Evidence map›Paper›PMID 41764324›Full record

ArticleScientific reports2026

Transportation-oriented isolated type energy interaction converter for vehicle-to-vehicle.

Wei Jia, Rui Wang, Zhongbao Wei, Chuanyu Jiang, Ziqiang Song

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

5 authors.

Wei JiaNortheastern University, Shenyang, 110819, China.
Rui WangNortheastern University, Shenyang, 110819, China. wangrui@ise.neu.edu.cn.
Zhongbao WeiBeijing Institute of Technology, Beijing, 100081, China.
Chuanyu JiangNortheastern University, Shenyang, 110819, China.
Ziqiang SongNortheastern University, Shenyang, 110819, China.

Funding

the National Key Laboratory of Multi-perch Vehicle Driving Systems QDXT-WY-202407-05
6 · The paper itself

Abstract

In recent years, the environmental pollution problem has become an important issue, and the electric vehicle (EV) is an alternative choice to achieve zero fuel cost and zero pollution. However, the development of EVs is being hindered due to the insufficient number of charging stations and the disadvantage of driving range. To this end, the energy interaction concept is proposed, and an isolated converter for energy interaction is designed in this paper. Firstly, according to requirements regarding the energy interaction converter for EVs, i.e., high-reliability, high-gain, high-power-density, and small-volume, a dual-active-bridge (DAB) converter is employed as the circuit topology, and a standard circuit model is built. The model and phase-shifting scheme of the DAB converter are incorporated and recreated, which produces a customized linear model for the design of the controller. Secondly, using the established circuit model, a robust voltage controller strategy based on uncertainty and disturbance estimator (UDE) was designed to address parameter disturbances, dead time, computation delays, and external disturbances. An integral term was introduced to reduce steady-state errors and improve steady-state tracking accuracy by utilizing the integral accumulation effect, ensuring that the DAB converter provides stable DC voltage/power output. The controller stability analysis is further provided. Finally, the simulation and experiment results demonstrate the feasibility and effectiveness of the proposed method.

Indexed as

Electric vehicleElectrified transportationEnergy interaction converterUncertainty and disturbance estimator

Identifiers

PMID41764324
PMCPMC13057174

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.