混合动力商用车AMT挡位自学习控制技术优化

Optimization on Self-Learning Control Technology for AMT Shifting Position of Hybrid Commercial Vehicle

在混合动力商用车电控机械式自动变速器(AMT)系统中,因制造、装配、磨损、更换等导致变速器各挡位置存在差异及变化,引起选换挡成功率降低甚至工作异常,需通过AMT挡位自学习解决此问题。针对AMT静态时各挡位位置自学习控制策略提出了优化,主要包括挡位学习顺序和再次进挡学习策略,通过自整定PID技术进行自适应参数优化。经过试验验证,提高了自学习成功率、合格率、效率和一致性。

In the automated mechanical transmisson （AMT） system of hybrid commercial vehicle, affected by manuthcturing, assembly, abration and parts replacement, the shifting position would be different or changed, thus, the shifting would have a low success rate or even cannot work normally. It was necessary to use the AMT shifting position self-learning technology to solve this problem. This paper showed the optimization of the AMT shifting position self learning technology, including changing the order of shifting position learning, shifting-again strategy and PID control technology based on self-tuning parameters. Proved by experiments the optimization can increase the success rate, the pass rate, accuracy and consistency of shifting position.