Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network

Renzheng Li, Hui Wang, Haifeng Dai, Jichao Hong, Guangyao Tong and Xinbo Chen

Energy, 2022, vol. 250, issue C

Abstract: Accurate prediction of the state of charge is critical to the safety and durability of battery systems in electric vehicles. This paper proposes a novel multi-step SOC prediction method for real-world battery systems using the gated recurrent unit recurrent neural networks, which fully considers the influences of the environment and driving behaviors on the prediction performance. A novel dual-dropout method is proposed to prevent overfitting and optimize training efficiency. The first dropout is based on Pearson correlation analysis approach. It extracts five actual vehicle parameters that are strong and implicitly correlated with predictive SOC as model inputs, including recorded SOC, pack voltage, vehicle speed, temperature of probe, and brake pedal stroke value. A random dropout function is constructed as the second dropout to decrease the network density and improve efficiency, which is applied to the state information passing process of the model. Furthermore, the training samples are constructed by deriving the yearlong operation data of an electric taxi. The optimal model framework and hyperparameters are discussed and determined. Verified by six sets of randomly selected vehicular operation data, the results show that the proposed method can perform real-time 5-min SOC prediction with maximum error of 0.86%.

Keywords: Electric vehicle; Battery system; State of charge prediction; Gated recurrent unit; Dual-dropout (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222007563
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007563

DOI: 10.1016/j.energy.2022.123853

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().