Abstract: This article takes an automated mechanical transmission (AMT) vehicle model in the coal transportation market segment as the research object, and builds a complete vehicle simulation model based on AVL Cruise and SPA software. Through big data analysis of the Internet of Vehicles, user personas are established from the perspectives of roads, vehicles, and people, clarifying user operating conditions and typical gear-shifting scenarios. The optimization direction of gear-shifting performance under different scenarios is analyzed, and optimized gear-shifting sequences and gear-shifting MAP are customized based on users' actual operating scenarios. Gear-shifting calibration parameters are obtained through desktop calibration optimization and economic simulation. The simulation results show that there is no increase in fuel consumption while driving performance scores are improved. Real vehicle testing simulates gear-shifting scenarios for verification. Under constant throttle opening acceleration conditions, the vehicle acceleration time is improved by 3.6%. The gear-shifting strategy meets design expectations and can balance economy and driving performance in typical gear-shifting scenarios.

Key words: commercial vehicle; gear-shifting strategy; shifting strategy; user picture; operational scenarios

摘要： 文章以煤炭运输细分市场的一款自动机械变速器（AMT）车型为研究对象，基于 AVL Cruise 和 SPA 软件搭建整车仿真模型。通过车联网大数据分析，从路、车、人角度建立用户 画像，明确用户工况及典型换挡场景，分析不同场景下的换挡性能优化方向，基于用户实际 运营场景定制优化换挡序列和换挡 MAP，通过桌面标定优化和经济性仿真得到换挡标定参 数，仿真结果表明在驾驶性评分提升的基础上油耗无增加。实车测试模拟换挡场景进行验证， 在定油门开度加速工况下整车加速时间提升 3.6%，换挡策略满足设计预期，可兼顾典型换挡 场景下经济性和驾驶性的平衡。

关键词: 商用车；AMT；换挡策略；用户画像；运营场景