Data-driven framework for large-scale prediction of charging energy in electric vehicles

Yang Zhao, Zhenpo Wang, Zuo-Jun Max Shen and Fengchun Sun

Applied Energy, 2021, vol. 282, issue PB, No S0306261920315798

Abstract: Large-scale and high-precision predictions of the charging energy required for electric vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid-load calculations. However, the complex and dynamic operating conditions of EVs make it challenging to accurately predict the charging energy under real-world conditions, especially for large-scale EV utilization. In this study, a novel data-driven framework for large-scale charging energy predictions is developed by individually controlling the strongly linear and weakly nonlinear contributions. The proposed framework concurrently addresses the overfitting of nonlinear networks using a low proportion of training data as well as the poorly descriptive ability of linear networks under complex environments. For each charging session, the charging energy predictions appropriately account for important factors such as the variations in the state of charge (SOC) of the battery, ambient temperatures, charging rates, and total driving distances. The results suggest that, compared with existing prediction models (such as the random forest, xgboost, and neural network), the proposed framework persists with evidently higher accuracy and stability over a wide range of the ratio between the number of EVs used for testing and training; its mean absolute percentage error (MAPE) is maintained at 2.5–3.8% when the ratio ranges from 0.1 to 1000. The proposed models can be further utilized for cloud-based battery diagnoses and large-scale forecasting of the energy demands of EVs.

Keywords: Charging energy; Large-scale prediction; Machine learning; Electric vehicle (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920315798
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:appene:v:282:y:2021:i:pb:s0306261920315798

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.116175

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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