PHEV由纯电动向混合驱动模式切换协调控制设计

针对并联混合动力汽车模式切换过程中动力中断和扭矩波动对车辆驾驶性能的影响,提出一种由纯电动向混合驱动模式切换的协调控制方法.首先,根据切换过程动力学分析及控制目标,将该过程分为离合器接合前后两部分.然后,对前者设计干扰观测器估计并补偿扭矩干扰和模型不确定性,提出基于干扰补偿的协调控制策略,以消除干扰,实现发动机的快速起动、同步;对后者引入发动机扭矩延迟变量,并利用电机扭矩补偿发动机扭矩误差,设计基于电机补偿的扭矩切换协调控制策略,实现平滑切换.仿真结果表明,该控制策略与传统控制方法相比,冲击度降低50.5%,有效减小了扭矩波动,确保了模式切换的平顺性,提高了驾驶性能.     

基于模型预测的PHEV模式切换协调控制

针对混合动力汽车在模式切换过程中,由于离合器的结合/分离易产生较大冲击度和滑磨功,提出一种模型预测切换协调控制方法。在进行动力学分析基础上,建立预测模型。以电机驱动下的速度响应作为目标转速,构造目标函数,考虑其非可行解问题、动力部件物理约束以及最大冲击度限制要求,引入松弛因子,设计状态约束条件,保证其解的可行性与切换平顺性,实现较小冲击度与滑磨功。同时,引入预测误差,对预测值进行反馈校正,以提高控制精度;将所提策略转化为在线二次规划问题,便于计算机实现。仿真验证结果表明,所提协调控制策略与传统策略相比,冲击度与滑磨功明显降低,保证了切换平顺性,提高了驾驶性能。     

基于MLD-MPC的快速路出口与辅路协调控制

城市交通信号控制系统是典型的混杂控制系统。针对当前大中城市普遍存在的快速路出口匝道、辅路及下游交叉口的信号协调控制问题,研究了一种多信号自适应控制方法,以保证出口匝道排队长度较小且下游交叉口排队不溢流。首先,基于车道路段当量排队长度建立状态方程,分别选取出口匝道、辅路交叉口和下游交叉口排队长度为连续状态变量,信号灯切换信号为离散控制变量;其次,以有限排队长度为模型约束,建立了包含快速路出口匝道、辅路及下游交叉口排队长度和信号状态的混合逻辑动态MLD模型;然后,比较研究了快速路出口匝道有信号灯控、无信号灯控两种不同情况下的MPC模型预测控制方法。仿真结果表明,基于MLD-MPC的控制方法可解决快速路出口匝道与辅路及下游交叉口的多交叉口信号协调控制问题。     

汽车传动系统平顺性驾驶品质控制

汽车驾驶性能是现代汽车的主要性能之一,与汽车动力性、乘员舒适性有很大关系.发动机、变速器的扭矩输出,离合器的分离接合过程等对汽车平顺性都有很大影响.本文介绍了汽车传动系统各组成部分工作原理及其动力学模型,并对影响汽车平顺性的起步控制、换挡控制、急加减速控制、发动机启停控制和间隙补偿控制及其控制算法进行了详细阐述.最后总结了汽车传动系统及其控制策略并介绍近段时间内汽车的研究热点,包括汽车电动化所引起的协调控制问题及智能化对驾驶性能控制的影响等.     

基于Q-学习算法的SAS与EPS协调控制仿真研究

为提高汽车的行驶平顺性和转向稳定性,用Matlab/simulink平台建立了SAS(半主动悬架)与EPS(电动助力转向)的集成模型,并与Carsim整车动力学模型结合,建立联合仿真模型;在此基础上,提出了一种基于Q一学习的协调控制方法,在给定转向工况范围情况下,通过Q一学习算法获得的Q值,将其用于控制器设计,实现对SAS与EPS两个子系统进行协调控制;通过比对仿真结果中的车身横摆角速度和车身侧倾角等性能参数表明:采用Q-学习协调控制方法比常规的集成控制方法有效地降低了车身横摆角速度和车身侧倾角,更好地优化两者之间的匹配关系,使汽车行驶的平顺性和转向的稳定性得到了有效的改善,从而提高了整车的安全性能.     

电控汽车自动变速器换挡系统设计

为了提升电控汽车在换挡过程中的顺畅度及舒适性，实现自动变速的换挡控制功能，分析了换挡质量的衡量指标，构建车辆传动系统中核心部件发动机、离合器、变速器的动力学模型以及车辆阻力模型，详细设计了自动变速换挡控制系统的转速控制模块、变速执行模块以及动态抗扰动模块。利用PID控制实现发动机的转速调整，利用选换挡电机实现变速控制，采用动态滑膜算法提升系统抗扰动能力。经过实车验证，系统在升档过程中换挡耗时1.15 s,车辆冲击度在4 m/s³以内，耗时短、冲击小，达到了良好的换挡控制效果。     

ISG混合动力汽车制动力动态协调控制策略研究

本文针对混合动力汽车的制动过程能量回馈效率不足进行分析.以ISG技术的中度混合动力汽车为平台,通过分析混合动力汽车前、后轮制动力分配以及摩擦制动力和再生制动力的合理分配,建立了基于制动安全性和高效制动能量回收的动态协调再生制动控制策略模型,并在不同的路况下进行仿真分析和修正.仿真结果表明,制动力动态协调控制策略与原有传...     

基于递归神经网络模型预测控制的模型平稳切换

不同生产条件下的控制系统可视多模型控制系统,但多模型控制在模型切换时会引起系统的瞬态响应。采用递归神经网络建立系统的多个模型,基于模型预测控制进行控制模型切换,克服了模型切换时引起的系统瞬态响应,实现系统的平稳切换。并通过仿真表明这种切换策略明显改善了模型切换过程的动态性能。     

矿井主要通风机切换过程供给风量优化控制研究

矿井主要通风机切换过程中存在供给风量大范围波动甚至中断问题,易导致瓦斯浓度超限。常用的基于模型的主要通风机切换控制方法难以很好地对约束进行处理,基于智能算法的控制方法依赖专家知识和经验,具有主观性和随意性。针对上述问题,以中国平煤能源化工集团有限责任公司二矿主要通风机切换过程为研究背景,研究了主要通风机切换过程供给风量优化控制问题。基于流体动力学方程和图论概念,建立了主要通风机切换过程动态模型,并采用泰勒展开式对其进行线性化处理,以降低计算复杂度。考虑矿井主要通风机切换过程具有强非线性、系统状态受约束,且为离散的多输入多输出系统,采用模型预测控制（MPC）算法研究风量控制问题,设计了主要通风机切换过程MPC系统,将风量优化转换为二次规划问题。采用原对偶神经网络在线求解优化问题,实现主要通风机切换过程的供给风量实时优化控制。试验结果表明：MPC系统可有效实现主要通风机切换,切换过程中4个风门风量可很好地跟踪参考值;各采样时刻运算时间为0.027 s,满足切换过程实时性要求;供给风量波动最大值仅为0.9%,明显优于传统PID控制效果。     

并联新能源汽车最优效率实时控制

为了提高并联混合动力汽车驱动系统的实时效率,降低燃油消耗,本文提出一种基于效率最优的协调控制策略.根据不同驱动模式下电池的充放电状态,建立了充放电状态下驱动系统的等效燃油消耗模型,在分析电池效率和发动机效率的基础上,得到驱动系统效率的统一表达式,进而通过建立不同功率需求不同荷电状态下系统最优效率的功率分配系数图谱,设计了系统效率最优的协调控制策略,协调控制策略根据优化的功率分配系数在发动机和电机间进行力矩分配,协调控制策略可以离线计算并实时执行.两种工况循环下的仿真结果表明效率最优控制策略能有效地提高混合动力系统实时效率和燃油经济性.     

Simulation and control of hybrid electric vehicles

This research develops a typical model for a parallel hybrid electric vehicle. Model predictive controllers and simulations for this model have been built to verify the ability of the system to control the speeds and to handle the transitional period for the clutch engagement from the pure electrical driving to the hybrid driving. If the output constraints are the measured speeds and the unmeasured torques which are not strictly imposed and can be violated somewhat during the clutch engagements, a modified model predictive controller with soften output constraints has been developed. Simulations show that the new model predictive controller can control the speeds very well for rapid clutch engagements, which enhance the driving comfort and reduce the jerk on the parallel hybrid electric vehicles.     

Hybrid optimal control of dry clutch engagement

Lately, with the increasing use of automated manual transmissions (AMT) the engagement control of the dry clutch becomes more important. The engagement control plays a crucial role, since different and conflicting objectives have to be satisfied: preservation of driver comfort, fast engagement and small friction losses. In this paper two optimal control strategies for clutch engagement, based on hybrid control principles, are compared. For developing a useful clutch control scheme, the driveline is modelled as a piecewise linear system. The first control strategy is widely known as explicit MPC. However, it seems that it is not suitable (yet) for this type of problem. The second strategy is a piecewise LQ controller, based on piecewise quadratic Lyapunov functions. Simulation results obtained with both strategies are presented and discussed.     

A hybrid model predictive control strategy for nonlinear plant-wide control

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.     

MPC-based torque control of permanent magnet synchronous motor for electric vehicles via switching optimization

In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming (DP) algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.     

An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems considering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter-varying MPC computes the optimal trajectory in closed-loop by iteratively linearizing the system along the previous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon, while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach.     

插电式混合动力汽车车速预测及整车控制策略

本文针对插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)这一典型混杂系统,提出了一种基于车速预测的混合逻辑动态(mixed logical dynamical,MLD)模型预测控制策略.首先,通过对发动机和电动机能量消耗模型进行线性化,建立双轴并联插电式混合动力城市公交车的动力传动系统数学模型;其次,运用模糊推理进行驾驶意图分析,提出基于驾驶意图识别和历史车速数据相结合的非线性自回归(nonlinear auto-regressive models,NAR)神经网络车速预测方法进行未来行驶工况预测.然后,以最小等效燃油消耗为目标建立PHEV的混合逻辑动态模型,运用预测控制思想对车速预测时域内最优电机转矩控制序列进行求解.最后,通过仿真实验验证了本文所提出控制策略在特定的循环工况下与电动助力策略相比,能够提高燃油经济性.     

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

A dual closed‐loop tracking control is proposed for a wheeled mobile robot based on active disturbance rejection control (ADRC) and model predictive control (MPC). In the inner loop system, the ADRC scheme with an extended state observer (ESO) is proposed to estimate and compensate external disturbances. In the outer loop system, the MPC strategy is developed to generate a desired velocity for the inner loop dynamic system subject to a diamond‐shaped input constraint. Both effectiveness and stability analysis are given for the ESO and the dual closed‐loop system, respectively. Simulation results demonstrate the performances of the proposed control scheme.     

电流型变流器的改进模型预测控制

传统的模型预测控制(MPC)采用脉冲响应的非参数模型作为系统的预测控制模型,计算量大,很难直接应用于实时控制系统.本文提出将传统的MPC进行改进,应用到电流型变流器(CSC)功率因数校正系统中.改进的MPC根据CSC控制量与被控制量的传递函数得出CSC的一阶差分方程作为预测控制模型,同时保留传统MPC反馈校正、动态优化等优点.将此改进的MPC与传统的PID控制进行对比研究,结果表明:改进的MPC用于电流型变流器,比传统的PID控制具有更好的鲁棒性和更快速的动态响应特性.