CN102639376A - 组合混合动力汽车控制方法 - Google Patents
组合混合动力汽车控制方法 Download PDFInfo
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Abstract
本发明公开了一种分配汽车内能量的方法,其包括:根据汽车控制器内的若干个方法变量计算汽车的能量预测(40),若干个方法变量包括驾驶员配置文件信息、GPS信息、ESS信息、环境信息、辅助信息和系统默认参数;控制器根据能量预测和若干个方法变量计算充电方法(42),并根据方法变量、能量预测和充电方法确定能量分配的控制方法(46);根据控制方法,能量被分配至汽车系统(48)。
Description
相关申请的交叉参考
本申请主张于2009年9月11日申请的申请号为61/241,601的美国临时申请的优先权,其全部内容通过参考引用结合于此。
技术领域
本发明涉及一种具有适应性控制方法的汽车,更具体的说,本发明涉及一种利用预测分析和汽车路线测绘的适应性控制方法。
背景技术
随着科技的进步和对环保高效汽车的日益关注,汽车开始交替使用燃料和电源。电动汽车或混合动力汽车使用能量储存系统(ESS),以根据不同的需求为汽车提供动力。但是,为了持续使用,汽车必须产生ESS并对ESS再充电。
通常,混合动力汽车根据转矩和ESS的荷电状态来确定发电机是否需要被激活以对ESS再充电。如果ESS的荷电状态降到设定的水平以下,则发电机被激活。因此,最好可以建立理想的充电计划,以对ESS再充电并尽可能少地中断汽车操作。对于电动汽车,汽车输出功率时无法对ESS再充电。因此,可以将可用能量分配至电动汽车内的关键系统,以确保汽车可以到达目的地或者下一个充电机会非常重要。
因此,最好可以在驾驶前或驾驶过程中预测所需能量消耗,以确保有能量可用并优化充电计划。
发明内容
一种分配汽车内能量的方法,其包括:根据汽车控制器中的若干个方法变量计算汽车的能量预测,其中,若干个方法变量包括驾驶员配置文件信息、GPS信息、能量储存系统(ESS)信息、环境信息、辅助信息和系统默认参数;控制器根据能量预测和若干个方法变量计算充电方法,并根据方法变量、能量预测和充电方法确定能量分配的控制方法;根据控制方法,能量被分配至汽车系统,其包括当能量预测预测汽车功率不足时限制汽车若干个功能中的至少一个;监视汽车路线,且当汽车偏离预定的路线或计算出的能量预测时重新计算能量预测、充电方法和控制方法。
以下结合说明书附图和权利要求书,详细描述用于实施本发明的各个实施方式和最佳实施方式,以使本发明的技术方案及其有益技术效果更加清楚明白。
附图说明
图1为具有利用适应性控制方法的控制器的电动汽车的示意图。
图2为用于管理图1所示的汽车的适应性控制方法的方法第一实施方式的示意图。
图3为用于确定图1所示的汽车的驾驶员配置文件信息的方法第一实施方式的示意图。
图4为用于确定图1所示的汽车的ESS信息和辅助信息的方法第一实施方式的示意图。
图5为用于计算图1所示的汽车的能量预测的方法第一实施方式的示意图。
图6为用于计算图1所示的汽车的充电方法的方法第一实施方式的示意图。
图7为用于计算图1所示的汽车的控制方法的方法第一实施方式的示意图。以及
图8为用于计算图1所示的汽车的控制方法的方法第二实施方式的示意图。
具体实施方式
请参照附图,不同的附图中,相同的附图标记表示相同的或相似的元件。图1所示为汽车10的示意图,其包括至少一个发动机12、至少一个发电机14,以及一个能量储存系统(ESS)16。汽车10可以是电动汽车,也可以是混合动力汽车。因此,除了至少一个发动机12,汽车10还可以包括一个内燃机18。汽车10还包括一个控制器20,控制器20用于控制汽车10的功能,包括对ESS16再充电、启动/停止内燃机18等。ESS16包括至少一个电池,最好可以是一个用来为汽车10的不同系统提供能量的电池组。
汽车10最好也包括一个全球定位系统(GPS)22,GPS22可以为汽车10提供地图和位置数据,GPS22最好还能提供天气和交通信息,GPS22与控制器20连接。如图2所示,控制器20确定一个控制方法24,以根据汽车信息(包括来自GPS22的信息)对ESS16充电。根据汽车10不断变化的信息,控制方法24可以不断地或经常地进行改编。
控制方法24还确定能量在汽车10的所有系统中的分配,例如,汽车10的所有系统可以包括发动机、ESS16、音响、加热/冷却系统、挡风玻璃雨刮等。根据汽车信息,控制方法24限制各个汽车系统的功率或将功率分配给各个汽车系统。
请参照图2,为确定控制方法24的一个实施方式的示意图。确定控制方法24自将方法变量26输入控制器20开始,方法变量26包含在确定控制方法24中,如步骤28。方法变量26包括但不局限于驾驶员配置文件信息30、GPS信息32、ESS信息34、辅助信息35、环境信息36,以及系统默认参数28。
GPS信息32可以包括当前的汽车位置、汽车的目的地和路线信息。环境信息36可以由GPS32提供,或者独立地由汽车10向控制器20提供,环境信息36可以包括环境温度、日、时间、湿度、天气(当前天气和预报天气)、3D地图、拓扑数据、太阳能负荷,以及其他天气数据和汽车数据。
控制器20随后利用方法变量26建立能量预测41,如步骤40,能量预测41为根据方法变量26预测到的汽车10所需的最大能量。
根据方法变量26,控制器20还预测ESS充电方法44,如步骤42。ESS充电方法44基于ESS16的能量需求和可用的再生机会,以确定ESS16所需的必要和理想的充电时间。
控制器20随后利用方法变量26、能量预测41和计算出的充电方法44确定控制方法24,如步骤46。控制方法24决定汽车10的能量如何在发动机12、ESS16、汽车附件和其他汽车系统分配。
控制器20利用控制方法24将能量分配到汽车10的各个系统,如步骤48。分配能量的步骤48包括为提供给各个子系统的能量和功率值提供能智能控制,以控制相关的最大负荷和名义负荷。此外,当汽车10的信息更新时,控制器20周期性地重复确定控制方法24,如步骤49所示。
请参照图3,为确定驾驶员配置文件信息30的一个实施方式的示意图。驾驶员识别器50被输入控制器20,如步骤52。驾驶员识别器50可以通过密码输入,密码由驾驶员通过GPS22输入汽车10或储存在汽车10的钥匙中。输入驾驶员识别器50后,控制器20检测储存的信息,以确定是否有驾驶员配置文件与驾驶员识别器50相关,如步骤54。如果没有驾驶员配置文件与驾驶员识别器50相关,系统就选择储存的默认驾驶员配置文件,如步骤56。此外,如果没有输入驾驶员识别器50或者驾驶员识别器50被不适当地记录,控制器20可以利用默认驾驶员配置文件。驾驶员配置文件信息30随后设成选定的驾驶员配置文件,如步骤58。驾驶员配置文件信息30随后被载入控制器20,如步骤60。
驾驶员配置文件信息30可以包括驾驶员路线历史、驾驶习惯/模式、驾驶员首选设定,如首选温度和首选音响,以及其他驾驶员历史信息。驾驶员路线历史也可以包括驾驶员已知的路线,其可以与一天中的具体时间相关,也可以与具体的附件使用相关。
迅速确定驾驶员配置文件信息30有助于控制器20快速、准确地确定控制方法24。驾驶员配置文件信息30包括模式识别算法,其可以计算启动或停车的次数、加速度和减速度,以及具体驾驶事件的附加载荷等。默认驾驶员配置文件包括,根据平均数据和汽车10的已知行为编入控制器20的单个变量的默认值。如上所述,驾驶员配置文件信息30的参数值为具体的时间或事件,以有助于更准确地确定控制方法24。
图4所示为确定ESS信息34和辅助信息35的一个实施方式的示意图。控制器20从ESS16收集数据,如步骤62。此外,控制器20利用至少包括环境温度的环境信息36。ESS信息34包括电压、电流、充电和放电历史、ESS温度,以及其他ESS数据,ESS信息34还进一步包括根据ESS温度和环境温度确定ESS16预测到的冷却/加热需求。根据ESS信息34,控制器20根据相关温度和时间计算ESS荷电状态(SOC)和ESS放电率,如步骤64。
此外,控制器20还可以在获得ESS信息34的同时计算辅助信息35,如步骤66,辅助信息35是汽车10的每个辅助系统需要的功率。辅助信息35可以包括汽车10中利用独立于ESS16的能量的所有系统,辅助信息35基于驾驶员配置文件信息30、环境信息36和系统默认参数28。辅助功率需求35基于来自驾驶员配置文件信息30和环境信息36的已知模式,包括湿度、温度、太阳能负荷、一天中的时间、空调使用、热使用、除霜使用、挡风玻璃雨刮使用、导航信息32等。根据当前的汽车状态,每个与驾驶员配置文件信息30和环境信息36相关的参数都被分配一个值。根据所分配的值,可以确定每个汽车系统所需的能量。ESS信息30和辅助功率需求35随后被载入控制器20,如步骤68。
图5所示为计算能量预测41的一个实施方式的示意图,如步骤40。控制器20确定是否有来自GPS22的目的地信息,如步骤70。如果没有目的地信息输入,控制器20根据驾驶员配置文件信息30(其包括以前与一天中的时间相关的驾驶员的目的地)确定最可能的目的地,如步骤72。目的地信息或预测到的目的地信息被用来计算路线信息,路线信息可被用来到达目的地和相关的拓扑数据,如步骤74。路线信息随后被用来计算和预测能量预测41,如步骤76。
能量预测41包括来自驾驶员配置文件信息30的信息,如速度、加速、减速、辅助载荷(动力转向载荷、制动载荷、交流载荷等),并为驾驶员预测在给定路线上所需的最大预期能量。如上所述,利用路线信息预测能量预测41还包括利用ESS信息34和辅助功率需求35。能量预测41包括预测到的各个不同的汽车系统的最大能量需求,而不仅仅是发动机12和ESS16的最大能量需求。
能量预测41采用以下方程式:
EREQ=∑(F x v x D)+∑(辅助功率需求35)
其中,EREQ是汽车10所需的能量,F是移动汽车10所需的力,v是汽车10的速度,D是汽车10的行驶距离。
此外,每个车轮的功率(PWHEEL)可以通过以下方程式计算:
PWHEEL=∑F x v=(FACCEL+FASCEND+FDRAG+FLOSSES)x v
其中,PWHEEL是车轮的功率,FACCEL是因加速作用于汽车10上的力,FASCEND是驾驶汽车10需要的力,FDRAG是因阻尼作用于汽车10上的力,FLOSSES是因能量损失作用于汽车10上的力。此外,FASCEND可以通过以下方程式计算:
FASCEND=(ma+mg sinA+mgCrr cosA+1/2ρ(CDA)ρv(aero)2)
其中,m是汽车10的质量,a是汽车10的加速度,g是地球重力加速度,ρ是空气密度,A是道路的坡度,Crr是滚动阻力系数,CDA是汽车10的迎风面积,aero是汽车10的气动阻力。
图6所示为确定充电方法44的一个实施方式,如步骤42。控制器20计算ESS16的充电需求,如步骤78。充电方法44包括确定是否可根据能量预测41和可用能量的时间和距离的函数来计算能量消耗,如步骤80。此外,充电方法78可以使用GPS信息32和环境信息36(包括天气和3D地图),以确定ESS16充电的理想时机。充电方法78还包括ESS16充电所需的再生制动量、所需能量的总量,以及所需的最大功率。利用此信息,算法可以计算充电方法44,如步骤82。
图7所示为计算控制方法24的一个实施方式的示意图。根据充电方法44,控制器20确定是否所有的时间都有足够的能量可供汽车10的所有系统使用。如果没有足够的能量,控制方法24确定哪个系统应当接收能量、接收多少能量,以及哪些系统的能量使用应当受到限制。控制方法24对各个汽车系统进行优先排序,优先排序基于各个系统的功能和系统是不是重要的系统。例如,重要的系统可以包括辅助约束系统(supplemental restraint system,SRS)、制动器、发动机12等。优选地,非重要、非关键系统,如高加热/冷却的使用、音响设备的能量使用会受到限制,以使汽车10的范围和功率最大化。此外,也可以限制汽车10的最高速度和加速,以保存能量。
如上所述,控制方法24包括确定到达下一个能量源所需的能量值,能量源可以是原发的能量源(再生制动以对ESS16充电)或次生的能量源(服务站、加气站、电池交换站等)。当汽车10的信息更新时,控制器20周期性地重复确定控制方法24,并提供新的控制方法24,如49(图1所示)。
图8所示为汽车10的控制方法124的另一个实施方式,当汽车10启动时,汽车10的控制器20(如图1所示)启动,如步骤102。控制器20确定方法变量,方法变量包括时间、位置、温度和风速,如步骤126,确定方法变量126可以包括利用来自天气数据接收器136和GPS接收器132的数据。控制器20随后请求目的地和路线信息,如步骤170。目的地和路线信息可以包括根据储存的驾驶员信息检查汽车存储器的预定路线,如步骤130。根据存储的信息,控制器20可以利用数据预测路线和目的地,如步骤172。
控制器20请求汽车10所需的能量,如步骤140。也就是,控制器20请求汽车10的能量预测。控制器20收集方法变量,如步骤126。收集的信息可以包括路线信息,其包括停车次数、上坡、下坡等,如步骤104。控制器20计算完成路线所需的能量,即计算能量预测,如步骤176。所需能量的计算包括因阻尼、滚动阻力、停车和启动,以及汽车的重量导致的能量损失,如步骤106。
控制器20请求汽车10的可用能量,如步骤142。控制器20收集ESS信息,其包括BMS模块的健康状况,如步骤134。收集到的信息可以包括路线信息,其包括停车次数、上坡、下坡等,如步骤104所示。控制器20随后确定汽车外的温度是否低于30华氏摄氏度,如步骤108。如果不低于30华氏摄氏度,控制器20计算汽车10的下一个再生机会,如步骤178。如果低于30华氏摄氏度,控制器20减少可用的用于补偿寒冷的能量生成,如步骤110。控制器20随后计算生成的总的可用能量,如步骤182。
发电机操作模式被请求,如步骤146。发电操作模式被用于在可能的时候增加可用的功率值,是汽车10的充电方法。如步骤126所示,控制器20利用方法变量,包括步骤112检测剩余燃料水平和步骤114回忆最后操作效率水平。计算发电机操作参数,以增加汽车输出,如步骤184。控制器20检测操作参数所需的能量是否足够,如步骤186。也就是,控制器20比较充电方法和能量预测,以确定能量是否足够。如果能量不够,控制器20启动汽车10的功率限制算法,如步骤118。驾驶员配置文件信息从存储器被收集,如步骤130。在可能的时候,控制器20限制加速、最高车速、汽车10的非安全辅助载荷,并增加再生值,如步骤188。
根据收集到的和计算出的数据,控制器20执行生成操作参数,如步骤148。也就是说,控制器20根据利用计算出的能量预测和充电方法确定的控制方法,将能量分配给汽车10。
当汽车10运行时,控制器20连续监视汽车10的能量消耗和路线,如步骤190。只要汽车10仍然在行程中,控制器20就连续监视能量消耗和路线,如步骤192。如果汽车10偏离行程,控制器20重新计算能量需求和能量潜力,如步骤149。
尽管已经详细描述了实施本发明的最佳实施方式,但是,本领域的技术人员在本发明的权利要求的保护范围内还可以想到实施本发明的各种变更设计和实施方式。
Claims (17)
1.一种分配汽车内能量的方法,其包括:
根据汽车控制器中的若干个方法变量计算汽车的能量预测,其中,若干个方法变量包括驾驶员配置文件信息、GPS信息、能量储存系统(ESS)信息、环境信息、辅助信息和系统默认参数;
根据能量预测和若干个方法变量计算充电方法;
根据方法变量、能量预测和充电方法确定能量分配的控制方法;
根据控制方法将能量分配至汽车系统,控制方法包括:当能量预测预测汽车功率不足时限制汽车若干个功能中的至少一个;以及
监视汽车路线,且当汽车偏离预定的路线或计算出的能量预测时重新计算汽车的能量预测、充电方法和控制方法。
2.根据权利要求1所述的方法,进一步包括输入驾驶员配置文件信息,其通过:
将驾驶员识别器输入控制器;
选择对应的驾驶员配置文件或默认的驾驶员配置文件;以及
根据选定的驾驶员配置文件通过算法计算驾驶员信息。
3.根据权利要求2所述的方法,其中,输入驾驶员配置文件信息包括输入驾驶员路线历史、至少一个驾驶习惯、驾驶员首选温度设定、驾驶员首选音响设定中的至少一个。
4.根据权利要求3所述的方法,其中,输入驾驶员配置文件信息包括使驾驶员路线历史与驾驶日的时间、相关的附件使用相联系。
5.根据权利要求1所述的方法,进一步包括输入GPS信息,其中,输入GPS信息包括输入当前的汽车位置、汽车目的地、汽车路线、3D地图、拓扑数据、交通信息和兴趣点中的至少一个。
6.根据权利要求5所述的方法,其中,输入GPS信息包括计算汽车路线中因阻尼、滚动阻力、汽车的启动和停车引起的汽车损失。
7.根据权利要求5所述的方法,其中,GPS信息包括汽车路线中的停车次数、上坡和下坡。
8.根据权利要求1所述的方法,其中,限制汽车若干个功能中的至少一个功能持续到汽车到达目的地,或者持续到汽车预测显示根据预测到的能量预测汽车有足够的能量。
9.根据权利要求1所述的方法,其中,限制汽车若干个功能中的至少一个功能包括限制汽车的加速水平、限制汽车的最高速度、限制汽车的非安全辅助载荷中的至少一个。
10.一种分配汽车内能量的方法,其包括:
根据汽车控制器中的若干个方法变量计算汽车的能量预测,其中,若干个方法变量包括驾驶员配置文件信息、GPS信息、能量储存系统(ESS)信息、环境信息、辅助信息和系统默认参数;
根据能量预测和若干个方法变量计算充电方法;
根据方法变量、能量预测和充电方法确定能量分配的控制方法;
当能量预测预测汽车没有路线和计算出的充电方法所需的足够功率时,根据包括限制至少一个汽车功能的控制方法将能量分配至汽车系统;以及
限制至少一个汽车功能持续到汽车到达目的地,汽车预测显示根据预测到的能量预测汽车有足够的能量。
11.根据权利要求10所述的方法,其中,限制至少一个汽车功能包括限制汽车的加速水平、限制汽车的最高速度、限制汽车的非安全辅助载荷中的至少一个。
12.根据权利要求10所述的方法,进一步包括输入驾驶员配置文件信息,其通过:
将驾驶员识别器输入控制器;
选择对应的驾驶员配置文件或默认的驾驶员配置文件;以及
根据选定的驾驶员配置文件通过算法计算驾驶员信息。
13.根据权利要求12所述的方法,其中,输入驾驶员配置文件信息包括输入驾驶员路线历史、至少一个驾驶习惯、驾驶员首选温度设定、驾驶员首选音响设定中的至少一个。
14.根据权利要求13所述的方法,其中,输入驾驶员配置文件信息包括使驾驶员路线历史与驾驶日的时间、相关的附件使用相联系。
15.根据权利要求10所述的方法,进一步包括输入GPS信息,其中,输入GPS信息包括输入当前的汽车位置、汽车目的地、汽车路线、3D地图、拓扑数据、交通信息和兴趣点中的至少一个。
16.根据权利要求15所述的方法,其中,输入GPS信息包括计算汽车路线中因阻尼、滚动阻力、汽车启动和停车引起的汽车损失。
17.根据权利要求15所述的方法,其中,GPS信息包括汽车路线中的停车次数、上坡和下坡。
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US12/878,254 US8548660B2 (en) | 2009-09-11 | 2010-09-09 | Integrated hybrid vehicle control strategy |
PCT/US2010/048376 WO2011031933A1 (en) | 2009-09-11 | 2010-09-10 | Integrated hybrid vehicle control strategy |
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Also Published As
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US8548660B2 (en) | 2013-10-01 |
CN102639376B (zh) | 2015-04-22 |
WO2011031933A1 (en) | 2011-03-17 |
US20110202216A1 (en) | 2011-08-18 |
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