Developing high-precision battery electric forklift driving cycle with variable cargo weight

Abstract

Driving cycles are essential for assessing vehicle energy demand, estimating driving range, and evaluating environmental impacts. Numerous driving cycles have been developed for passenger cars and buses. However, tailored driving cycles for logistics vehicles, especially forklifts, remains limited. Therefore, we introduce high-precision driving cycles for battery electric forklifts, which include profiles of velocity and cargo mass. The construction of driving cycles involves route selection, data acquisition, micro-trip segmentation, characteristic parameters selection, driving pattern categorization, transition probability matrix development, and driving cycle construction and evaluation. The methods proposed for constructing driving cycles are based on Markov Chain, Micro-trips combinations, and genetic algorithms. The constructed driving cycles are evaluated using relative error analysis and a simulation model. The results confirm that these cycles accurately reflect actual forklift operations and can be utilized to estimate their energy consumption.