车辆低频纵振全工况主动控制算法研究

To optimize vehicle longitudinal vibration which can be a significant source of objectionable vehicle Noise, Vibration and Harshness (NVH) performance is a key issue in modern automotive research. Low frequency vehicle longitudinal vibration which mainly occurs in driving manoeuvres of rapidly acceleration or deceleration, starting, shifting and braking is the fore and aft oscillations in acceleration which is excited by driveline torsional vibration ,and its frequency is lower than 10Hz. The low frequency longitudinal vibration has a bad effect on the drivability and passengers' comfort, and it cannot be eliminated by the normal vibration reduction measure.Considering all these factors, a new active control strategy for engine torque compensation has been proposed to apply to a counterforce to the driveline so as to realize the active suppression of low frequency vehicle longitudinal vibration. Firstly,through comprative study on multi-body dynamics simulation and complete vehicle test,the low frequency vehicel longitudinal vibration mechanism and parameters influence law under different driving manoeuvres can be revealed. Based on this, these different excitation sources,dynamics process and non-linear characteristics which should be considered in model-based control can be studied. Secondly, for multiple excitaions and non-linear hybrid characteristics of driveline system under different driving manoeuvres, the hybrid modeling theory can be used to build the hybrid predicitve model of low frequence longitudinal vibration in all driving manoeuvres in the unified frame.Finally,based on the established hybrid preditive model, applying the preditive control theory of hybrid model and aiming at the optimal target of drivability and comfort, the moving horizon optimization algorithm for engine torque compensation is studied and verified with simulation.This project explores the new approach for controling low frequency vehicle longitudinal vibration in full driving manoeuvres.

随着人们对驾驶性、舒适性、安全性要求的不断提高，车辆NVH问题成为近年来研究的热点。车辆低频纵振是传动系统扭振引起的10Hz以下的纵向加速度波动，主要出现在急加速/减速、起步、换挡、制动等驾驶工况，对驾驶性和舒适性有极大的影响，且无法通过常规减振措施消除。本课题采用发动机转矩补偿控制对传动系统施加"反激励"，实现低频纵振的主动抑制。首先通过多体动力学仿真及整车试验对比研究，揭示各典型驾驶工况下低频纵振产生的机理及参数影响规律，明确基于模型的控制中需要考虑的激励源、动态过程及非线性因素；之后针对不同驾驶工况下传动系统多激励、非线性混杂特征，应用混杂建模理论，在统一的框架下构建全工况低频纵振混杂预测模型；最后基于所建立的混杂预测模型，应用混杂模型预测控制理论，以驾驶性和舒适性为优化目标，构建发动机补偿转矩滚动优化算法，并完成仿真验证。本课题探索了综合解决全工况下低频纵振抑制问题的新途径。

车辆低频纵向振动广泛存在于传统车辆和纯电动车辆的起步、急加速、急减速工况，以及混合动力车辆的模式切换工况，低频振动的抑制很难通过车辆结构及部件的优化实现。本项目建立了详细的车辆纵向低频振动动力学模型，完成了典型工况下低频纵向振动的机理及参数影响规律研究；考虑未来车辆控制系统硬件运行速度的不断提高，构建了具有不同保真度的面向控制的二自由度和三自由度低频纵振预测模型；针对传统车辆和混合动力车辆全工况低频纵振综合控制问题，考虑了离合器结合、分离、间隙穿越的加速过程，以及混合动力车辆多种工作模式，构建了多工况混杂预测模型，为基于模型的车辆低频纵振控制算法研究奠定了良好的基础；基于三自由度模型建立了自适应最优预测车辆纵振控制算法，该算法可以自适应路面的变化，有效的抑制车辆低频纵向振动，且在不同路面上，保证一致的动力性和舒适性；基于所建立的多工况混杂预测模型，开发了车辆低频纵振全工况综合控制算法，为解决包含离散事件及具有非线性特征系统的低频纵振控制问题提供了新思路；考虑发动机转矩输出的延迟特征，建立了线性时变参数的多模型预测控制器，有效的抑制了车辆低频纵振；针对发动机转矩主动补偿控制的实现问题，构建了基于神经元网络的转矩预测算法以及基于发动机燃烧模型的转矩预测算法；构建了基于神经网络模型的舒适性和动态响应性主观评价等级拟合模型，实现了基于客观测试数据的主观评价，避免了人的情绪、外界干扰等因素对主观评价结果的影响，同时也解决了普通客观评价方法与真实的主观感觉存在一定的偏差的问题，为车辆舒适性和动态响应性标定提供了有效的手段。最后通过离线仿真和硬件在环仿真，对控制效果进行有效性验证。验证结果表明，本项目提出的控制算法可以实现典型驾驶工况下低频振动的最优控制，同时对传动系统间隙、路面附着状态变化以及轴系动刚度变化具有良好的适应性。