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.
Highlights
Dependence on petroleum has resulted in serious environmental and energy problems
5 Results and discussion Based on the correlation analysis of actual data, it is observed that state of charge (SOC) and driving speed have significant impacts on driving distance of Battery electric logistics vehicle (BELV)
The other factors regarding battery status, such as battery state of health (SOH) [36], Fig. 8 Relations between driving distance and driving speed under different SOCt values have impacts on driving distance, which will be considered in the future research
Summary
In the field of transportation, battery electric vehicles (BEVs) are utilized to reduce pollution because they do not produce tailpipe emissions during operation. Different from conventional vehicles that use fossil fuel, BEVs convert chemical energy entirely to electricity stored in rechargeable battery packs. BEVs are better than internal combustion engine vehicles in terms of greenhouse gas emissions and energy consumption [1]. Battery electric logistics vehicles (BELVs) can reduce transportation costs and air pollution. The driving range of BELVs is shorter than that of conventional logistics vehicles. Driving range refers to the distance that a fully charged BEV can traverse until the battery runs out of usable electricity [2]. With the recent development of data collection techniques, a large amount of data of vehicle state and battery status of BELVs can be obtained. Establishing a model by using the data is an effective method to realize the accurate driving distance estimation
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