Search

The dual-carbon strategy advocates a green, environmentally friendly, and low-carbon lifestyle. In the field of transportation, electric vehicles (EVs) have been regarded as an effective solution to reduce carbon emissions and to conserve energy. Developing a reasonable charging guidance scheme for users is a feasible way to solve problems, such as the range anxiety of EV users, and has a great application value for the promotion of EVs in the future. In practical situations, how to develop charging induction schemes for users that better meet their needs according to the type of user and their multi-dimensional preferences is the focus of this paper. To this end, this study utilized charging behavioral data to investigate the multi-dimensional charging preference of users based on the collaborative filtering algorithm. Then, a multi-objective optimization model was established based on the preference degree of each charging station and the integrated travel cost. An NSGA-III framework was used to design the algorithm to solve the proposed model. The algorithm was tested using simulation experiments that were designed based on the road network and charging stations in Beijing. The final result is an experimental analysis of the weight matrices for the three different preferences of minimum energy consumption cost, minimum time cost, and minimum fee cost, which yields a difference of about 4.4% between the optimal energy consumption cost and the maximum energy cost, about 2.9% between the optimal time cost and the maximum time cost, and about 10% between the optimal fee cost and the maximum fee cost under these three different preferences, respectively. The proposed multi-objective optimization model is able to provide users with reliable charging station selection by incorporating their personalized charging preference characteristics and charge guidance schemes. [ABSTRACT FROM AUTHOR]

Abstract Battery electric vehicles (BEVs) have positive effects on the reduction of petroleum dependence and vehicle emissions. However, limited driving range of BEVs contributes to the range anxiety of drivers. Therefore, accurately estimating remaining driving range is a critical issue for BEV manufacturers to help drivers alleviating range anxiety. In this paper, by using the real-world data collected from a BEV operating in Beijing, China, the nonlinear estimation models for remaining driving range under different temperature conditions are established based on the data-driven method. The models consider the State of Charge (SOC), speed and temperature conditions as the impacting factors for remaining driving range. The significant nonlinear relationship between speed and driving distance per SOC is explored and considered in the model. The robust nonlinear regression method is used to determine the parameters of the models. Model verification results confirm the accuracy of the model. Moreover, the models are used to explore the economical driving speeds for the BEV under different temperature conditions. The results indicate that the economical driving speeds have an increasing trend as temperature increases. The economical driving speeds under low, moderate and high temperate conditions equal to 48.97 km/h, 50.89 km/h and 51.37 km/h, respectively. Highlights • The data from Beijing are used to model the remaining driving range of BEVs. • Energy consumption under different speeds and temperature conditions is explored. • Robust nonlinear regression method is adopted to establish the models. • Economical driving speeds are explored by using the models. [ABSTRACT FROM AUTHOR]