A data-based model for driving distance estimation of battery electric logistics vehicles

Abstract

Battery electric logistics vehicles (BELVs) reduce transportation costs and air pollution unlike conventional logistics vehicles. However, the limited driving range of BELVs creates new problems for logistics transport. Accurate driving distance estimation of BELVs can help logistics companies determine transport strategies and alleviate the range anxiety of drivers. Based on mass data from BELVs operating in Beijing, China, this study uses a practical and effective data-based modeling method, regression analysis, to establish the data-based model of driving distance estimation. During the modeling process, a nonlinear relation between percentage of energy consumption per kilometer and driving speed is explored based on the experimental data. After determining the model variables, the model frame of driving distance in consideration of driving speed and state of charge is established. The forgetting factor recursive least-squares algorithm is applied to estimate the parameter values of the model. Verification results confirm the feasibility of the model and show that the model errors are small. The proposed model is also used to explore the economical driving speed of BELVs.