Robust Charging Demand Prediction and Charging Network Planning for Heterogeneous Behavior of Electric Vehicles

This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.

An Electricity Demand-Based Planning of Electric Vehicle Charging Infrastructure

The construction of charging infrastructure is an important prerequisite for the development of electric vehicles （EVs）. In this paper, the classification of charging vehicle models and charging infrastructure was firstly summarized, and the optimal charging mode of each type of EV model and the total electicity demand of charging were then analyzed. Combined with the general principle of the development and application of new energy vehicles in the city H, the model of electric vehicle charging infrastructure planning was designed. The case we proposed fully proved the effectiveness of the model.

Optimal charging plan for electric bus considering time-of-day electricity tariff

Purpose–The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day(TOD)electricity tariff,to reduce electricity bill.Design/methodology/approach–Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed,to minimize the electricity costs of daily operation of an electric bus.The charging time is taken as the optimization variable.The TOD electricity tariff is considered,and the energy consumption model is developed based on real operation data.An optimal charging plan provides charging times at bus idle times in operation hours during the whole day(charging time is 0 if the bus is not get charged at idle time)which ensure the regular operation of every trip served by this bus.Findings–The electricity costs of the bus route can be reduced by applying the optimal charging plans.Originality/value–This paper produces a viable option for transit agencies to reduce their operation costs.

Planning and Real-time Pricing of EV Charging Stations Considering Social Welfare and Profitability Balance

This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.