CN111891113A - 混合动力车辆的信息物理能量优化控制系统及其控制方法 - Google Patents
混合动力车辆的信息物理能量优化控制系统及其控制方法 Download PDFInfo
- Publication number
- CN111891113A CN111891113A CN202010802712.0A CN202010802712A CN111891113A CN 111891113 A CN111891113 A CN 111891113A CN 202010802712 A CN202010802712 A CN 202010802712A CN 111891113 A CN111891113 A CN 111891113A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- optimization
- information
- optimized
- threshold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 32
- 238000001514 detection method Methods 0.000 claims description 4
- 239000000446 fuel Substances 0.000 abstract description 18
- 230000008569 process Effects 0.000 abstract description 15
- 238000004364 calculation method Methods 0.000 abstract description 4
- 230000000694 effects Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000003915 air pollution Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/20—Control strategies involving selection of hybrid configuration, e.g. selection between series or parallel configuration
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/40—Controlling the engagement or disengagement of prime movers, e.g. for transition between prime movers
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3461—Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2300/00—Indexing codes relating to the type of vehicle
- B60W2300/10—Buses
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/15—Road slope, i.e. the inclination of a road segment in the longitudinal direction
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/406—Traffic density
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
- B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/62—Hybrid vehicles
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Business, Economics & Management (AREA)
- Chemical & Material Sciences (AREA)
- Mechanical Engineering (AREA)
- Transportation (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Combustion & Propulsion (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Analytical Chemistry (AREA)
- Geometry (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Human Computer Interaction (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Hybrid Electric Vehicles (AREA)
Abstract
本发明提供一种混合动力车辆的信息物理能量优化控制系统及其控制方法,包括实现车辆与路况信息采集、HCU阈值优化、阈值无线更新加载的信息层,以及作为物理层的被优化对象PHEB;收集检测车辆的实时位置以及当前位置的道路坡度信息,收集待优化路段反映交通状况的车速信息;用收集的信息来构建用于阈值优化的车辆模型虚拟运行平台,然后借助高效的优化算法进行相关参数的快速寻优,最终获得最佳结果,并在受优化车辆即将行驶到该路段之前,将相应参数发送并加载至其HCU中。本发明减少了优化过程中的运算量,也使得受优化参数更具有针对性,能够更好地改善车辆的燃油经济性。
Description
技术领域
本发明涉及车辆控制技术领域,尤其是涉及一种混合动力车辆的信息物理能量优化控制系统及其控制方法。
背景技术
随着空气污染和燃料短缺等问题的日益严峻,在行驶过程中产生大量尾气且能量转化率较低的传统汽车愈发无法满足社会需求。相比之下,混合动力汽车逐渐成为一种理想的交通工具。究其原因,在于其特殊的动力系统结构,一般情况下,混合动力汽车除装配有一个发动机外,还具备一个或更多的电机作为辅助动力装置来共同驱动车辆的行驶。由于电动装置的引入,使车辆对石油或天然气等不可再生、非清洁能源的需求直接降低。而且,在电机装置的辅助作用下,发动机这一非线性装置能够更好地调整自身的实时工作点,尽可能以一种高效率的状态向外输出动力。由此一来,发动机的能量转化效率也获得了大大改善。
为了能够在保证车辆动力需求的前提下实现上述目的,建立一种控制方法来对不同动力装置进行合理的任务分配十分必要。但这里的“合理”则是需要建立在整车控制器(Hybrid control unit,HCU)具备适当的参数之上的,因此要使车辆实现良好的燃油经济性需要对相关参数进行优化。
图1描述了一种为改善同轴并联混合动力汽车的燃油经济性而使用的能量管理策略优化流程。其中,该车辆使用了一种基于规则控制(Rule-based)的方法来实现车辆内部动力系统的转矩分配。此方法的优越性在于所需运算负荷较小,在当前常规的HCU中便可实现实时的转矩分配方案计算,因此也在其他实际控制问题中被广泛应用。规则控制中最为关键的部分为其规则库,其表现形式为If Input is X,Then Output is Y,直接影响着控制器输入与输出之间的映射关系。由上图可知,此方法中混合动力汽车中HCU的输入为电池的电荷状态SOC和整车需求转矩Td,而输出则为发动机和电机需要分别承担的转矩,即Te和Tm。取车辆动力系统控制过程中的一例对规则控制进行说明:If SOC>X1,Td>X2,Then Te=a(a为预设常量),Tm=Td-Te。其含义为,当控制器的两个输入量为此状态时,会根据其内部预设的规则库对发动机和电机产生相应的控制指令,以满足车辆正常行驶的动力需求。同时,由此规则语句也可知,阈值X1和X2的取值将直接影响控制器的作用效果,即决定了在何时指挥不同动力源以何种状态进行工作。由于动力系统,尤其是发动机的能量转换效率受其实际工作状态的直接影响,所以如果能够通过软件控制使其能够以理想转态运行,将极大改善车辆的燃油经济性。出于此目的,目前在实现混合动力汽车节能降耗目标的过程中,优化基于规则的控制器阈值的手段较为常见。
说明:本研究中HCU阈值受优化的混合动力汽车通常为行驶路线固定的插电式混合动力公交车(Plug-in hybrid electric bus,PHEB),究其原因,是由阈值的优化方法决定的。在优化过程中,需要借助采集的汽车历史行驶工况数据来构建虚拟运行场景,然后结合优化算法选取最佳阈值,最后这些阈值在实车控制中被应用。换句话说,阈值的最优性是依赖于具体工况的,如果路况发生较大变化,之前优化好的阈值在改善汽车燃油经济性的效果上也会大打折扣。另外,受限于传统汽车HCU参数优化步骤的复杂性,如果优化结果不能被重复使用(举极端例:受优化车辆只在该路线行驶一次或几次),该优化操作显然是性价比极低的。
而由公交车行驶路线固定且频繁行驶这一特点可知,其对其HCU阈值进行优化是十分有意义的。而且在采集公交车行驶工况数据的过程中,由于道路坡度是固定的,只需要收集体现交通状况的公交车车速轨迹便可完成搭建上述虚拟运行场景所需材料的准备。而这些数据引申到仿真优化中的作用,主要是通过固定的运算方式,来计算车辆在不同时间内所需转矩Td的变化情况。
上述阈值的优化过程结合模型实现,步骤如下:
1借助车载电子水平仪和CAN卡分别测量和记录受优化路段的坡度及车速变化轨迹等数据。
2利用采集到的工况数据构建用于阈值优化的虚拟行驶场景,并借助以遗传算法为代表的优化算法来选取最佳阈值。在算法内部,将阈值控制下车辆的油耗和评价该值性能的适应度函数建立联系,油耗越低,适应度函数值越高,由此获得阈值进化的方向。
3通过迭代优化,最终获得针对该路况的最佳阈值。然后在不同代表交通流状况的车速轨迹下重复步骤2,最终得到在多种工况下均能使汽车燃油经济性较佳的阈值。(意义:如上文所说,阈值的性能十分依赖于其实际的优化环境,然而在实际情况下没有两个时刻的工况完全相同。因此,为保证阈值优化在不同场景下均能改善汽车燃油经济性的普遍性,采用多场景下阈值总体最优的手段实现这一功能)
4将优化好的阈值人工加载到实际公交车的HCU中,达到降低其油耗的目的。
上述技术存在以下缺点:
1.现有技术需要人工采集用于控制器阈值优化的工况数据,然后进行离线优化,最终再经人工手段装载至实际公交车HCU中。此方法步骤繁琐,且需要人为介入,效率较低。
2.由于上述对公交车HCU阈值进行更换的复杂性,不可能实现根据路况的变化对其进行实时更新。现有技术只能采取事先通过大量路况数据优化来换取能量管理策略在不同环境下均能实现较佳效果的折中方案,此方案不仅造成了沉重的运算负荷,而且获得的策略一般不能使汽车的燃油经济性达到特定情况下的最优。(原因:因为该结果是考虑不同的工况后的折中值,而不是针对这一种工况进行最佳阈值选择的。)
3.以遗传算法为代表的优化算法在完成此类参数优化的过程中,存在优化速度较慢、容易陷入局部最优等问题,有待被解决。
发明内容
本发明提供一种混合动力车辆的信息物理能量优化控制系统及其控制方法,相比于传统方法中繁琐的操作流程,本发明由于合理地借助了无线通讯技术,大大减少了参数优化过程中的人工成本,同时也为及时根据路况情况调整HCU阈值,进而提升车辆的燃油经济性创造了可能。并且这种信息物理优化架构也使得不必借助体量庞大的路况数据来改善控制器在不同环境下的适应性,直接减少了优化操作的时间成本。本发明采用寻优效果更佳的烟花算法对HCU阈值进行优化,大大加快了寻优速度,并有效提高了获得阈值的最优性。
为实现上述目的,本申请采用的技术方案是:
混合动力车辆的信息物理能量优化控制系统,包括实现车辆与路况信息采集、HCU阈值优化、阈值无线更新加载的信息层,以及作为物理层的被优化对象PHEB;
所述的信息层包括:
GPS/GIS:用于检测车辆的实时位置以及当前位置的道路坡度信息;
交通流状况采集装置:主要包括路边车速检测摄像头和与受优化车辆路线相同的车辆,负责采集反映交通状况的车速信息;
远程监控中心:负责收集待优化路段的GPS/GIS、交通流状况采集装置采集的信息来构建用于阈值优化的车辆模型虚拟运行平台,然后借助高效的优化算法进行相关参数的快速寻优,最终获得最佳结果,并在受优化车辆即将行驶到该路段之前,将相应参数发送并加载至其HCU中。
混合动力车辆的信息物理能量优化控制方法,包括以下步骤:
收集检测车辆的实时位置以及当前位置的道路坡度信息,收集待优化路段反映交通状况的车速信息;用收集的信息来构建用于阈值优化的车辆模型虚拟运行平台,然后借助高效的优化算法进行相关参数的快速寻优,最终获得最佳结果,并在受优化车辆即将行驶到该路段之前,将相应参数发送并加载至其HCU中。
优选的,本发明所述的阈值优化采用烟花算法。烟花算法可置换为其他高效寻优算法,能够在短时间内获得改善车辆燃油经济性的较佳控制器参数即可。
本发明规则控制方法可置换为模糊控制,因为这两种控制方法均具有较好的实时性。当使用模糊控制时,受优化的参数多为该控制方法中的隶属度函数,以此来建立控制器输入与输出之间合理的对应关系。
本发明具有技术效果:
信息物理系统的建立带来了一定优势:
信息物理系统下的PHEB可借助GIS、GPS和交通流检测装置等,更加实时准确的获知待优化路段的工况信息。随后,这些信息可用于构建虚拟运行场景来用于HCU阈值的云端优化,最终优化结果将经由无线通讯手段加载至实车HCU中。相比于传统方法,HCU参数的优化工作更显得实时化、便捷化、高效化。
由于上述所描述技术的先进性,使得HCU的阈值优化工作不在需要借助体量庞大的历史工况数据,而是只需特定的路况信息。这一改变不禁减少了优化过程中的运算量,也使得受优化参数更具有针对性,能够更好地改善车辆的燃油经济性。
相比于其他算法,烟花算法具备其自身优化特性:
烟花算法在寻优的过程中,遵循高可能区域重点搜索,低可能区域快速搜索的原则,能够做到利用有限的运算能力和运算时间对全区域位置进行高效筛选。因此,它具有比遗传算法等传统算法更强的寻优能力。
附图说明
图1现有能量管理策略优化流程;
图2是本发明的信息物理能量优化框架;
图3是本发明的HEB动力系统结构;
图4是本发明的HCU的控制逻辑;
图5是本发明的基于烟花算法的HCU阈值优化流程。
具体实施方式
结合实施例说明本发明的具体技术方案。
本实施例拟借助当前迅速发展的智能网联技术(包括V2V,V2I等),针对PHEB提出一种高效的信息物理能量优化控制方法。如图2所示,在混合动力车辆的信息物理能量优化控制系统框架内包含了可实现车辆与路况信息采集、HCU阈值优化、阈值无线更新加载的信息层,以及作为物理层的被优化对象PHEB。其中,信息层的主要组成部分及其功能如下:
GPS/GIS:用于检测车辆的实时位置以及当前位置的道路坡度等信息。
交通流状况采集装置:主要由路边车速检测摄像头和与受优化车辆路线相同的车辆(如图2中与受优化PHEB路线相同的其他公交车)组成,负责采集反映交通状况的车速信息。
远程监控中心:负责收集待优化路段的上述两类信息来构建用于阈值优化的车辆模型虚拟运行平台,随后借助高效的优化算法进行相关参数的快速寻优,最终获得最佳结果,并在受优化车辆即将行驶到该路段之前,将相应参数发送并加载至其HCU中。
而物理层PHEB的动力系统大致结构如图3所示,包括依次连接的发动机1、离合器2、电机3、变速箱4、差速器5,电机3与电池6连接。它与信息层中的不同组成部分以信息基站为纽带,利用无线通信技术进行信息传递。而车辆内部HCU的控制逻辑及其具体规则库分别在图4和表1中被给出,从表1中可知有4个重要阈值SOC_h,SOC_l,Pe_h,Pe_l需要被优化来改善车辆的燃油经济性。另外,结合表中的具体规则可知,改善汽车燃油经济性的前提为保证整车动力性,以及其自身硬件的安全性。
表1规则控制的规则库
当然,在阈值优化的过程中,采用一种高效的优化算法对于提高优化效率也是十分重要的。在本发明,拟采用烟花算法作为HCU中阈值的优化方法,这是一种近年出现的新型智能优化算法。烟花算法模拟了现实生活中烟花燃放的过程,将一定的燃放空间视作参数的取值域,随机产生的火星多维位置坐标作为受优化参数序列的候选值,例如四维的烟花位置(0.7,0.5,150,98)可理解为阈值SOC_h,SOC_l,Pe_h,Pe_l分别取值为0.7,0.5,150,98。它有着分布式、扩散式寻优,高可能区域重点搜索,低可能区域快速搜索的特点,对于车辆控制参数的具体优化流程如图5所示。经过相关仿真实验的验证,烟花算法能够较好的契合信息物理系统下的HCU参数优化工作。
在烟花算法进行参数优化的过程中,有以下几点需要注意:
(1)图5中适应度值这一环节是优化算法与汽车控制问题相联立的关键。首先,在烟花算法内部,适应度值直接评判了该参数的优劣,适应度越高说明该参数越能较好的适合当前的控制工作,而且在迭代进化的过程中越容易被保存下来。而对于车辆控制问题来讲,适应度值是与车辆在相应参数控制下在特定路段的油耗情况直接联系的,二者呈负相关关系。
(2)烟花位置(烟花不止一个)和火星位置对于寻找最佳位置(阈值)这件事上是地位相同的,区别是火星通常是以烟花位置为中心,以一定距离为半径而随机产生,旨在检查烟花位置周围有无更好的坐标点。而火星的产生遵循较佳烟花在较小半径内产生较多火星,而较差烟花在较大半径内产生较少火星的原则。此操作的依据在于参数的性能存在一定连续性,最佳参数有大概率出现在较佳参数附近,因此此举无异于合理地利用了有限的运算能力来快速地展开全局寻优。
(3)当进行下一代烟花位置的筛选时,会遵循精英保留策略的原则,将本代中最好的烟花或火星位置保留下来作为下一代烟花中的一个。同时,为有效避免优化陷入局部最优,其他烟花可在当前已知位置中随机产生。
Claims (3)
1.混合动力车辆的信息物理能量优化控制系统,其特征在于,包括实现车辆与路况信息采集、HCU阈值优化、阈值无线更新加载的信息层,以及作为物理层的被优化对象PHEB;
所述的信息层包括:
GPS/GIS:用于检测车辆的实时位置以及当前位置的道路坡度信息;
交通流状况采集装置:主要包括路边车速检测摄像头和与受优化车辆路线相同的车辆,负责采集反映交通状况的车速信息;
远程监控中心:负责收集待优化路段的GPS/GIS、交通流状况采集装置采集的信息来构建用于阈值优化的车辆模型虚拟运行平台,然后借助高效的优化算法进行相关参数的快速寻优,最终获得最佳结果,并在受优化车辆即将行驶到该路段之前,将相应参数发送并加载至其HCU中。
2.混合动力车辆的信息物理能量优化控制方法,其特征在于,包括以下步骤:
收集检测车辆的实时位置以及当前位置的道路坡度信息,收集待优化路段反映交通状况的车速信息;用收集的信息来构建用于阈值优化的车辆模型虚拟运行平台,然后借助高效的优化算法进行相关参数的快速寻优,最终获得最佳结果,并在受优化车辆即将行驶到该路段之前,将相应参数发送并加载至其HCU中。
3.根据权利要求1所述的混合动力车辆的信息物理能量优化控制方法,其特征在于,所述的阈值优化采用烟花算法。
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010802712.0A CN111891113A (zh) | 2020-08-11 | 2020-08-11 | 混合动力车辆的信息物理能量优化控制系统及其控制方法 |
US17/098,372 US20210107449A1 (en) | 2020-08-11 | 2020-11-14 | Cyber-physical energy optimization control system and control method for hybrid electric vehicle |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010802712.0A CN111891113A (zh) | 2020-08-11 | 2020-08-11 | 混合动力车辆的信息物理能量优化控制系统及其控制方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111891113A true CN111891113A (zh) | 2020-11-06 |
Family
ID=73228983
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010802712.0A Pending CN111891113A (zh) | 2020-08-11 | 2020-08-11 | 混合动力车辆的信息物理能量优化控制系统及其控制方法 |
Country Status (2)
Country | Link |
---|---|
US (1) | US20210107449A1 (zh) |
CN (1) | CN111891113A (zh) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115472013B (zh) * | 2022-09-08 | 2024-05-17 | 江苏大学 | 一种基于虚拟交通场景的新能源汽车能量管理测试平台 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108427985A (zh) * | 2018-01-02 | 2018-08-21 | 北京理工大学 | 一种基于深度强化学习的插电式混合动力车辆能量管理方法 |
CN108446477A (zh) * | 2018-03-14 | 2018-08-24 | 中南大学 | 吸能结构耐撞性能优化方法、系统与可读存储介质 |
CN110281904A (zh) * | 2019-06-18 | 2019-09-27 | 浙江吉利控股集团有限公司 | 一种混合动力车辆的能量管理方法、装置及终端 |
CN110509911A (zh) * | 2019-09-04 | 2019-11-29 | 北京理工大学 | 一种混合动力车辆的控制方法及装置 |
CN110723132A (zh) * | 2019-10-24 | 2020-01-24 | 深圳技术大学 | 一种混合动力车及其控制方法、存储介质 |
CN110929920A (zh) * | 2019-11-05 | 2020-03-27 | 中车戚墅堰机车有限公司 | 一种基于工况识别的混合动力列车能量管理方法 |
CN111277141A (zh) * | 2020-02-29 | 2020-06-12 | 武汉理工大学 | 双向dc/dc变换器优化控制方法 |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009129106A1 (en) * | 2008-04-15 | 2009-10-22 | The Uwm Research Foundation, Inc. | Power management systems and methods in a hybrid vehicle |
KR20140044686A (ko) * | 2012-10-05 | 2014-04-15 | 현대자동차주식회사 | 하이브리드 자동차 및 하이브리드 자동차의 구동 제어 방법 |
KR101619637B1 (ko) * | 2014-11-07 | 2016-05-10 | 현대자동차주식회사 | 하이브리드 차량의 제어방법 |
US10059330B2 (en) * | 2016-09-22 | 2018-08-28 | Toyota Motor Engineering & Manufacturing North America, Inc. | Drafting detection and vehicle operation optimization system |
FR3061471B1 (fr) * | 2017-01-05 | 2020-10-16 | Renault Sas | Procede d’optimisation de la consommation energetique d’un vehicule hybride |
CN109131350B (zh) * | 2018-08-23 | 2020-04-03 | 北京理工大学 | 一种混合动力汽车能量管理方法及系统 |
US20200074348A1 (en) * | 2018-08-30 | 2020-03-05 | Fujitsu Limited | Multi-function improvement for machine learning systems |
US11186277B2 (en) * | 2018-10-09 | 2021-11-30 | Peter H. Bauer | Energy-optimal adaptive cruise controller |
-
2020
- 2020-08-11 CN CN202010802712.0A patent/CN111891113A/zh active Pending
- 2020-11-14 US US17/098,372 patent/US20210107449A1/en not_active Abandoned
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108427985A (zh) * | 2018-01-02 | 2018-08-21 | 北京理工大学 | 一种基于深度强化学习的插电式混合动力车辆能量管理方法 |
CN108446477A (zh) * | 2018-03-14 | 2018-08-24 | 中南大学 | 吸能结构耐撞性能优化方法、系统与可读存储介质 |
CN110281904A (zh) * | 2019-06-18 | 2019-09-27 | 浙江吉利控股集团有限公司 | 一种混合动力车辆的能量管理方法、装置及终端 |
CN110509911A (zh) * | 2019-09-04 | 2019-11-29 | 北京理工大学 | 一种混合动力车辆的控制方法及装置 |
CN110723132A (zh) * | 2019-10-24 | 2020-01-24 | 深圳技术大学 | 一种混合动力车及其控制方法、存储介质 |
CN110929920A (zh) * | 2019-11-05 | 2020-03-27 | 中车戚墅堰机车有限公司 | 一种基于工况识别的混合动力列车能量管理方法 |
CN111277141A (zh) * | 2020-02-29 | 2020-06-12 | 武汉理工大学 | 双向dc/dc变换器优化控制方法 |
Non-Patent Citations (1)
Title |
---|
高建平: "基于实际道路工况的混合动力客车能量管理策略多目标优化", 《汽车技术》 * |
Also Published As
Publication number | Publication date |
---|---|
US20210107449A1 (en) | 2021-04-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tian et al. | An ANFIS-based ECMS for energy optimization of parallel hybrid electric bus | |
Wu et al. | An optimized real-time energy management strategy for the power-split hybrid electric vehicles | |
Dawei et al. | Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle | |
CN110450641B (zh) | 一种汽车制动能量回收方法、装置和电动汽车 | |
Li et al. | Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus route | |
CN103606271B (zh) | 一种混合动力城市公交车控制方法 | |
CN104044589B (zh) | 用于操作混合动力车辆的方法 | |
CN105528498B (zh) | 网联智能电动车辆一体化建模与集成控制方法 | |
CN110936949A (zh) | 基于行驶工况的能量控制方法、设备、存储介质及装置 | |
Ganji et al. | A study on look-ahead control and energy management strategies in hybrid electric vehicles | |
CN106055830B (zh) | 基于动态规划的phev控制门限参数优化方法 | |
CN105539423A (zh) | 结合环境温度保护电池的混合动力车转矩分配控制方法及系统 | |
Ouddah et al. | From offline to adaptive online energy management strategy of hybrid vehicle using Pontryagin’s minimum principle | |
Wang et al. | Cyber physical energy optimization control design for PHEVs based on enhanced firework algorithm | |
Wang et al. | Hybrid electric vehicle modeling accuracy verification and global optimal control algorithm research | |
Finesso et al. | An unsupervised machine-learning technique for the definition of a rule-based control strategy in a complex HEV | |
Fries et al. | Operational strategy of hybrid heavy-duty trucks by utilizing a genetic algorithm to optimize the fuel economy multiobjective criteria | |
CN110667565A (zh) | 一种智能网联插电式混合动力汽车协同优化能量管理方法 | |
Hu et al. | Energy management optimization method of plug-in hybrid-electric bus based on incremental learning | |
CN111891113A (zh) | 混合动力车辆的信息物理能量优化控制系统及其控制方法 | |
Zhou et al. | Research on fuzzy energy management strategy of parallel hybrid electric vehicle | |
CN104614986B (zh) | 基于工况自学习的混合动力总成优化标定实验系统及方法 | |
Yadav et al. | Fuzzy control implementation for energy management in hybrid electric vehicle | |
Zhuang et al. | Optimal energy management strategy design for a diesel parallel hybrid electric vehicle | |
Li et al. | Fuel economy optimization of hybrid electric vehicles |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201106 |