混合动力系统能量管理策略的实时优化控制算法

依据最优控制理论得到的混合动力汽车能量管理策略与未来的驾驶需求相关联,无法解决算法的实时性问题.本文另辟蹊径,结合规则构造二次型性能指标来限制发动机功率的大幅度频繁波动,间接地降低油耗.为此,在对混合动力系统近似线性处理的基础上,利用二次型最优跟踪理论推导出定常的反馈控制律,将发动机和电机功率表示成系统当前状态和车速指令的线性函数并应用于非线性实车系统.仿真结果表明,本文提出的能量管理实时控制算法可以达到良好的节油效果, 对不同的道路工况和电池初始荷电状态有良好的适应性.     

遗传算法在受电弓主动控制器中的应用

为了提高高速列车的受流能力, 降低离网率, 本文以线性二次型最优控制为基础设计了受电弓的主动控制器. 针对线性二次型最优控制中权矩阵Q和R的取值问题, 采用遗传算法进行优化, 通过系统的动态性能指标计算出系统的目标函数并得到权矩阵的最优值, 解决了传统线性二次型最优控制中权矩阵由经验设计所带来的全局最优难实现的问题. 通过仿真分析不同时速下接触网的刚度变化和弓网之间接触压力的参数变化, 本文设计的主动控制器能够很好的减小和控制接触压力的波动, 提高了弓网系统的动态性能指标.     

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.     

伺服系统校正装置的二次型最优频域解

本文利用二次型最优频域解计算得出伺服系统校正装置的最佳参数。它与经典控理论所用的频率法和根轨迹法有原则的区别,是现代控制理论的最优控制设计方法之一。它与二次型性能指标最优控制的传统算法相比较,避免了求解非线性Riccati方程和选择权矩阵Q的困难,为二次型性能指标最优控制在工程上应用提供了一种容易解法。本文用实验结果加以验证。     

基于Hopfield神经网络的双线性离散系统最优控制

将基于二次型性能指标的离散双线性系统最优控制问题转化为动态规划问题，并用Hopfield神经网络（HNN）求解．该方法具有结构简单、易于硬件实现、求解速度快且能求得精确最优解等特点．在复杂系统的实时优化与控制等方面具有广阔的应用前景．     

周期时变线性系统的一般线性二次型最优控制

讨论周期时变线性系统的一般线性二次型最优控制问题, 即状态方程为非齐次方程且二次型性能指标包含线性项的一般情况. 给出了该问题可解的一系列充分必要条件, 同时给出了最优控制的解析构造以及最优性能指标值.     

随动系统的奇异最优控制

控制量受约束条件下的二次型性能指标 xQxdt最优控制问题,是一类奇异最优控制问 题.本文介绍了把奇异最优控制原理用于高精度快速随动系统的分析设计方法.仿真研究与 实时模拟实验的结果证明.其性能优于用传统方法设计的随动系统.     

桥式吊车系统的伪谱最优控制设计

对于桥式吊车系统的最优控制问题,根据实际的工况要求,性能指标有时不一定是标准的二次形式.同时,在实际的控制问题中,状态和控制输入往往会受到一些边界条件和路径过程中的约束.针对这一问题,本文应用Chebyshev伪谱优化算法来处理,它可以处理状态和控制约束的非线性最优化问题以及一个非标准的目标函数.首先对桥式吊车系统模型进行一系列的坐标变换,将其转变为上三角系统形式的误差模型.然后将桥式吊车最优控制问题转化成具有一系列代数约束的参数优化问题,即非线性规划问题.通过求解离散化后的参数优化问题,得到桥式吊车的最优控制律.本文还给出了Chebyshev伪谱最优解的可行性和一致性分析.最后,在仿真研究中验证该控制器的有效性.     

插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.     

基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略

针对一类状态和控制变量均带有时滞的非线性系统的带有二次性能指标函数最优控制问题, 本文提出了一种基于新的迭代自适应动态规划算法的最优控制方案. 通过引进时滞矩阵函数, 应用动态规划理论, 本文获得了最优控制的显式表达式, 然后通过自适应评判技术获得最优控制量. 本文给出了收敛性证明以保证性能指标函数收敛到最优. 为了实现所提出的算法, 本文采用神经网络近似性能指标函数、计算最优控制策略、求解时滞矩阵函数、以及给非线性系统建模. 最后本文给出了两个仿真例子说明所提出的最优策略的有效性.     

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.     

Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a benchmark strategy.     

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-toinfrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industriallevel HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.     

Least costly energy management for series hybrid electric vehicles

Energy management of plug-in hybrid electric vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user – the combined costs of fuel, grid energy and battery degradation – is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign.     

混合动力电动汽车能量管理策略研究综述

能量管理对于提高混合动力电动汽车(Hybrid electric vehicles, HEVs)的燃油经济性、驾驶性能及减少排放具有至关重要的作用.本文对混合动力电动汽车能量管理问题的研究进展及现状进行了全面总结, 从不同角度对混合动力电动汽车的能量管理问题进行描述, 并对主要能量管理策略进行了分析和对比研究, 指出各种控制方法的优点及其存在的问题与不足, 最后对混合动力电动汽车能量管理策略研究的未来发展方向进行了展望.     

混合动力电动汽车的跟车控制与能量管理

混合动力电动汽车(Hybrid electric vehicles,HEVs)的能量管理问题至关重要,而混合动力电动汽车的跟车控制不仅涉及跟车效果与安全性,也影响着能量的高效利用.将HEVs的跟车控制与能量管理相结合,提出一种基于安全距离的HEVs车辆跟踪与能量管理控制方法.首先,考虑坡度、载荷变动建立了HEVs车辆跟...     

Hardware-in-the-loop simulation for the design and verification of the control system of a series–parallel hybrid electric city-bus

Hybrid electric buses have been a promising technology to dramatically lower fuel consumption and carbon dioxide (CO₂) emission, while energy management strategy (EMS) is a critical technology to the improvements in fuel economy for hybrid electric vehicles (HEVs). In this paper, a suboptimal EMS is developed for the real-time control of a series–parallel hybrid electric bus. It is then investigated and verified in a hardware-in-the-loop (HIL) simulation system constructed on PT-LABCAR, a commercial real-time simulator. First, an optimal EMS is obtained via iterative dynamic programming (IDP) by defining a cost function over a specific drive cycle to minimize fuel consumption, as well as to achieve zero battery state-of-charge (SOC) change and to avoid frequent clutch operation. The IDP method can lower the computational burden and improve the accuracy. Second, the suboptimal EMS for real-time control is developed by constructing an Elman neural network (NN) based on the aforementioned optimal EMS, so the real-time suboptimal EMS can be used in the vehicle control unit (VCU) of the hybrid bus. The real VCU is investigated and verified utilizing a HIL simulator in a virtual forward-facing HEV environment consisting of vehicle, driver and driving environment. The simulation results demonstrate that the proposed real-time suboptimal EMS by the neural network can coordinate the overall hybrid powertrain of the hybrid bus to optimize fuel economy over different drive cycles, and the given drive cycles can be tracked while sustaining the battery SOC level.     

The Combination of Two Control Strategies for Series Hybrid Electric Vehicles

With most countries paying attention to the environment protection, hybrid electric vehicles have become a focus of automobile research and development due to the characteristics of energy saving and low emission. Power follower control strategy (PFCS) and DC-link voltage control strategy are two sorts of control strategies for series hybrid electric vehicles (HEVs). Combining those two control strategies is a new idea for control strategy of series hybrid electric vehicles. By tuning essential parameters which are the defined constants under DC-link voltage control and under PFCS, the points of minimum mass of equivalent fuel consumption (EFC) corresponding to a series of variables are marked for worldwide harmonized light vehicles test procedure (WLTP). The fuel economy of series HEVs with the combination control schemes performs better compared with individual control scheme. The results show the effects of the combination control schemes for series HEVs driving in an urban environment.