Reliable and Secure X2V Energy Trading Framework for Highly Dynamic Connected Electric Vehicles

Abstract

Vehicle-to-vehicle energy trading has become one of the most popular charge-sharing systems nowadays. It allows energy transfer between electric vehicles without being necessarily relied on infrastructure-based charging stations. However, the demands-offers matching and energy transportation between the vehicles remain challenging issues while considering vehicles' space and temporal location, their dynamicity, availability, and reliability. This paper addresses these issues by proposing a framework of energy trading based on blockchain and smart contracts. The energy transfer between vehicles is performed via a distributed coalition of unmanned aerial vehicles transporting the electric energy from selected sellers to a needy requester vehicle. The selection mechanism of sellers aims to maximize the service availability and fault-tolerance and minimize both the energy transportation latency and overhead. We modeled the selection process by a 0-1 knapsack problem, which we relaxed using a dynamic protocol of energy negotiation, and then developed a linear approach for its resolution. The seller reliability assessment is addressed by the proposition of a trust management approach, which evaluates over time the quality of participants regarding their history of transactions. We conducted intensive simulations with a comparison to the exact solution of resolution. The obtained results show a reduction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$42\%$</tex-math></inline-formula> of charging latency, an improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$24\%$</tex-math></inline-formula> of service availability, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of approximation from the exact resolution, and an increase of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$62\%$</tex-math></inline-formula> of robustness against unfulfilled commitments.