Sharing electric vehicle scheduling and service pricing optimization based on battery swapping
With the continuous promotion of the"Internet Plus"strategy and the sharing economy, the sharing electric vehicle travel mode has emerged in the transportation industry.The mode not only enriches transportation supply to meet the private transportation needs, but also reduces carbon emissions and parking demands in the transportation sector.Therefore, it is worthy of development.In practice, sharing electric vehicle mode faces the vehicle imbalance issue.That is, some stations have too many electric vehicles, while others have few.This issue make it hard for customers to find an available vehicle, reduces customer satisfaction and wastes vehicle resources.In addition, due to the short cruising range of electric vehicles, sharing electric vehicle operators need to replenish energy for sharing electric vehicles in the operation process.Relevant studies generally assume that sharing electric vehicles use charging mode to replenish electricity, ignoring the battery swapping mode.Compared with the charging mode, the battery swapping mode take less energy replenishment time, which is more advantageous for the sharing electric vehicle system.Thus, the paper proposes a method to solve the imbalance and energy replenishment issues of sharing electric vehicles based on the battery swapping mode.The specifically considered battery swapping mode is the centralized charging and unified distribution mode proposed by State Grid Corporation of China in 2011.In the mode, all empty batteries are replenished at the centralized charging station near the power plant, which doesn't affect the voltage of the electricity distribution network and thus is grid-friendly.Fully charged batteries will be together distributed to various battery swapping stations where electric vehicles replenish energy.Based on the characteristics of the battery swapping mode, a nonlinear non-convex model with the goal of maximizing profits is firstly constructed to joint optimize battery swapping station locations, battery distribution routes, sharing electric vehicle scheduling including vehicle-passenger match, vehicle relocation and energy replenishment, and travel service prices, as so to address the imbalance and energy replenishment issues of sharing electric vehicles.The nonlinear non-convex model is then transformed into a more easily solvable nonlinear convex model by inverse transformation of the demand-price function for sharing electric vehicles.Based on the nonlinear and decomposable characteristics of the model, secant-based outer approximation algorithm, tangent-based outer approximation algorithm, and Lagrangian relaxation algorithm are chosen to solve the model.The numerical results show that the constructed model is correct; the secant-based outer approximation algorithm is superior to the tangent-based outer approximation algorithm and Lagrangian relaxation algorithm in both solution quality and computation time; joint optimization of battery swapping station locations, battery distribution routes, sharing electric vehicle scheduling, and travel service prices is benefit to sharing electric vehicle operators.The sensitivity analyses of parameters show that the potential demand for sharing electric vehicles and the availability of electricity represented by the battery capacity and the load capacity of the battery distribution truck both have important effects on the profits of sharing electric vehicle operators.
traffic engineeringsharing electric vehiclemix-integer non-linear programming modelbattery swappingouter-approximation algorithm
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