A Fuzzy Logic Control-Based Adaptive Gear-Shifting Considering Load Variation and Slope Gradient for Multi-Speed Automated Manual Transmission (AMT) Electric Heavy-Duty Commercial Vehicles

The current trend in pure electric heavy-duty commercial vehicles (PEHCVs) is the increasing utilization of automated manual transmission (AMT) to optimize driveline efficiency. However, the existing gear-shift schedule of AMT fails to account for crucial factors such as vehicle load and slope gradient, leading to frequent gear position changes during uphill driving, compromising driving comfort. This study proposes a novel approach incorporating the vehicle’s load and slope gradient to develop an enhanced gear-shift strategy based on fuzzy logic control to address this issue more effectively. Initially, a dynamic gear-shift schedule was formulated for a 6-speed AMT-equipped PEHCV, followed by an analysis of the impact of vehicle load and slope gradient on the gear-shift schedule. Subsequently, an adaptive gear-shift design framework was developed using fuzzy logic control, considering inputs such as acceleration pedal opening, vehicle load, and slope gradient. Simultaneously, the velocity correction factor was designed as an output to adjust the velocity of gear-shift points based on the dynamic gear-shift schedule. Finally, simulations were conducted under various operating scenarios, including different slope gradients, varying vehicle loads, changing pedal openings, and random scenarios to compare and validate the proposed gear-shift schedule against its predecessor—the previous dynamic gear-shift schedule. The results demonstrate that the proposed gear-shift schedule exhibits exceptional adaptability to various driving scenarios. The average acceleration time can be reduced by over 20%, while the gear-shift frequency within 200 s can be decreased by more than 30 times.