimation methods.

Keywords: lithium-ion battery; state of charge; comparative study; recurrent neural network; multi-objective analysis decision method (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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