This study presents a novel map-based energy consumption prediction model for agricultural electric vehicles operating in real orchard environments. Traditional static models often overlook resistance factors caused by varying terrain and soil conditions. To address this, we introduce an unknown resistance component F u , mapped spatially to reflect local environmental influences such as slope and soil hardness. Field experiments were conducted in a vineyard in Hokkaido, Japan, using GNSS and battery data collected at 10 Hz during uphill and downhill runs. The proposed model achieved a maximum mean absolute percentage error of 2.3%, significantly outperforming conventional models. A notable negative correlation between F u and soil hardness was observed, confirming that softer soils increase vehicle resistance. Simulations of continuous operations across adjacent routes further demonstrated reduced cumulative prediction errors, supporting applications in route planning and battery management. F u (x,y) is currently treated as static, and future work will expand it to a spatiotemporal parameter F u (x,y,t) to incorporate dynamic environmental changes. Online learning and validation across diverse terrains are also planned. This approach enhances model adaptability, offering a reliable tool for energy-efficient and sustainable operation of electric vehicles in agriculture.
Hizatate et al. (Sun,) studied this question.