ArticleScientific reports2025
Hardware-in-loop implementation of an adaptive MPPT controlled PV-assisted EV charging system with vehicle-to-grid integration.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The penetration of electric vehicles (EVs) into society needs extensive charging infrastructure. The existing charging system solely depends on the grid supply, which is essentially fossil fuel-dependent and leads to carbon emissions and environmental pollution. This can be minimized by incorporating renewable energy into the charging grid. This article presents a charging scheme combining photovoltaic (PV) and grid, offering a clean and dependable charging plan to sustain green transport. The proposed work presents the modelling and controlling a 10 kW EV charging/discharging framework integrating PV and grid. This work has multi-fold objectives: i) the development of an intelligent hybrid maximum power point tracking (MPPT) strategy, ii) the design of a fuzzy logic controlled bidirectional charger, iii) the setup of a PV-grid integrated charging system, and iv) the implementation of vehicle-to-grid (V2G) operation. The proposed charging system utilizes PV power and seamlessly switches to grid power whenever required. Since the performance of the PV source is affected by varying temperatures and irradiance, MPPT methods are needed to extract maximum power from the PV source. This paper developed and compared perturb and observe (P&O), Particle swarm optimization (PSO), and hybrid PSO + Adaptive neuro-fuzzy inference system (ANFIS) based algorithm for MPPT. The findings indicate that the PSO + ANFIS-driven method offers the highest tracking efficiency of 99.5%. This algorithm is also tested under dynamic partial shading conditions (PSC) to ensure robustness, and it led to achieving fast convergence and high efficiency despite multiple power peaks. In addition, the designed bidirectional charging system maximizes solar energy collection, minimizes the charging cost, and improves grid stability through demand balancing. The overall system is validated in a hardware-in-loop real-time environment through FPGA-based OPAL-RT.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.