PROJECT TITLE :
Electric Vehicle Route Optimization Considering Time-of-Use Electricity Price by Learnable Partheno-Genetic Algorithm
In the context of energy saving and carbon emission reduction, the electrical vehicle (EV) has been identified as a promising alternative to ancient fossil fuel-driven vehicles. Thanks to a different refueling manner and driving characteristic, the introduction of EVs to the present logistics system can create a important impact on the vehicle routing and also the associated operation costs. Primarily based on the traveling salesman problem, this paper proposes a replacement optimal EV route model considering the fast-charging and regular-charging under the time-of-use value within the electricity market. The proposed model aims to reduce the entire distribution prices of the EV route whereas satisfying the constraints of battery capability, charging time and delivery/pickup demands, and therefore the impact of auto loading on the unit electricity consumption per mile. To unravel the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of professional data concerning EV charging station and client selection. A comprehensive numerical check is conducted on the thirty six-node and 112-node systems, and also the results verify the feasibility and effectiveness of the proposed model and resolution algorithm.
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