Transportation path planning for hybrid tractor considering the minimum equivalent consumption

Currently, the hybrid electric tractor is more commonly used in agricultural transportation system in the transition from traditional fuel-dependent vehicle to the pure electric vehicle. Because of the complex powertrain system of the hybrid tractor, the economy of driving routes will also be affected. By implementing appropriate adjustments to transportation routes, significant economic benefits can be attained through repeated driving cycle, while resulting in relatively small impact on other factors. Thus, in this paper, a novel tractor transportation path planning strategy is proposed to design the driving route considering the cost. The proposed strategy combines dynamic programming (DP) algorithm and a real-time adaptive equivalent consumption minimization strategy (ECMS), which includes both offline part and online part. In the offline phase, local driving cycles are segmented and optimized through a dual-layer process. First, the grey wolf optimization algorithm (GWOA) determines optimal equivalent factors (EFs) for each segment under varying initial state-of-charge (SOC) conditions, establishing a computationally efficient EF lookup table. Subsequently, the DP algorithm calculates global optimal routes considering both segment-specific energy costs and SOC dynamics. In the online phase, real-time adaptive ECMS utilizes the pre-optimized lookup table to achieve instantaneous energy allocation while maintaining global cost optimality. The proposed strategy is verified both by simulation and hardware-in-the-loop (HIL) tests, which demonstrates a 16.9% reduction in cost for the economically optimal route compared to the time-optimal route while maintaining similar mileage.