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In the electro-pneumatic automated mechanical transmission (AMT) system, the manufacturing, assembly, wear, replacement and other issues often lead to gear position change and some differences in various gears of transmission, which reduces the success rate and even results in abnormally working. To solve these problems, based on the intelligent control theories of fuzzy PI control, this research developed an intelligent self-learning system for the AMT gear position. This system contains the position initialization module after assembling (offline module) and the position correction module in use (online module). The system can automatically recognize gear position deviation and actively correct it, which improves the robustness of shift actuator. The precise control of shift actuator is the key of this self-learning system.

Transmission gear whine noise is one of the main noise problems in powertrain NVH, which is caused by dynamic meshing force of gear pairs, it acts as transmission error. Due to the coupling effects of transmission gears, shaft, bearings and housing, it needs comprehensive management from many aspects to solve the problem of gear whine noise. Aiming at gear whine noise of a 4-speed AMT used in electric bus, the main noise sources is identified by using the order tracking analysis approach firstly. Secondly, gear misalignment and contributions of system deformation to the misalignment is analyzed by means of simulation tools, and the factor is taken into account in the subsequent gear modification design. At last, based on the improved Smith slice method, the calculation model of transmission error of helical gears is established.