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Wireless Power Train for Electric Vehicles an optimization framework for efficient solar powered

Project description

Smart charging solutions for the future of electric mobility

One of the main barriers to the widespread adoption of electric vehicles (EVs) is the inconvenience of existing charging systems. Alongside continued dependence on fossil-based energy, this limits progress towards climate-neutral transport. Supported by the Marie Skłodowska-Curie Actions programme, the WPT for EV project is developing a solar-photovoltaic (PV) integrated wireless charging system with smart grid support. To ensure automated and uninterrupted charging, the system combines high-efficiency wireless power transfer with a hybrid evolutionary-reinforcement optimisation approach. Overall, the project will enhance charging performance under variable renewable energy conditions. The solution will help increase EV adoption, reduce emissions, and support EU climate and transport goals.

Objective

Climate neutrality by 2050, as envisioned under the European Green Deal, causes urgent transformations in the transportation sector, which handles nearly 25% of greenhouse gas emissions. The dominance of internal combustion engines must give way to sustainable, fully electric transportation systems. However, adopting electric vehicles (EVs) faces persisting barriers, particularly range anxiety, dependency on conventional energy, reliance on user intervention, and limitations of intermittent renewable power sources.

This project WPT for EV proposes a solar-photovoltaic (PV) integrated wireless EV charger with seamless utility grid support. This system eliminates fossil fuel reliance, ensures uninterrupted charging despite PV intermittency, and mitigates range anxiety through automated wireless operation that requires no user involvement.

The key objectives are to:
Enhance charging efficiency beyond 95%, surpassing existing wireless systems (85–90%).
Eliminate user dependency with automatic wireless charging.
Integrate renewable PV power with grid interconnection for continuous operation.

Support the EU Green Deal, Fit for 55, and AFIF infrastructure goals. The methodology employs a Hybrid Evolutionary-Reinforcement Optimization (HERO) technique. Genetic Algorithms (GA) are used for static design optimisation, such as sizing coils and predicting inverter operating frequencies under varying solar input. Reinforcement Learning (RL) dynamically fine-tunes system parameters, adjusting inductive coil values in real time to maintain resonance and maximise power transfer efficiency. This hybrid optimisation approach ensures robust static performance and adaptive dynamic control to efficiently deliver the required charging power. The proposed WPT for EV chargers offers a highly efficient, user-independent, renewable-powered solution for advancing EV adoption by combining PV integration.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

UNIVERSITETET I SOROST-NORGE
Net EU contribution
€ 267 418,56
Total cost
No data

Partners (1)