Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle
Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle
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工程车辆液压混合动力系统的递阶模型预测控制
DOI：
10.3390/app10030745
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发表时间：
2020-01
期刊：
影响因子：
--
通讯作者：
Xiaohong Jiao
中科院分区：
文献类型：
--
作者：
Zhong Wang;Xiaohong Jiao
混合液压技术具有功率密度高、价格低廉的优点，在工程机械中表现出良好的适应性。复杂的混合动力系统架构需要优化和管理功率需求分配，并准确响应电源子系统的所需功率分配，以实现油耗、驾驶性能、部件寿命和废气排放方面的目标性能。对于工程机械中使用的混合动力液压车辆（HHV）来说，面临的挑战是考虑到液压蓄能器能量密度相对较低、负载变化频繁、行驶条件的随机性以及发动机动力学的不确定性，设计合适的控制方案以真正实现燃油经济性的改善。为了提高燃油经济性和各种工况对在线能量管理的适应性，增强发动机对期望扭矩的响应性能，本文以喷漆工程车辆为例，提出了一种分层模型预测控制（MPC）方案。上层是基于随机MPC（SMPC）的能量管理控制策略（EMS），下层是带有柴油机扰动估计器的基于MPC的跟踪控制器。在上层管理的SMPC-EMS中，利用实际工程车辆的行驶工况数据建立马尔可夫模型，预测有限后退范围内的未来扭矩需求，以应对行驶工况的随机性。提出了多阶段随机优化问题，并使用基于场景的枚举方法来求解随机优化问题并进行在线实施。在下层跟踪控制器中，设计了扰动估计器来处理发动机的不确定性，并引入MPC来保证发动机输出扭矩对于上层SMPC-EMS分配的扭矩的跟踪性能，从而真正实现柴油机的高效率。通过在两种实际驾驶条件下与几种现有策略进行比较，使用仿真 MATLAB/Simulink 和实验测试平台对所提出的策略进行了评估。结果表明，在仿真和实验中，与由基于规则（RB）管理策略和发动机比例积分微分（PID）控制器组成的控制策略（RB+PID）相比，所提出的策略（SMPC+MPC）平均每加仑英里数分别提高了 7.3% 和 5.9%。
Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively.
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基于模型预测控制的重型商用车液压轮毂电机辅助系统动态协调策略
影响因子：
8.4
作者：
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通讯作者：
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DOI：
10.1109/acc.2013.6579870
发表时间：
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期刊：
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影响因子：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/icma.2011.5986284
发表时间：
2011-08
期刊：
2011 IEEE International Conference on Mechatronics and Automation
影响因子：
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作者：
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通讯作者：
Daiwei Feng;Dagui Huang;Dinggen Li
DOI：
10.1115/1.4032742
发表时间：
2016-05
影响因子：
1.7
作者：
R. Hippalgaonkar;M. Ivantysynova
通讯作者：
R. Hippalgaonkar;M. Ivantysynova