机械式自动变速器的滚动优化控制

Automated Manual Transmission (AMT) has the advantages of high efficiency, energy saving and environmental protection. However, how to guarantee the drivability performance of AMT is a challenging control issue. The control problem of AMT is a constrained multi-variable and multi-objective optimization problem, which is suitable to be solved under the framework of Model Predictive Control (MPC). Driving environment and driving maneuver of automotive drivetrain, however, are complex and time-varying, and bring about large variation of modeling uncertainties for transmission control, which requires that the controller should be adaptive to the changes of driving environment and maneuver. This project focuses on the moving horizon optimal control of AMT in combination with model uncertainty estimation and predictive control, so that the key control problems of AMT in internal-combustion engine automotive, hybrid electric vehicle and pure electric vehicle can be solved. The implementation of this project will improve the drivability performance of AMT, and provide supports for drivetrain control of new-energy automotives.

电控机械式自动变速器（AMT, Automated Manual Transmission）传动效率高、有节能环保优势，却存在平顺性控制难度大的问题。AMT传动系控制属于有系统约束的多目标多变量优化问题，适合在模型预测控制（MPC, Model Predictive Control）框架下加以解决。然而汽车传动系复杂多变的行驶工况和环境给控制带来了大范围变化的不确定和时变因素，这就要求指定的控制算法具有自适应的功能，能够随着工况和环境参数变化而进行自调整。本项目结合模型不确定性估计和预测控制，研究面向汽车传动控制的具有自适应性的滚动优化算法，从而解决内燃机汽车、混合动力汽车以及纯电动汽车中AMT的关键控制问题。通过项目实施将会提高AMT汽车的驾驶平顺性并为新能源汽车提供传动控制方案的有力支撑。

电控机械式自动变速器（AMT, Automated Manual Transmission）传动效率高、有节能环保优势，却存在平顺性控制难度大的问题。利用MPC (Model Predictive Control) 的多变量优化功能和非线性控制功能，解决AMT传动系统有系统约束的多目标多变量优化问题。项目在结合模型不确定性估计和预测控制的框架下展开，具体内容包括面向汽车传动控制的具有自适应性的滚动时域优化算法、内燃机汽车AMT离合器优化控制、混合动力汽车的发动机快速调速控制、纯电动汽车AMT换档扭矩跟踪控制等难度较大的控制问题。仿真和试验结果表明，具有自适应性的滚动优化算法提高了AMT汽车的平顺性品质，同时设计出新型2挡AMT构型，实现了无动力中断换挡。为我国新能源汽车的传动控制方案提供了有力支撑。