Research On Guidance Strategies And Optimization For Electric Vehicle Charging Service

Energy crisis and environmental pollution are the major challenges in the present world.The conventional internal combustion engine vehicle-based transportation sector is one of the important industries that contributes to such issues.Thus,as the worldwide consensus,changing traditional transport modes and developing travel patterns of high efficiency,low carbon and sustainability is one of effective means to alleviate energy crisis and environmental pollution.For this reason,electric vehicles(EVs)become a critical technology direction of promoting energy-saving and sustainable transportation,because of their promising energy efficiency and environmental protection effect.In recent years,given the dual drive of policy and market,EVs play an increasing important role in the urban transportation.However,EVs have a shorter driving range as compared to conventional vehicles,which often results in charging behaviors during trips.Meanwhile,insufficient charging infrastructure and long charging time also bring challenges for EV-based travels.In current periods,due to the limited development of battery technology and charging infrastructure construction,the most feasible method to overcome the charging difficulty is to recommend suitable charging and traveling schemes to EV drivers by virtue of charging guidance service.To realize this objective,a core task is to establish the feasible and effective algorithms,models and strategies for charging guidance.Therefore,with the increasing adoption of EVs in the transportation system,how to determine efficient schemes for charging and traveling under limited traffic conditions becomes a critical issue for the current and future urban transportation development.Regarding the charging guidance service as application background,this study explores the EV charging and traveling problems by considering the characteristics of EVs,road network and charging station operation.Combining the charging demands of drivers,the algorithms,models and strategies for charging guidance are proposed in consideration of various complex situations and application environments.Given the current and future developing trends of EVs,the charging guidance methods are developed to satisfy charging and traveling demands of drivers,including the driving direction characteristic-based charging guidance fast algorithm,the multi-period charging situation-oriented charging guidance optimization model,the multi-objective charging guidance optimization model by considering stochastic vehicle operating state,the charging guidance strategies by considering dynamic charging demands in a time-varying road network.The real-time performance,demand diversity,road network randomness and charging demand dynamic are introduced in the charging guidance methods.The simulation experiments are designed to validate the feasibility and effectiveness of the proposed methods.The results would provide decision support to realize charging guidance service.Specifically,the contributions of this study are presented as follows.(1)Considering the charging demand characteristic of EV drivers,a driving direction characteristic-based charging guidance fast algorithm is proposed from the perspective of engineering application.Through the analysis of remaining driving range of EVs,a fast searching method for accessible charging stations is designed in virtue of the geographic research findings.Meanwhile,besides the driving distance,the driving direction characteristic is further considered in charging station selection.The geometric methods are applied in designing a formulation to quantify the consistency of the direction trend between the charging routes and the destination.The simulation experiment results indicate that,the algorithm has a better or identical performance than the conventional shortest-first algorithm in terms of entire travel chains.Furthermore,the computational efficiency of the algorithm is significantly superior to that of shortest-first algorithm and corresponding improved one.(2)Considering the operation characteristic of EVs under long-distance travel,this section proposes a multi-period charging situation-oriented charging guidance optimization model.Based on the characteristics of charging station location,EV driving range and road network structure,a long-distance trip situation-oriented feasible route searching method is established.Given multiple charging events during long-distance EV trips,a multi-period charging programming model for each feasible route is developed.The number of optimization periods equals to the charging station number on the routes.Considering the interaction effects among different charging periods,the original models are transformed into multiple interdependent sub-problems based on the dynamic programming method to realize model transformation and solution.The numerical example results indicate that,the model and solution method have feasibility and effectiveness.Furthermore,the residual energy at destination has significant impacts on optimal charging schemes for the EV long-distance travel.(3)Considering the impacts of actual travel environment on vehicle operating state,combining the characteristic of drivers’ diverse demands,a multi-objective charging guidance optimization model is proposed by considering the stochastic operation state characteristic.The stochastic equations in terms of driving speed and energy consumption is established based on the analysis of their random variation.A robust optimization-based model is proposed to search routes with anti-interference ability.Furthermore,based on the robust driving speed,a multi-objective combinatorial optimization model is built and its optimization objectives include energy consumption,travel time and charging costs.The fuzzy mathematics methods are employed to transform the multiple objective functions into a single objective function.The genetic algorithm and relative comparison approach are used to realize model solution.The numerical example results indicate that,the model and solution methods have feasibility and effectiveness.Meanwhile,the algorithm test results indicate that the genetic algorithm has a good efficiency for the model solution.(4)Given the large-scale EV operating situation,combining the time-varying road network,this section proposes the guidance strategies for charging service by considering dynamic charging demands.The dynamic characteristics of EV charging demands are explored based on the temporal and spatial characteristics of charging demand generation in real-world road network.Combining the influence of large-scale charging demands on charging station operation,a dynamic equation is established to capture the change trends of EV number in charging stations.Furthermore,a charging guidance problem based on dynamic charging demands is proposed.Considering the impacts of large-scale charging demands on charging efficiency and charging station operation,the charging service guidance strategies are established based on drivers’ travel demands and charging station vehicle balance,respectively.A dynamic simulation example is designed to compare and analyze the strategies under different situations and the recommendations for their application under different situations are presented based on the simulation results.