Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.

SEGMENTIZED OPTIMIZATION STRATEGY FOR PREDICTIVE CONTROL

To improve the computational efficieney of optimization based control methods, a new kind of Segmentized Optimization Strategy is presented,aiming at achieving more economical computation as well as comparatively satisfactory performance. Its profitability is examined. And the effectiveaess is shown in the simulation.

Control strategy optimization using dynamic programming method for synergic electric system on hybrid electric vehicle

Dynamic Programming (DP) algorithm is used to find the optimal trajectories under Beijing cycle for the power management of synergic electric system (SES) which is composed of battery and super capacitor. Feasible rules are derived from analyzing the optimal trajectories, and it has the highest contribution to Hybrid Electric Vehicle (HEV). The methods of how to get the best performance is also educed. Using the new Rule-based power management strat-egy adopted from the optimal results, it is easy to demonstrate the effectiveness of the new strategy in further improvement of the fuel economy by the synergic hybrid system.

Stabilizing model predictive control scheme for piecewise affine systems with maximal positively invariant terminal set

An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Real-time microgrid economic dispatch based on model predictive control strategy

To deal with uncertainties of renewable energy,demand and price signals in real-time microgrid operation,this paper proposes a model predictive control strategy for microgrid economic dispatch, where hourly schedule is constantly optimized according to the current system state and latest forecast information. Moreover, implicit network topology of the microgrid and corresponding power flow constraints are considered, which leads to a mixed integer nonlinear optimal power flow problem. Given the non-convexity feature of the original problem, the technique of conic programming is applied to efficiently crack the nut. Simulation results from a reconstructed IEEE-33 bus system and comparisons with the routine day-ahead microgrid schedule sufficiently substantiate the effectiveness of the proposed MPC strategy and the conic programming method.

Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus(PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based(RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based(DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge(SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming(DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm(GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

A Model Predictive Control for Microgrids Considering Battery Aging

The increasing number of distributed energy resources(DERs),advancing communication and computation technologies,and reliability concerns of the customers have caused an intense interest in the concept of microgrid.Although DERs are the biggest motivation of the microgrids due to their intermittent generation characteristics,they constitute a risk for system reliability.Battery storage systems(BSSs)stand as one of the most effective solutions for this reliability problem.However,the inappropriate use of BSS creates other operational problems in power systems.In order to deal with these concerns explicitly in microgrids,an optimized microgrid central controller(MGCC)is the key factor,which controls the realtime operation of a microgrid.This work proposes a model predictive control(MPC)based MGCC that will provide optimal control of the microgrid,considering economic and operational constraints.The proposed system will minimize the energy cost of the microgrid by utilizing mixed-integer linear programming(MILP)assuming the presence of DERs and BSS as well as the bi-directional grid connection.Moreover,the aging effect of BSS will be considered in the proposed optimization problem which will provide an up-to-date system model.The proposed method is evaluated using real load and photovoltaic(PV)generation data.

Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity

This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

跨临界CO_(2)循环系统控制优化策略的研究进展

控制策略作为跨临界CO_(2)循环系统的重要组成部分,是保证系统高效节能运行的关键。介绍了系统最优排气压力经验计算和泊金汉π定理的反馈控制、基于梯度追踪和极值寻优的实时在线控制以及基于神经网络的预测控制等,详细分析了系统控制策略的发展历程和未来发展趋势,并总结如下:离线控制建立简单、成本低,但易受到环境因素和系统部件变化的影响而导致控制性能降低;实时在线控制策略可以实时追踪系统最大能源效率对应的排气压力,但由于寻优过程费时较长,导致控制系统的收敛时间过长;模型预测控制系统可以实现实时优化和快速收敛,有着良好的发展前景。结合新能源汽车、建筑供暖、轨道交通、商超冷藏、军工等实际场景对跨临界CO_(2)循环系统控制策略的应用特点和未来发展趋势进行分析,进一步说明了提高控制策略的适用性是未来研究的重要方向,并分析将广义预测控制、强化学习等具有自适应属性的方法应用于跨临界CO_(2)循环系统控制策略的可行性,同时探讨了开发适用于大规模循环系统和储能系统控制策略在我国“双碳”背景下的重要意义。

基于DP-MSCAOA算法的梯级水库多目标防洪优化调度研究

为提高梯级水库联合防洪能力,针对不同频率洪水,综合考虑大坝防洪安全和下游防护区防洪安全,以调度期水库最高运行水位最低、下游防洪控制断面最大削峰和下游防护区超额洪量最小为目标,建立梯级水库多目标防洪联合优化调度模型,设计融合动态规划、多策略协同阿基米德优化算法优势的DP-MSCAOA嵌套优化算法,并以资水某梯级水库为例,针对不同频率洪水进行多目标防洪联合优化调度,与常规调度结果和粒子群优化结果进行对比。结果表明,多目标联合优化调度模型削峰和错峰效果更优,验证了多目标联合优化调度模型的适用性及DP-MSCAOA嵌套优化算法的有效性,可为降低洪灾风险、缓解防洪压力提供技术支撑。

模型预测控制技术在婴配乳粉配料优化中应用

为降低婴幼儿配方乳粉生产过程中的营养成分含量波动,本研究基于模型预测控制(MPC)算法和序列二次规划(SQP)算法构建婴幼儿配方乳粉配料优化控制模型,进行配料过程营养成分含量预测、反馈校正和滚动优化。结果表明,通过配料优化控制模型模拟干预100批次乳粉生产过程后,蛋白质、脂肪和碳水化合物含量的标准差分别从5.18、4.91、5.86 g·kg^(-1)降低至1.01、1.22和1.33 g·kg^(-1),验证了配料优化控制模型的有效性。本研究可为实现婴幼儿配方乳粉的配料优化控制提供参考。

基于模型预测控制的水电站系统运行控制策略

模型预测控制是一种有效的水电站系统运行控制策略,为了制定开环最优控制策略,使水电站的水位保持在特定范围内,提出了基于模型预测控制的水电站系统运行控制策略系统模型。为了达到最佳和最有效的性能,对模型预测控制参数进行调整。文中确定了对水电系统进行模型预测控制的权重参数和预测视界长度,对最优控制问题权重参数的几个测试集和不同的预测视界长度进行了仿真和比较。结果表明,使用预测视界长度大于7天的测试集性能最佳,下游流量的变化平稳,可为水电站系统运行控制提供数据支持。

小角度燕尾型同层侧钻水平井分段控水策略

渤海油田高含水低产低效的井逐渐增多,部分区块产量递减快。同层侧钻水平井技术搭配分段控水完井是治理高含水低产低效井的有效手段,但小角度燕尾型同层侧钻水平井应用分段控水完井后未见到控水效果。为此,通过建立理论模型分析发现此类井分段控水失效原理,确定了原井眼的“空腔”是分段控水技术在此类井上适应性差的主控因素。在考虑可行性及经济性等因素后,调整现阶段的钻完井思路,提出了水平段延伸与优化完井管柱配管的综合钻完井改进策略,并对该策略的有效性进行了矿场试验验证。试验井控水效果远超预期,投产164 d仍未见水,无水期累产油高达1.5×10^(4) m^(3)。该策略在目标井控水效果显著的同时,单井建井成本增幅仅为8.8%,为后续类似低产低效井治理提供思路及技术储备。

基于模型预测控制的冰蓄冷空调系统优化控制策略研究

在峰谷电价背景下,冰蓄冷空调系统可以利用谷值电价进行蓄冷,在电价较高时段释放蓄冷量,从而减少峰值用电量。为此,提出一种基于模型预测控制(MPC)的冰蓄冷空调系统优化控制策略,目标是系统能耗成本最低,同时解决传统运行策略不当而导致的冰槽使用效率低下问题。基于参照建筑搭建TRNSYS-Python联合模拟仿真平台,采用极端梯度提升(XGBoost)算法构建建筑未来24h逐时冷负荷预测模型,运用模型预测控制理论,考虑建筑负荷预测偏差,逐时滚动求解冰蓄冷空调系统蓄放冷最优控制策略,在保证全天冷量合理分配的同时,实现峰谷电价背景下系统运行能耗成本最低。与传统规则策略相比,优化控制策略可以针对不同冷负荷需求采取灵活的控制措施,在冷负荷需求较高时,将有限的冷量优先用在电价高峰期,有效规避高额电费支出,降低能耗成本;在冷负荷需求较低时,通过精准预测,仅储存必要冷量,确保冰量在一天中被完全利用,提升冰蓄冷系统效率。在基于设计日负荷100%、75%、50%三种工况下,优化控制策略比传统规则策略分别可节约能耗成本7.95%、12.64%和10.18%。

改进圆形边界限定的永磁同步电机预测控制

基于边界限定的预测控制是一种有效的低开关频率电机控制方法,该文针对该方法应用在永磁同步电机时存在的无法正常启动的现象进行研究,并提出改进方法。首先,对无法启动现象产生的原因进行研究,提出常规方法存在启动扭矩电流极限这一猜想,即在启动转矩电流较大时,所有的电压矢量均无法使电流轨迹进入到边界内,因此电机无法顺利启动。随后,分析不同电压矢量作用下的启动电流轨迹,验证其启动扭矩电流存在极限这一猜想,进而求解出启动扭矩极限的一般形式,并通过仿真验证了该极限值的正确性。针对启动扭矩受限问题,提出一种改进的电压矢量寻优策略,在所有电压矢量均不符合需求时,切换到能够使电流误差最快减小的电压矢量,进而保证闭环调速顺利启动。仿真与实验结果证明了改进方法的有效性,通过对比实验,验证了该方法在低开关频率下的优越性。

风储发电系统作黑启动电源的功率协调优化策略

针对黑启动过程中的功率协调问题,基于模型预测控制(model predictive control,MPC)提出一种优化策略。首先,介绍了风储发电系统作黑启动电源,设计了风储发电系统的出力方式;其次,结合MPC策略设定了风储出力优化策略,构建风储系统预测模型和滚动优化模型;最后,通过算例对不同功率协调优化策略分析,得出所提风储发电系统功率协调优化策略可以使风电出力有效跟踪火电厂辅机负荷,减小黑启动过程中储能系统的输出功率,进而使储能荷电状态(state of charge,SOC)维持在理想值附近,防止储能SOC越限。

机场航站楼安检资源运行策略优化

为了提升机场航站楼安检资源的利用率及整体的均衡性,结合安检资源运行特性及分布情况提出了基于改进模型预测控制(MPC,model predictive control)的安检资源运行策略优化方法。本文系统性地分析了机场安检流程及运行评估标准并给出优化目标,设计了面向安检队列状态辨识结果的资源运行策略优化框架,考虑到安检资源系统的演化态势及资源约束,构建了面向控制时域的安检资源动态优化模型并生成安检资源运行策略,采用中国某枢纽机场实际运行数据开展仿真验证。结果显示,优化后的队列长度均控制在优化目标范围内,且在旅客到达人数变化时表现出良好的跟踪能力。因此,优化后的安检资源运行策略能够满足离港旅客安检队列状态预测需求,保证了机场航站楼的安全及高质量运行。