Energy consumption prediction of PEVs incorporating traffic flow information.

Yong Chen, Zeyu Song, Ruoyu Chen

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yong ChenMechanical Electrical Engineering School, Beijing Information Science and Technology University - Qinghe Xiaoying Campus, No.12, Xiaoying East Road, Qinghe, Haidian, Beijing, 100192, China. chenyong_jz@126.com.

Zeyu SongMechanical Electrical Engineering School, Beijing Information Science and Technology University - Qinghe Xiaoying Campus, No.12, Xiaoying East Road, Qinghe, Haidian, Beijing, 100192, China.

Ruoyu ChenBeijing Electric Vehicle Co. Ltd, 5 Donghuan Middle Road, Yizhuang Economic Development Zone, Daxing District, Beijing, 100175, China.

Funding

Beijing Laboratory for New Energy Vehicles PXM2021_014224 _000065

6 · The paper itself

Abstract

The problem of range anxiety caused by the discrepancy between the mileage on the dashboard and the driving mileage of pure electric vehicles (PEVs) is one of the most important reasons hindering the development of PEVs. Prediction of energy consumption can effectively reduce the driver's range anxiety and provide support for energy management strategies optimization and energy-efficient route plan. To this end, this paper analyzes the effects of velocity, environment and driving style on energy consumption from the real driving data of PEVs. Based on the analysis, a driving condition prediction method combining with real-time traffic information is proposed, and integrated into the energy consumption prediction model. The real-world trip test results show that the prediction errors of RMSE and MAPE of the proposed method are respectively reduced by 66.37% and 68.10% compared to the conventional method.

Indexed as

Driving condition predictionEnergy consumption predictionReal-time traffic information

Identifiers

PMID40594031

DOI10.1038/s41598-025-05098-7

PMCPMC12216824

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.