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CN109263640A - 基于模型的路线能量预测、修正和优化的车辆 - Google Patents

基于模型的路线能量预测、修正和优化的车辆 Download PDF

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Publication number
CN109263640A
CN109263640A CN201810728914.8A CN201810728914A CN109263640A CN 109263640 A CN109263640 A CN 109263640A CN 201810728914 A CN201810728914 A CN 201810728914A CN 109263640 A CN109263640 A CN 109263640A
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China
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vehicle
energy
data
controller
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CN201810728914.8A
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CN109263640B (zh
Inventor
D·S·帕克
K·J·史密斯
T·L·米汉
T·P·林德曼
R·B·温伯格
J·B·尼科尔森
C-H·刘
A·M·泽特尔
R·A·阿朗索
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GM Global Technology Operations LLC
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GM Global Technology Operations LLC
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    • 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
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    • B60W30/18—Propelling the vehicle
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    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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    • B60K6/32—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs characterised by apparatus, components or means specially adapted for HEVs characterised by the fuel cells
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Abstract

车辆包括驱动轮、具有可用能量的能量源、由能量源供电以提供输入转矩的转矩产生装置、被配置为接收输入转矩并且向该组驱动轮传递输出转矩的变速器，以及控制器。作为编程方法的一部分，控制器使用车载数据、非车载数据和第一逻辑块来预测沿着预定行驶路线的可用能量的消耗，并且还使用车载数据、非车载数据以及第二逻辑块与第一逻辑块之间的纠错回路来修正预测的能量消耗。控制器还使用修正后的能量消耗对车辆执行控制动作，包括改变车辆的逻辑状态。

Description

基于模型的路线能量预测、修正和优化的车辆
引言
车辆可以被配置为使用多种动力源进行推进。例如，汽油、柴油、天然气或其它替代燃料可以通过发动机燃烧以产生发动机转矩。混合动力电动车辆(HEV)使用一个或多个电机和发动机作为替代或补充的转矩源。在HEV中，发动机转矩可以单独地或者与来自一个或两个电机的电动机转矩一起传递到变速器输入或输出构件。发动机可以在电动车辆操作模式下关闭以节省燃料。增程型电动车辆(EREV)使用发动机转矩选择性地对发电机供电，而不会将发动机转矩传输到变速器。电池电动车辆(BEV)缺少作为其动力系的一部分的发动机，并且因此使用电能来产生用于车辆推进的电动机转矩。燃料电池车辆(FCV)利用来自燃料电池组过程的电能来对电机供电和/或对电池组充电。
无论动力系配置和车辆上使用的备用燃料或能源的具体类型如何，车载控制器都能够调节可用转矩源的转矩贡献。控制器从各种传感器接收输入信号，这些传感器共同监控当前操作状况。控制器执行算法或控制逻辑以确定在性能或能量效率方面适合于当前操作状况的动力系操作模式。动力系以最佳方式操作的能力在很大程度上取决于控制器适应动态变化的操作状况的速度和整体精度。
发明内容
本文公开了一种车辆，其包括动力系、传感器和具有编程的能量/功率(E/P)预测模型的控制器。E/P预测模型允许控制器预测车辆在预定行驶路线上的能量消耗，而不考虑车辆上使用的特定类型的能量源。E/P预测模型使用前瞻性或“预见性”信息进行。控制器还被配置为使用由实时数据和学习的车辆性能通知的误差反馈回路来修正E/P预测模型的随时间变化的整体预测精度。最终，控制器使用修正后的预测以适合于车辆的动力系配置的方式(诸如通过针对BEV以提高精度来计算和显示路线能量预测，或者通过为HEV、EREV或FCV选择适当的动力系操作模式)来优化车辆的性能。
在非限制性示例实施例中，车辆包括一组驱动轮、具有可用能量的能量源、由能量源供电以提供输入转矩的转矩产生装置、被配置为接收输入转矩并且向该组驱动轮传递输出转矩的变速器，以及控制器。该特定实施例中的控制器被配置为使用车载数据、非车载数据和第一逻辑块来预测沿着预定行驶路线的来自能量源的可用能量的消耗速率，该能量源可以包括多个不同的能量源。控制器使用车载数据、非车载数据以及第二逻辑块与第一逻辑块之间的纠错回路来修正随时间变化的预测能量消耗。此后，控制器使用修正后的能量消耗对车辆执行控制动作，包括改变车辆的逻辑状态。
车辆可以包括显示屏。在这样的实施例中，改变逻辑状态可以包括使用修正后的能量消耗来更新车辆相对于预定行驶路线的估计剩余行驶里程，然后经由显示屏显示更新的剩余电动行驶里程。
在一些配置中，能量源包括能量存储系统(ESS)，并且转矩产生装置包括电连接到ESS的电机。在其它配置中，能量源可以包括可燃燃料的供应，其中转矩产生装置包括由可燃燃料的燃烧供电的发动机。或者，能量源可以包括氢气和氢燃料电池，其中转矩产生装置包括经由来自氢燃料电池的输出电流激励的电机。由于燃料电池的瞬态响应与发动机的瞬时响应大不相同，所以该方法可以采取诸如调度燃料电池以在给定驾驶循环中(例如当道路负载即将发生时)更早开始发电的动作，否则以类似方式进行，不管能量源的来源如何。
改变逻辑状态可以包括开启发动机以便将车辆从其中ESS被放电到第一阈值充电状态(SOC)的电量消耗模式转变为ESS的SOC保持在高于第一SOC的第二SOC以上的电量保持模式。
控制器可以将沿着预定行驶路线的预测能量消耗分成具有不同相对功率水平的功率组，并且也可以在最高功率组期间开启发动机以进入电量保持模式。
根据某些实施例的非车载数据可以包括描述预定行驶路线的高程的高程数据、描述车辆沿着预定行驶路线的估计速度的路线速度数据、描述预定行驶路线的环境的环境数据、描述车辆的坐标的位置数据以及描述沿着预定行驶路线的交通状况的实时交通数据。
车载数据可以包括变速器的流体温度、车辆的加热、通风和空调(HVAC)使用数据以及ESS的SOC。
该控制器可以包括旋转损失逻辑块，其被配置为使用流体温度来确定变速器的旋转损失，其中控制器使用旋转损失来预测车辆的能量消耗。
环境数据可以包括沿着预定行驶路线的风速和方向、降雨量和太阳能负载中的一个或多个。
控制器还可以包括增量速度逻辑块，其可操作用于计算指示车辆沿着预定行驶路线的预测加速度的增量速度值，并且用于使用增量速度值来预测车辆的能量消耗。
本文还公开了一种用于与具有动力系的车辆一起使用的方法，该动力系包括电机和变速器，该电机由能量存储系统选择性地激励以产生电动机转矩，该变速器被配置为从电机接收电动机转矩并且向一组驱动轮传递输出转矩。该方法的示例实施例包括经由控制器接收非车载和车载数据，然后当车辆沿着预定行驶路线行驶时使用非车载数据、车载数据和第一逻辑块来预测动力系的能量消耗。该方法还可以包括使用车载数据、非车载数据以及第二逻辑块与第一逻辑块之间的纠错回路来修正预测能量消耗。此后，该方法继续经由控制器使用修正后的能量消耗对车辆执行控制动作，这包括通过将输出信号传输到显示屏以由此显示车辆的估计电动行驶里程来改变车辆的逻辑状态和/或控制动力系的操作模式。
根据结合附图进行的用于执行本公开的最佳模式的具体实施方式，本公开的上述和其它特征和优点是显而易见的。
附图说明
图1是具有动力系和控制器的示例车辆的示意图，该控制器被编程为预测车辆沿预定行驶路线的能量消耗，使用误差反馈回路修正随时间变化的预测，并且使用如本文所述的修正后的路线能量预测优化车辆的功能性。
图2是图1中所示的控制器的路线能量预测、修正和系统优化功能的示意框图。
图3和4分别是图1的控制器的示意能量/功率预测逻辑块和修正/学习逻辑块。
图5是图1中所示的控制器的示意优化逻辑块。
图6是描述用于使用修正后的路线能量预测来优化图1的车辆的系统功能的示例实施例的示意逻辑流程图。
图7和7A分别是应用和不应用图6的功能优化逻辑块的电池充电状态的示意时间曲线图。
本公开具有各种修改和替代形式。代表性实施例在附图中已作为示例示出并且将在本文进行详细描述。然而，本公开的新颖方面不限于附图中所说明的特定形式。相反，本公开旨在涵盖落入由所附权利要求限定的本公开的范围内的修改、组合和/或替代。
具体实施方式
参考附图，其中相同的附图标记在几个视图中指代相同的部件，图1描绘了示例机动车辆10。车辆10包括与路面14滚动摩擦接触的一组驱动轮12。车辆10包括使用一个或多个能量源的动力系20，当车辆10沿着预定行驶路线行驶时，经由控制器(C)50自动地预测和修正该能量源的功率/能量消耗的速率，根据如下面另外参考图2到7A所描述的方法100来这样做。控制器50被配置为在车辆10的操作期间接收输入信号(箭头CCI)并且以某种方式产生改变车辆10的逻辑状态的输出信号(箭头CCO)，该方式诸如通过调节操作或改变动力系20的操作模式和/或命令车辆10的显示器或其它功能。
车辆10的能量源11随着车辆10的配置而变化。例如，车辆10可以包括内燃机(E)13，其通过燃烧从燃料箱19供应的燃料(箭头F)产生发动机转矩(箭头TE)，该燃料诸如汽油、柴油燃料、生物燃料、乙醇或天然气。能量存储系统(ESS)28(诸如多单元电池组、飞轮、超级电容器或用于存储备用功率的其它合适的装置)可以用作能量源11。能量源11提供可用能量以对车辆10的各种转矩产生装置供电，并且可包括在来自氢供应箱17S和氢燃料电池堆(FC)17的压力下供应的氢(箭头H2)，产生燃料电池输出电流(箭头iFC)。来自这种源11的能量可用于对一个或多个转矩产生装置(例如，发动机13和/或电机(MA)21)供电。这些和其它可能的能量源11可以在本公开的范围内使用。
一种可能的调节功能是在车辆10协商给定的行驶路线或行程时预测车辆10的功率/能量消耗，以及使用实时信息对这种路线能量预测的自适应修正。最终，控制器50执行车辆10上的控制动作以改变车辆10或其子系统之一的逻辑状态或物理状态，使用修正后的能量预测来这样做。例如，控制器50可以在更有效的时间点将动力系20在电量消耗模式与电量保持模式之间转变，可能包括调度燃料电池组的开启或关闭，和/或向车辆10的操作员显示更精确电动行驶里程信息。命令多单元电池进行最大程度充电有时会导致单个电池单元的过热和潜在退化。因此，在开始行程之前对车辆10的真实剩余里程进行更精确的预测用于提高车辆10的总体操作寿命和性能效率。
在图1的示例配置中，车辆10可以包括电机21，其被选择性地激励以产生用于车辆推进的电动机转矩(箭头TM)。在各种示例实施例中，车辆10可以被配置为混合动力电动车辆、插电式混合动力电动车辆、燃料电池车辆，如所示的增程型电动车辆或电池电动车辆。虽然被示为轿车型乘用车辆，但是在可能的示例实施例中，车辆10可以替代地被配置为跨界车、运动型多用途车辆、休闲车辆、卡车、公共汽车、商用车辆或移动平台。因此，对增程型电动车辆的以下描述用于说明目的，而不将应用限于这种配置。
在图1中所示的动力系20的范围扩展配置中，电机(MA)21的电相引线23电连接到高压能量存储系统(ESS)28并且由该高压能量存储系统(ESS)28选择性地激励或供电，其中术语“高电压”是相对于辅助/低电压电平。当由ESS28激励时，电机21经由输出轴22的旋转而产生电动机输出转矩(箭头TM)。电动机输出转矩(箭头TM)可以传输到变速器(T)24，该变速器的可旋转输出构件25最终将输出转矩(箭头To)传递到驱动轮12以推进车辆10。虽然为了简单起见而从图1中省略了，但是车辆10可以被配置为插电式车辆，其中ESS28包括插入到诸如充电插座等非车载能量供应器(未示出)并且当车辆10没有运行时进行再充电的多单元电池组。
在图1的示例实施例中，内燃机(E)13经由燃料-空气燃烧过程产生围绕曲轴15的发动机转矩(箭头TE)，以驱动发电机(G)16并且由此产生足够水平的电能以对ESS28进行再充电和/或用于直接对电机21供电。为了提供用于旋转电机21的输出轴22的必要电力，ESS28在某些实施例中可以包括超级电容器或利用适用于应用的电池化学物质(例如，锂离子、镍金属氢化物(NiMH)、镍镉(NiCd)或氯化钠钠(NaNiCl))的单独电池单元(未示出)。功率逆变器模块(PIM)26经由直流电压总线(VDC)27电连接到ESS28。PIM26根据需要诸如通过基于内部脉宽调制(PWM)的开关控制将电力从直流(DC)转化为交流(AC)，反之亦然。DC电压总线(VDC)27可以连接到DC-DC转换器形式的辅助功率模块(APM)29，其能够将DC电压总线27的电压电平降低到辅助12到15VDC电平(VAUX)以适合于存储在辅助电池(BAUX)30中和/或用于对车辆10上的辅助电气装置和系统供电。
为了执行方法100，控制器50被编程有能量/功率(E/P)预测模型52。E/P预测模型52用于使用前瞻性或“预见性”信息预测当车辆10从路线的始发点行驶到目的地时车辆10的能量消耗，并且使用由实时数据和观察到的车辆10的实际性能通知的纠错回路自适应地改进或修正随时间变化的预见性预测。如上文简要地提及，控制器50使用修正后的预测来优化车辆10的性能，诸如通过以提高预测准确度水平来计算和显示路线能量预测以确保驾驶员能够在耗尽可用能量之前到达目的地，或者通过选择适当的动力系操作模式，诸如通过控制切换到电动和汽油动力和/或从电动和汽油动力切换的定时和/或发起燃料电池堆17的能量产生操作。
通过控制器局域网总线或其它合适的通信信道与动力系20的部件进行通信的控制器50包括处理器(P)和存储器(M)。存储器(M)可以包括有形、非暂时性存储器，诸如光学、磁性、闪速或其它只读存储器。控制器50还包括显示屏51(诸如导航或信息娱乐触摸屏)和足量的随机存取存储器、电可擦除可编程只读存储器等、高速时钟、模数转换和数模转换电路，以及输入/输出电路和装置，以及适当的信号调节和缓冲电路。现在将参考图2到7A详细地描述用于执行方法100的控制器50的逻辑和控制功能。
图2描绘了控制器50的E/P预测模型52的示意框图。包含E/P预测模型52的各种控制块可以被实施为编程的软件例程或使用必需的硬件实施的模块。作为对E/P预测模型52的输入，图1的控制器50从相应的非车载数据块(DOFB)32和车载数据块(DONB)33接收非车载数据34和车载数据36。如名称指示，“非车载”数据被收集、报告或以其它方式源于车辆10外部，而“车载”数据被收集、报告或以其它方式源于车辆10内。
由图2的非车载数据块32收集或者可通过该非车载数据块获取的非车载数据34可以包括高程数据(ELV)、路线速度数据(RSD)、环境数据(ENV)、位置数据(POS)和实时交通数据(TRF)。高程数据(ELV)可以包括沿着车辆10的行驶路线的各种路标的接收或报告的高度信息。路线速度数据(RSD)可以包括沿着行驶路线的各种路段的历史的、公布的或估计速度。关于环境数据(ENV)，这些数据可以包括环境空气温度、降雨量水平、相对湿度以及风速和风向。在一些实施例中，控制器50可以使用作为太阳能的代表的紫外(UV)指数作为环境数据(ENV)的一部分，或者使用雷达数据来跟踪沿着行驶路线的降雨量。位置数据(POS)可以呈描述车辆10在地理空间地图上的当前位置的GPS坐标的形式。实时交通数据(TRF)可以通过联网导航系统报告，以包括由于事故、建筑或其它因素而导致的交通拥堵。
来自车载数据块33的车载数据36可以包括图1中所示的变速器24的报告的变速器流体温度(TFT)以及加热、通风和空调(HVAC)数据和设置。另外，车载数据36可以包括位于ESS28内的电池单元的报告的或计算的充电状态(SOC)。可以在本公开的范围内考虑非车载数据34和车载数据36的这些和其它示例。
非车载数据34和车载数据36由控制器50的能量/功率(E/P)预测逻辑块38接收并且用于产生路线能量预测(箭头EPRED)。如本文所使用，术语“路线能量预测”是指当车辆10沿着给定路线行驶时ESS28的电能消耗或电量消耗的估计或预测的量或速率。即，控制器50产生并显示车辆10的驾驶员预期要遵循的行驶路线。假设驾驶员遵循所显示的路线，控制器50估计沿着路线的各个点处的能量消耗速率。车辆10的剩余能量或电动行驶里程可以经由图1的显示屏51基于这样的能量预测以及其它控制动作显示给驾驶员，该控制动作诸如显示沿着或靠近路线的可用充电站的位置。
此后，自适应修正(A/COR)逻辑块40用于使用实时数据34和36来修正来自E/P预测逻辑块38的能量预测(箭头EPRED)，由此产生修正后的能量预测(箭头EC)，其中控制器50在E/P预测逻辑块38的情况下使用误差反馈回路53中的修正后的能量预测(箭头EC)来提高预测准确度。可选地，控制器50使用优化逻辑块(OPT)60来例如经由输出信号(箭头CCo)对图1的车辆10或动力系20执行控制动作。
将分别参考图3和4更详细地描述E/P预测逻辑块38和自适应修正逻辑块40的功能。
首先参考图3，在示例实施例中，E/P预测逻辑块38接收变速器流体温度(箭头TFT)并且经由旋转损失逻辑块130计算变速器旋转损失(LSP)。在图1中示意地示出的变速器24是流体致动和冷却的动力变速器，即，变速器流体的供应经由流体泵(未示出)循环到变速器24和从变速器24循环。因此，控制器50诸如经由图1的存储器(M)中的查找表将操作成本与变速器流体温度(TFT)相关联，其中在较低温度下有较高的变速器旋转损失(LSP)和能量低效。逻辑块132接收测量或报告的车速(N10)和位置数据(POS)以及估计的路线速度(NR)并且计算预测的行驶路线的给定路段上的补偿速度(NCOMP)。估计的路线速度(NR)可以被确定为所报告的实时速度(例如，来自交通相机、导航系统、历史速度和公布的限速)的函数。通过实际车速(N10)的通知，逻辑块132可以导出补偿速度(NCOMP)，其进而更准确地估计沿着行驶路线的不同路段的路线速度。
另外，逻辑块134可以接收上述高程数据(ELV)并且将即将到来的路段上的地形的斜率(箭头S)估计为路线高程和距离的函数。逻辑块136可以接收沿着路线的环境温度(TA)、当前HVAC设置和数据(HVAC)以及电池充电状态(SOC)，并且输出估计的HVAC能量使用量(HVACEST)。在本文被称为“增量速度”块的逻辑块138使用来自逻辑块132的补偿速度(NCOMP)来确定与车速中的变化或“增量”相关联的能量的量，例如由于即将到来入口匝道或车辆10预期会加速的其它路段而加速。然后从逻辑块138输出增量速度(NΔ)。
然后使用逻辑块133计算推进能量/功率消耗(箭头E1)，其中逻辑块133的输入是变速器旋转损失(LSP)、增量速度(NΔ)、位置(POS)和校准后的车辆参数(箭头VP)，诸如质量、空气动力学、轮胎压力/滚动阻力、系统转换损失等。单独地，在逻辑块135处使用来自逻辑块136的估计HVAC使用量(HVACEST)来计算HVAC能量/功率消耗(箭头E2)，其中来自逻辑块133的估计的推进能量/功率消耗(箭头E1)和来自逻辑块135的估计的HVAC能量/功率消耗(箭头E2)在加法(∑)逻辑块137处相加以产生图2中所示和上文提及的预测能量消耗(箭头EPRED)。预测能量消耗(箭头EPRED)此后成为图2和4中所示的自适应修正逻辑块40的输入。
如图3中所说明，由控制器50进行的当前能量预测是基于标称条件。E/P预测逻辑块38提供前瞻性策略，其计算将在给定路线地形上爬升和下降所需的能量的量，以热调节车辆10的乘客舱和ESS28，并且使车辆10以期望的行驶速度移动，该预期的行驶速度可能随路线过程中公布的限速、交通和天气状况而变化。使用图3的E/P预测逻辑块38因此旨在确保在车辆10的驾驶员开始沿着给定行驶路线行驶之前以及在行驶期间预测车辆10的预测电动行驶里程更准确，因为预测在反馈回路中是不断地或定期地更新的。这种自适应更新是通过自适应修正逻辑块40的操作来提供的，现在将参考图4更详细地描述该自适应修正逻辑块40。
自适应修正逻辑块40可以包括修正逻辑块(COR)41、比较逻辑块(COMP)42和实际能量/功率计算逻辑块43。如上面参考图3所述，来自逻辑块38的预测能量消耗(箭头EPRED)用作逻辑块40的第一输入。具体地，预测能量消耗(箭头EPRED)被馈送到逻辑块41和42，其中来自模块32和33的相应的非车载数据34和车载数据36用作逻辑块43的输入。在逻辑块43发生的计算可以包括道路负载(LD)、车辆10的实时速度(NL)、驾驶员技术(DT)和HVAC能量使用量(HVAC)。然后将实际值从逻辑块43作为输入前馈到比较逻辑块42。
上面提及的道路负载(LD)包括车辆10上的静态负载，即，基于与车辆10的运动相反的力或转矩，例如，滚动摩擦力和空气动力学阻力。车辆10的实时速度(NL)是实际速度，其受到交通、道路状况等的影响。在驾驶员技术(DT)方面，这是指车辆10的给定操作员的表明的驾驶行为。例如，图1的控制器50的存储器(M)可以针对多个操作员(例如，驾驶员A和B)的驾驶特性曲线进行编程。驾驶员A可能有积极的驾驶历史，诸如所表明的快速加速、拐弯和制动的能量消耗倾向，而驾驶员B可能有逐渐加速和制动的历史，这有助于提高能量效率。HVAC能量使用量(HVAC)是车辆10中的加热或空调系统在整个行程中的实际使用量，其中任一者在ESS28上呈现影响图1的动力系20的操作效率的负载。然后将实际值作为输入前馈到比较逻辑块42。
图4的比较逻辑块42可以从图3的逻辑块38接收预测能量消耗(箭头EPRED)，并且从逻辑块43接收实际能量消耗，即，在道路负载(LD)、实时速度(NL)、驾驶员技术(DT)和HVAC能量使用量(HVAC)方面的实际能量消耗。如名称所指示，比较逻辑块(COMP)42经由比较功能确定预测值与在行驶路线上来自道路负载(LD)、实时速度(NL)、驾驶员技术(DT)和HVAC能量量(HVAC)的实际值的变化。差异可以输出到短期存储(STS)队列。控制器50将该变化量化为数值修正因子(CF)，例如增益或乘数，并且将数值修正因子(CF)反馈给能量/功率修正逻辑块(COR)41。在那里，控制器50将调整后/修正后的能量预测(箭头EC)确定为初始预测(EPRED)和修正因子(箭头CF)的函数，例如EC＝(EPRED)(CF)。
图5示意地描绘了图2的优化逻辑块60的操作。来自上面参考图4描述的逻辑块41的调整后/修正后的能量预测(箭头EC)由优化逻辑块60接收。车辆10在给定行驶路线上的能量/功率消耗可以被分成多个相对功率组，诸如高功率(HP)组、中功率(MP)组和低功率(LP)组。控制器50可以使用能量负载分组(GEL)逻辑块61将类似的能量负载分组在一起并且执行对应于能量组的控制动作。
示例控制动作包括将来自控制器50的输出信号(箭头CCo)传送到图1的动力系20以命令改变车辆10的当前操作模式，诸如通过开启发动机13以开始对ESS28进行车载充电而不是继续以电量消耗模式操作。在其中发动机13并非动力系20的一部分的电池电动实施例中，控制器50在行程开始时进行的能量预测可导致控制器50基于到达特定充电站时的估计SOC而建议在该充电站停车。针对这种动力系20的可能的控制动作可以包括向驾驶员提供警告以比原计划更早的停车，或者从预期路线转移到不同的充电站以便在到达时确保足够的SOC。现在将参考图6、7和7A描述对这种能量分组的可能方法。
图6描绘了描述用于实施图5的优化逻辑块60的单独处理块或步骤的流程图。步骤S61需要确定对应于图5的低、中和高功率组(HP、MP、LP)中的每一个的能量负载并且测量图3的环境温度(TA)。一旦这些值被确定，控制器50然后进行到步骤S62。
步骤S62可以包括将环境温度(TA)和调整后/修正后的能量预测(EC)与校准极限进行比较，该校准极限进而可以预先记录在控制器50的存储器(M)中。当环境温度(TA)和调节后/修正后的能量预测(Ec)都在它们的相应的校准极限内时，控制器50执行步骤S63。否则，实施步骤S64。
步骤S63可以包括对图4的负载组重新排序，使得因为优化逻辑块60准备执行步骤S65，所以在给定行程中更早发生的组更优先。控制器50然后进行到步骤S65。
在步骤S64处，控制器50重新排序图4的负载组，使得最高功率组在执行步骤S65之前更优先。控制器50然后进行到步骤S65。
步骤S65需要检查来自步骤S63和S64的优先功率组，以查看单个组是否能够供应用于到达该行程的目的地所需的能量。如果是，则控制器50进行到步骤S68。当单个组不能供应用于到达行程目的地的必需能量时，控制器50实施步骤S66。
步骤S66可以包括组合较大的功率组。为了步骤S66的目的，如果给定组持续少于阈值持续时间，则控制器50可以忽略该组。控制器50然后进行到步骤S67。
在步骤S67处，控制器50检查在步骤S63和S64处优先的功率组，并且确定在给定的一个功率区中的操作是否将提供用于到达目的地的所需能量。如果是，则控制器50进行到步骤S68。否则，控制器50进行到步骤S69。
步骤S68包括经由控制器50为优先组确定适当的驾驶模式，并且将驾驶模式内的激活点决定为控制器50命令发动机13开启的时间。在这种情况下，控制器50激活发动机开启驾驶模式并且前进到步骤S70。
步骤S69包括退出优化逻辑块60并且命令或继续默认的电量保持模式。
步骤S70需要将图1的输出信号(箭头CCo)传输到图1的动力系20，以由此命令在步骤S68处确定的激活点处从发动机关闭电量消耗模式转变到发动机开启电量保持模式。
图7描绘了如参考图6所解释的优化逻辑块60的操作，而图7A提供了没有应用逻辑块60的比较动力系控制结果。图7和7A描绘了迹线80和180，具体是图1的ESS28的车速(N10)、充电状态(SOC)和驾驶模式命令(DMC)。驾驶模式命令(DMC)对应于二进制信号，其中高电平值请求开启图1的发动机13，而低电平信号请求关闭或保持关闭发动机13。在垂直轴上描绘最大电量的百分比(％)的图1的ESS28的SOC，而在水平轴上描绘以英里为单位的驾驶距离。可能的驾驶模式包括电量保持模式和电量消耗模式，在该电量保持模式中，图1的发动机13开启并且用于对发电机16供电，并且因此将ESS28的SOC保持在校准后的SOC阈值或者高于该校准后的SOC阈值，在该电量消耗模式中，发动机13关闭并且SOC被允许降低到低阈值水平。
图7的示例实施例说明了驾驶循环中具有中功率(MP)、高功率(HP)和低功率(LP)组的推进能量使用量的优化，其中优化电动行驶里程以减少排放并增加电动行驶里程。图7的迹线80描绘了分别对图3和4的逻辑块38和40的模式选择的影响。图7的迹线80和图7A的迹线180示出了车辆10在电量消耗模式下以中功率(MP)组开始，即，发动机13关闭并且允许SOC在没有发动机13操作的情况下逐渐降低或缓降为零。由于SOC在车辆10到达其行驶路线终点之前的高功率(HP)操作期间在图7A的t3大部分耗尽，所以控制器50根据需要通过循环开启和关闭发动机13来在t3切换到电量保持模式。然而，这发生在可能不如在电量消耗模式下继续操作那样有效的时刻。
在图7中达到相同的较低SOC极限，如迹线80所表明。然而，通过本方法100的操作，控制器50在高功率(HP)操作期间在t1处做出较早的动力系控制决策来开启发动机13。因此，图1的ESS28的SOC在高功率操作期间保持在电量保持模式，直到控制器50确定车辆10将以电量消耗模式到达其目的地为止，此时控制器50命令发动机13在t2处关闭。紧接着，动力系20变换为低功率(LP)操作。
因此，增加了图2的优化逻辑块60允许控制器50使用图2的非车载数据34和车载数据36来计算具有各种可能的能量源11的车辆10的电量保持操作并将其改变为更有效的驾驶状态。在驾驶循环开始之前提供前瞻性能量使用量预测，这通过图3的逻辑块38的操作来进行。在逻辑块38和40之间建立的反馈回路随时间工作，以提高逻辑块38的前瞻性预测准确度。在其中车辆10缺少发动机13的实施例中，方法100仍然可以用于优化里程预测，其中输出信号(箭头CCo)潜在地用于在控制器50的用户界面上显示更新的里程。
另外，本方法的前瞻性/预测性和自适应性可以容易地扩展到自主车队。这样的车队中的车辆可能在开始行程之前载入了目的地。可以准确评估一旦这样的自主车辆在路线上就增加的重新路线或计划外的权衡点，以确定车辆到达其目的地的能力。对地形、环境影响和交通信息的前瞻性预测可以使得乘客拥有更可靠的体验。类似地，一旦到达原始目的地的车程完成，车队拥有者就可以更好地理解自主车辆和/或客户的“返家”行程。
虽然已经参考所说明实施例详细地描述了本公开的多个方面，但是本领域技术人员将认识到，在不脱离本公开的范围的情况下可以对实施例做出许多修改。本公开不限于本文所公开的精确结构和组成；上述描述中显而易见的任何和所有修改、改变和变动都在如所附权利要求书中限定的本公开的精神和范围内。另外，本概念明确地包括前述元件和特征的任何和所有组合和子组合。

Claims (8)

1.一种用于与具有动力系的车辆一起使用的方法，所述动力系包括电机和变速器，所述电机由能量存储系统选择性地激励以产生电动机转矩，所述变速器被配置为从所述电机接收所述电动机转矩并且向一组驱动轮传递输出转矩，所述方法包括：
经由控制器接收非车载和车载数据；
当所述车辆沿着预定行驶路线行驶时使用所述非车载数据、所述车载数据和第一逻辑块来预测所述动力系的能量消耗；
使用所述车载数据、所述非车载数据以及第二逻辑块与所述第一逻辑块之间的纠错回路来修正所述预测能量消耗；以及
经由所述控制器使用修正后的能量消耗对所述车辆执行控制动作，这包括通过将输出信号传输到显示屏以由此显示所述车辆的估计电动行驶里程来改变所述车辆的逻辑状态和/或控制所述动力系的操作模式。
2.根据权利要求1所述的方法，其中执行所述控制动作包括：
使用所述修正后的能量消耗来更新所述车辆相对于所述预定行驶路线的估计的电动行驶里程；以及
经由所述显示屏显示所述更新的估计电动行驶里程。
3.根据权利要求1所述的方法，其中所述车辆包括内燃机，所述内燃机选择性地对所述电机供电以产生所述电动机转矩，并且改变所述逻辑状态包括开启所述发动机以便将所述车辆从其中所述ESS被放电到第一阈值充电状态(SOC)的电量消耗模式转变为所述ESS的所述SOC保持在高于所述第一SOC的第二SOC以上的电量保持模式。
4.根据权利要求3所述的方法，进一步包括将沿着所述预定行驶路线的所述预测能量消耗分成具有不同相对功率水平的功率组，并且在所述最高功率组期间开启所述发动机以由此进入所述电量保持模式。
5.根据权利要求1所述的方法，其中接收所述非车载数据包括描述所述预定行驶路线的高程的高程数据、描述所述车辆沿着所述预定行驶路线的估计速度的路线速度数据、描述所述预定行驶路线的环境的环境数据、描述所述车辆的坐标的位置数据以及描述沿着所述预定行驶路线的交通状况的实时交通数据，所述环境数据包括
以下一个或多个：风速和风向、降雨量和太阳能负载。
6.根据权利要求5所述的方法，其中接收所述车载数据包括接收所述变速器的流体温度、所述车辆的加热、通风和空调使用量数据以及所述ESS的充电状态。
7.根据权利要求5所述的方法，进一步包括使用所述流体温度经由旋转损失逻辑块计算所述变速器的旋转损失，并且使用所述旋转损失来预测所述车辆的所述能量消耗。
8.根据权利要求5所述的方法，进一步包括经由所述控制器的增量速度逻辑块来计算增量速度值，所述增量速度值指示所述车辆沿着所述预定行驶路线的预测加速度，并且使用所述增量速度值来预测所述车辆的所述能量消耗。
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