CN104898063B - 车辆、系统和方法 - Google Patents

车辆、系统和方法 Download PDF

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Publication number
CN104898063B
CN104898063B CN201510098314.4A CN201510098314A CN104898063B CN 104898063 B CN104898063 B CN 104898063B CN 201510098314 A CN201510098314 A CN 201510098314A CN 104898063 B CN104898063 B CN 104898063B
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battery unit
battery
parameter
model
variable quantity
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CN104898063A (zh
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杰森·梅尔
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Ford Global Technologies LLC
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Ford Global Technologies LLC
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K6/00—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 Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00
    • B60K6/20—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 Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs
    • B60K6/42—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 Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs characterised by the architecture of the hybrid electric vehicle
    • B60K6/44—Series-parallel type
    • B60K6/442—Series-parallel switching type
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B60L15/2009—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for braking
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    • B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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    • B60L50/00—Electric propulsion with power supplied within the vehicle
    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
    • B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
    • B60L50/66—Arrangements of batteries
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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]
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • 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
    • B60L58/18—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules
    • B60L58/21—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules having the same nominal voltage
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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    • B60L2210/10—DC to DC converters
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L2240/40—Drive Train control parameters
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    • B60L2240/545—Temperature
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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Abstract

本发明提供一种车辆、系统和方法，所述车辆可包括：电池单元；控制器，被配置为基于电池单元的状态观测利用模型参数来控制车辆，其中，根据通过远程确定的电池单元变化量参数而调整的控制器确定的电池单元标称参数来计算模型参数，响应于与被发送至外部计算装置的单元电池模型相关的电池单元测量值从外部计算装置接收变化量参数，变化量参数指示用于提高标称参数的准确度的调整量。

Description

车辆、系统和方法
技术领域
本公开涉及通过利用云计算资源来提高电池荷电状态估计。
背景技术
现代的混合动力车辆和电动车辆利用电池组来提供用于推进的能量以及储存再生的能量。电池组通常包括多个单独的电池单元，电池单元可按照并联、串联或者并联与串联的特定组合而连接。电池组的一个特性是荷电状态(SOC)。电池组SOC是保留在电池组中的那部分总电荷的测量值，并且可被认为类似于燃料表。对于确定何时及如何对电池组充电或放电而言，SOC可以是重要的。对于向驾驶者提供信息(诸如车辆可行驶里程)以及操作车辆而言，SOC也可以是重要的。电池组的其它特性包括限定最高定额的电池功率极限，在最高定额下，在不损坏电池或任何其它连接的系统的情况下，电池可以安全地释放所储存的能量(即，放电功率极限)以及可以安全地储存附加的能量(即，充电功率极限)。
发明内容
在第一示例性实施例中，一种车辆包括：电池单元；控制器，被配置为基于电池单元的状态观测利用模型参数来控制车辆，其中，根据通过远程确定的电池单元变化量参数而调整的控制器确定的电池单元标称参数来计算模型参数，响应于与被发送至外部计算装置的单元电池模型相关的电池单元测量值从外部计算装置接收变化量参数，变化量参数指示用于提高标称参数的准确度的调整量。
根据本发明的一个实施例，所述控制器还被配置为根据电池单元测量值的函数来估计标称参数，并且电池单元测量值包括电池单元电压、电池单元电流和电池单元温度。
根据本发明的一个实施例，每个标称参数具有相关联的变化量参数，控制器还被配置为将模型参数中的每个识别为(i)标称参数与相关联的变化量参数的总和以及(ii)标称参数与相关联的变化量参数的乘积中的一个。
根据本发明的一个实施例，电池模型包括电池等效电路模型，控制器还被配置为执行状态观测，以估计电池荷电状态(SOC)。
根据本发明的一个实施例，测量值还包括电池SOC的先前估计值。
根据本发明的一个实施例，控制器还被配置为根据安培小时积分而计算SOC的初始估计值。
在第二示例性实施例中，提供一种系统，所述系统包括计算装置。所述计算装置被配置为：经由通信网络从车辆接收与车辆的电池单元的电池模型相对应的电池单元测量值；响应于所述测量值，通过通信网络将表示对由车辆针对电池单元确定的电池单元标称参数的调整量的电池单元变化量参数发送至车辆，变化量参数指示用于提高标称参数的准确度的调整量。
根据本发明的一个实施例，测量值包括电池单元电压的测量值、电池单元电流的测量值和电池单元温度的测量值。
根据本发明的一个实施例，电池模型包括：电池等效电路模型，被配置为供应电池等效电路模型参数，以估计电池荷电状态(SOC)。
根据本发明的一个实施例，测量值还包括先前的电池SOC估计值。
根据本发明的一个实施例，计算装置还被配置为：根据电池单元测量值的函数而估计标称参数；将电池单元变化量参数计算为根据电池等效电路模型识别的模型参数与标称参数之间的差异。
根据本发明的一个实施例，计算装置还被配置为：通过根据电池等效电路模型识别的模型参数的直接识别而计算电池单元变化量参数。在第三示例性实施例中，一种方法包括：基于电池单元的状态观测利用模型参数通过控制器来控制车辆，其中，根据通过远程确定的电池单元变化量参数而调整的控制器确定的电池单元标称参数来计算模型参数，响应于与被发送至外部计算装置的单元电池模型相关的电池单元测量值从外部计算装置接收变化量参数，变化量参数指示用于提高标称参数的准确度的调整量。
根据本发明的一个实施例，所述方法还包括：根据电池单元测量值的函数来估计标称参数，其中，电池单元测量值包括电池单元电压、电池单元电流和电池单元温度。
根据本发明的一个实施例，每个标称参数具有相关联的变化量参数，并且所述方法还包括：将模型参数中的每个识别为(i)标称参数与相关联的变化量参数的总和以及(ii)标称参数与相关联的变化量参数的乘积中的一个。
根据本发明的一个实施例，电池模型包括电池等效电路模型，所述方法还包括：执行状态观测，以估计电池荷电状态(SOC)。
根据本发明的一个实施例，测量值还包括电池SOC的先前估计值。
根据本发明的一个实施例，所述方法还包括：根据安培小时积分而计算SOC的初始估计值。
附图说明
图1是示出了动力传动系和能量储存组件的混合动力电动车辆的示意图；
图2是示出了包括多个电池单元且由电池控制模块监测与控制的可能的电池组布置的示意图；
图3是示例性的电池单元等效电路的示意图；
图4是将电池参数划分为标称值和变化量值(delta value)的修改的电池单元等效模型的示意图；
图5是用于从车辆卸载(offload)参数识别任务的基于云的架构的示意图；
图6是利用基于云的架构的电池荷电状态观测器架构的示意图，其中，基于云的架构用于将变化量参数值(delta parameter value)的计算卸载到云计算资源；
图7至图8是用于将多方面的参数识别任务从车辆卸载到基于云的架构的方法的示意图。
具体实施方式
根据需要，在此公开了本发明的具体实施例，然而，应该理解的是，所公开的实施例仅为本发明的示例，并且本发明可以以多种和替代形式实施。附图不一定按百分比绘制；可放大或缩小一些特征以示出特定组件的细节。因此，在此所公开的具体结构和功能性细节不应解释为限制，而仅为用于教导本领域技术人员多样地采用本发明的代表性基础。
SOC估计可利用基于模型的方法(诸如卡尔曼滤波)来确定SOC。基于模型的方法通过限定电池单元的模型而工作，接着，基于特定的实际测量值而预测电池单元的内部状态。所估计的内部状态可包括(但不限于)电池内电阻或SOC。典型的方法是针对电池组的每个电池单元应用卡尔曼滤波器，接着，利用这些电池单元值计算总的电池组特性。这可需要电池控制模块和存储器，电池控制模块能够执行数量等于电池组中存在的多个电池单元的数量的多个卡尔曼滤波器，存储器能够存储被建模的电池单元中的每个的状态信息。电池组中的电池单元的数量不同，现代的车辆电池组可包括62至100个或者更多个电池单元。
不是仅仅由车辆单独执行SOC估计，而是各方面的SOC计算可卸载到车辆外部的计算服务。一个这样的外部计算服务可包括通过从车辆到因特网的网络连接可访问的基于云的计算服务器。例如，利用基于模型的方法进行SOC估计包括参数识别和状态观测的分离，参数识别任务可卸载到外部服务，从而减少计算量以及将要由车辆保持的状态。在这一方法中，车辆可收集关于车辆电池的输入信息(例如，电压、电流、温度、SOC等)，将所述信息转发到外部服务，并且从服务接收模型参数(例如，电池等效电路模型)，以在车辆SOC估计中使用。
与利用车辆的计算资源可进行的计算相比，通过结合外部计算服务的计算能力，车辆可能够具有更复杂和更准确的电池模型的优势。然而，由车辆接收外部识别的电池参数的速率可比在电池控制模块中使用的计算速率慢得多。因此，随着实际的电池状态连续变化，在更新之间的时间段中参数误差会增大，从而降低依赖于外部计算的参数(例如，电池功率极限、SOC估计值等)的计算值的准确性。通过将电池参数的确定划分为通过函数计算的已知的标称前馈部分和被计算为函数的变化量(delta)的未知的动态识别部分，车辆可被配置为提高卸载的参数估计的稳健性，允许更慢的云更新速率以及车辆更好的从断断续续的数据丢失中恢复。
虽然关于车辆的SOC计算讨论了所公开的方法，但是应该注意的是，所述方法可应用于其它类型的模型识别或基于模型的控制或估计应用，其中，与控制器或估计器确定系统值的速率相比，模型识别算法可以按照更慢的速率更新。
图1描绘了混合动力电动车辆2。典型的混合动力电动车辆2可包括机械地连接至混合动力变速器6的一个或更多个电动机4。此外，混合动力变速器6机械地连接至发动机8。混合动力变速器6还机械地连接至驱动轴10，驱动轴10机械地连接至车轮12。当发动机8开启或关闭时，电动机4可提供推进力和减速能力。电动机4也用作发电机，并且可通过回收在摩擦制动系统中通常将作为热损失掉的能量而提供燃料经济性效益。由于混合动力电动车辆2可在特定状况下按照电动模式运转，电动机4还可以提供减少的污染物排放。
电池组14储存可以由电动机4使用的能量。车辆电池组14通常提供高压直流(DC)输出。电池组14电连接至电力电子模块(power electronics module)16。电力电子模块16还电连接至电动机4，并且提供在电池组14与电动机4之间双向传输能量的能力。例如，典型的电池组14可以提供DC电压，而电动机4可能需要三相交流(AC)电流来运转。电力电子模块16可以将DC电压转换为电动机4所需要的三相AC电流。在再生模式下，电力电子模块16将来自用作发电机的电动机4的三相AC电流转换为电池组14所需要的DC电压。在此所描述的方法同样可应用于纯电动车辆或者利用电池组的任何其它装置。
电池组14除了提供用于推进的能量之外，还可以提供用于其它的车辆电气系统的能量。典型的系统可包括将电池组14的高压DC输出转换为与其它的车辆负载兼容的低压DC电源的DC/DC转换器模块18。其它高压负载可直接连接，而不使用DC/DC转换器模块18。在典型的车辆中，低压系统电连接至12V电池20。
可以通过多种化学配方构建电池组14。典型的电池组14的化学成分可以是铅酸、镍金属氢化物(NIMH)或锂离子。图2示出了N个电池单元32简单串联配置的典型的电池组14。然而，其它电池组14可由任何数量的单独的电池单元按照串联或并联或它们的特定组合连接而组成。典型的系统可具有一个或更多个控制器(诸如用于监测并控制电池组14的性能的电池控制模块(BCM，battery control module)36)。BCM 36可以监测多个电池组14的水平特性(诸如电池组电流38、电池组电压40以及电池组温度42)。
除了测量和监测电池组的水平特性外，还可测量和监测电池单元的水平特性。例如，可以测量每个电池单元的端电压(terminal voltage)、电流和温度。系统可使用传感器模块34来测量电池单元的特性。根据性能，传感器模块34可以测量一个或多个电池单元32的特性。电池组14可利用多达Nc个传感器模块34来测量所有电池单元32的特性。每个传感器模块34可将测量值传输至BCM 36，以进行进一步处理和协调。传感器模块34可将模拟形式或数字形式的信号传输至BCM 36。
图3是示例的电池单元等效电路模型48的示意图。电池单元可被建模为电压源(Voc)50，电压源(Voc)50具有与其相关联的电阻(52和54)和电容56。更具体地说，电池单元等效电路模型48可以是兰德尔斯电路(Randles circuit)，具有有源电解质电阻(activeelectrolyte resistance)R152、并联的电容C256和有源电荷转移电阻(active chargetransfer resistance)R254，并联的电容C256和有源电荷转移电阻R254与有源电解质电阻R152串联。由于电池单元阻抗，导致端电压(Vt)58通常不与开路电压Voc 50相同。开路电压Voc 50不容易被测量，而只有电池单元的端电压58易于被测量。由于开路电压Voc 50不容易被测量，所以可以利用基于模型(诸如兰德尔斯模型)的方法来估计开路电压Voc 50的值。虽然在此使用兰德尔斯电路模型作为示例，但是也可以使用其它电池模型。例如，模型48可包括高阶兰德尔斯电路模型、另一类型的电池等效电路模型、电池电化学模型或特定的电池单元或电池单元的组件的其它模型。
图3中的兰德尔斯等效电路模型可允许预测的电池系统参数的计算。可以通过下面的等式来表示兰德尔斯电路模型：
其中，V2是R-C电路的电压，I是激励电路的电流，t是时间，R2(即，电阻54)和C2(即，电容56)表示车辆运转期间的电池动态变化。对于具有容量Q的电池而言，SOC可被如下定义为可用电荷与最大电荷容量Q的百分比：
此外，端电压Vt 58可被建模如下：
Vt(t)＝I(t)R1+V2(t)+foc(SoC)(t)) (3)
图4是将电池参数划分为标称参数值62部分和变化量参数值64部分的修改的电池单元等效模型60的示意图。在修改的模型60中，电池系统仍可被建模为兰德尔斯电路模型(即，可期望的任意阶的兰德尔斯电路模型)。然而，每个电路元件(例如，电阻52、电阻54和电容56等)可由与总的电路元件等效的一组多个电路元件来表示。例如，每个电阻可被建模为一对串联连接的电阻，每个电容可被建模为一对并联连接的电容。每对可包括：理想的元件，根据操作条件(例如，温度、SOC、健康状态等)的平滑函数而变化；黑盒子(blackbox)、变化量或未知元件，表示除了由理想元件捕获的其它元素以外的所有元素。
利用修改的电池单元等效模型60的结构，每个电池参数的标称值或理想值可以根据平滑函数按照前馈方式来描述。结果，电池参数估计系统需要仅仅估计黑盒子元件的值。这些黑盒子元件可被建模为关于理想值的变化量扰动。例如，电阻52可被建模为标称参数值62R1,0和变化量参数值64ΔR1，使得电阻52的总值等于被划分的值的和(R1＝R1,0+ΔR1)。类似地，电阻54可被建模为标称参数值62R2,0和变化量参数值64ΔR2，使得电阻54的总值等于被划分的值的和(R2＝R2,0+ΔR2)。此外，电容56可被建模为标称参数值62C2,0和变化量参数值64ΔC2，使得电容56的总值等于被划分的值的和(C2＝C2,0+ΔC2)。
考虑这些求和的部分，修改的一阶兰德尔斯电路模型60可因此被表示为如下的等式：
此外，端电压Vt58可被表示为如下：
Vt(t)＝I(t)(R1，0+ΔR1)+V2(t)+foc(SoC(t)) (5)
不考虑修改的模型中的这些差异，SOC可继续被建模为如等式2中所表示。
作为可替换的，与变化量参数值64给定加法效应的模型60相对照，可以利用变化量参数值64给定乘法效应的修改的电池单元等效模型60。例如，电阻52可被建模为标称参数值62R1,0和变化量参数值64ΔR1，使得电阻52的总值等于被划分的值的乘积，即，R1＝R1,0(1+ΔR1)。类似地，电阻54可被建模为标称参数值62R2,0和变化量参数值64ΔR2，使得电阻54的总值等于被划分的值的乘积，即，R2＝R2,0(1+ΔR2)。此外，电容56可被建模为标称参数值62C2,0和变化量参数值64ΔC2，使得电容56的总值等于被划分的值的乘积，即，C2＝C2,0(1+ΔC2)。
因此，所述可替换的修改的一阶兰德尔斯电路模型60可以通过下面的等式来表示：
此外，端电压Vt58可被表示为如下：
Vt(t)＝I(t)R1，0(1+ΔR1)+V2(t)+foc(SoC(t)) (7)
在任何一个模型60中，标称值62可被预测为电池温度和可能的其它变量(诸如累积的安培小时吞吐量或SOC)的函数。在一些情况下，前馈参数估计可随着电池的寿命而缓慢地适应(例如，随着电池老化，考虑电池的行为中的容量Q的差异)。可通过利用电路模型60的扩展卡尔曼滤波器来识别变化量参数值64。例如，变化量参数值64可被确定为标称参数值62与通过滤波器确定的模型参数之间的差异。可选择地，变化量参数可以由卡尔曼滤波器直接识别。特别地，与变化量参数值的确定相比，标称参数值62前馈确定可明显减少计算且可集中存储。
图5是用于从车辆卸载计算任务的基于云的架构70的示意图。架构70包括将车辆与云模块72通信的BCM 36，其中，云模块72被配置为使得车辆便于与云计算资源74通信。
如架构70中所示，如箭头(1)所示，BCM 36可被配置为管理电池并执行各种电池变量的电池单元测量。此外，如箭头(2)所示，BCM 36还可被配置为将输入传送至云模块72。云模块72可被配置为接收所述输入，并且如箭头(3)所示，通过移动链路(例如，通过车辆远程信息处理控制器)将所述输入传送至云计算资源74。云计算资源74可被配置为基于所接收的输入而执行计算。如箭头(4)所示，云计算资源74还可被配置为通过移动链路将包括计算结果的响应返回至云模块72。云模块72可接收所述响应，并且如箭头(5)所示，将计算的结果回传至BCM 36。因此，BCM 36可利用由资源74所计算的计算结果。因此，通过利用基于云的架构70，车辆能够卸载计算任务，这样的计算任务对于BCM 36利用其自身的计算能力来计算可能是代价昂贵或困难的。
图6是利用基于云的架构70的电池荷电状态观测器架构80的示意图，其中，基于云的架构70用于将变化量参数值64的计算卸载到云计算资源74。在架构80中，可通过云计算资源74计算变化量参数值64，并且可以通过云计算资源74周期性地(例如，按照离散的间隔，诸如每秒、每15秒、每3分钟等)提供变化量参数值64。此外，不考虑与云计算资源74的通信速率或连接状态，可通过BCM 36计算标称值62的计算值。变化量参数值64可供车辆使用，以通过向由BCM 36所确定的标称值62应用由云计算资源74所识别的变化量值而更新模型参数92的计算。由于不考虑从云计算资源74接收的更新值，而基于当前的电池测量值而执行标称值62的计算，所以架构80提供云更新值之间的参数误差增长速率的降低，并提供更稳健的基于云的电池状态确定。
更具体地讲，架构80包括输入模块82，输入模块82被配置为接收传感器输入，并提供由架构80的其它模块使用的输入。基于模型的SOC估计模块86可被配置为从输入模块82接收模型参数92并估计SOC 98。云模块72可被配置为将来自输入模块82的电池模型输入96以及所估计的SOC 98传送至云计算资源74，并接收由云计算资源74根据修改的电池等效电路模型60而计算的变化量参数值64。参数估计模块84可被配置为周期性地(例如，按照离散的间隔，诸如每秒、每15秒、每3分钟等)从云计算资源74接收变化量参数值64，根据从输入模块82接收的参数估计输入90而确定标称值62，并执行标称值62与变化量参数值64的组合，从而确定模型参数92。功率极限计算模块88可被配置为基于诸如电池年龄和温度的因素而确定电池的功率极限(例如，可传递进入电池以及从电池中传递出的最高定额能量)。特别地，可不管云通信速率和云连接状态而通过车辆连续地计算标称值62的计算值。因此，不考虑从云接收的更新而进行的标称值62的计算提供了云更新之间的参数误差增长速率的降低，也提供了更稳健的基于云的电池控制系统。
输入模块82可被配置为接收将要在计算模型参数92中被使用的输入。对于修改的电池等效电路模型60而言，作为示例，这些输入可包括电压、电流和温度。通过输入模块82收集的输入可包括由BCM 36从传感器模块34接收的信息(诸如，电池单元水平电压测量值、电流测量值和温度测量值)以及由BCM 36接收的电池组14的水平特性(诸如电池组电流38、电池组电压40和电池组温度42)。
基于模型的SOC估计模块86可被配置为将所收集的电池模型输入96和所估计的SOC 98提供至云模块72(例如，架构70中所示的箭头2)，以通过云计算资源74进行处理。例如，可根据电池组14在点火开关接通时的开路电压(OCV)的测量值或者根据不需要模型参数92的SOC计算的方法(诸如根据从输入模块82接收的当前测量的电池模型输入而计算的安培小时积分)，来确定最初的估计的SOC 98。
云计算资源74可被配置为接收从输入模块82传递的电池模型输入96和估计的SOC98，并计算变化量参数值64。云计算资源74可根据上面详细讨论的修改的电池等效电路模型60而执行变化量参数值64的计算。例如，云计算资源74可利用扩展卡尔曼滤波器来计算模型参数92，并且可将变化量参数值64确定为模型参数92与标称参数值62之间的差异(例如，标称参数值62也通过云计算资源74利用与BCM 36所使用的函数相同的函数进行计算而得到，或者通过云计算资源74从BCM 36接收而得到，等等)。变化量参数可以可替换地通过卡尔曼滤波器直接识别。云计算资源74还可被配置为将所确定的变化量参数值64通过云模块72返回至参数估计模块84(例如，架构70中的箭头5所示)。
参数估计模块84可被配置为根据接收的参数估计输入90的函数而估计标称参数值62。例如，参数估计输入90可包括电池温度和可能的其它变量(诸如累积的安培小时吞吐量或SOC 98)。基于标称参数值62和变化量参数值64，参数估计模块84可被配置为计算电池模型参数92。例如，使用修改的电池等效电路模型60，可将电阻52、电阻54和电容56的值确定为相对应的标称参数值62和变化量参数值64的总和。或者，利用可替换的修改的电池等效模型60，可通过将变化量参数值64对相应的标称参数值62应用乘法效应而确定电阻52、电阻54和电容56的值。接着，模型参数92可被提供至基于模型的SOC估计模块86。
基于模型的SOC估计模块86可被配置为估计电池组14的SOC 98。例如，可根据从输入模块82接收的模型参数92而驱动SOC估计模块86的观测器。可根据模型(诸如图4中所示的修改的电池单元等效电路模型60)实现观测器，并详细解释如下。
功率极限计算模块88可被配置为确定电池组14的总容量。例如，功率极限计算模块88可被配置为基于诸如电池年龄和温度的因素而确定电池的功率极限(例如，可传递进入电池以及从电池中传递出的最高定额能量)。
特别地，在接收的变化量参数值64的更新之间，BCM 36可利用更新的前馈标称值62和最新接收的变化量参数值64而持续估计SOC 98。因此，利用架构80，标称参数值62可连续更新(前馈)，同时，可基于云计算资源74的更新速率周期性地更新变化量参数值64。因此，标称参数值62的连续更新导致在来自云计算资源74的更新值的接收之间的显著降低的模型参数92误差增长速率。此外，对于云信息丢失和断断续续的云连通性而言，由参数估计模块84提供的模型参数92的估计值还可以对于车辆更稳健。
图7是用于从车辆卸载参数识别任务的方法700的示意图。例如，可通过与云计算资源74通信的车辆的云模块72和BCM 36来执行方法700。
在判定点702处，BCM 36确定电池测量间隔时长(timeout)是否已经过去。例如，BCM 36可被配置为周期性地(例如，每秒、每15秒、每3分钟等)执行电池测量。如果电池测量间隔时长已经到期，则控制行进到框704。否则，控制保持在判定点702。
在框704处，BCM 36执行电池测量。例如，BCM 36可利用车辆的传感器模块34来测量电池单元特性，诸如，可测量每个电池单元的电压、电流和温度。作为另一示例，BCM 36可测量电池组14的水平特性(诸如电池组电流38、电池组电压40和电池组温度42)。
在框706处，BCM 36更新标称参数。例如，将电池测量值用作参数估计输入90，参数估计模块84可根据平滑函数按照前馈方式来预计每个电池参数的标称参数值或理想参数值。
在判定点708处，BCM 36确定云更新间隔时长是否已经过去。例如，BCM 36可被配置为周期性地(例如，每秒、每15秒、每3分钟等)将电池测量值提供至云模块72。在多个示例中，云更新间隔时长可以是比电池测量间隔时长更长的间隔时长，使得标称值更新比云更新更快。如果云更新间隔时长已经过去，则控制进行到框712。否则，控制进行到框710。
在框710处，BCM 36存储电池测量值。例如，BCM 36可保持一组或更多组电池测量值，以在云更新间隔时长期满时将一组或更多组电池测量值传送至云模块72。在框710之后，控制行进到判定点702。
在框712处，BCM 36将电池测量值提供至云模块72。例如，BCM 36可通过车辆总线将包括电池测量值的一个或更多个消息传送至云模块72。BCM 36还可将其它信息(诸如根据由BCM 36执行的早期状态观测而确定的先前计算的SOC值)提供至云模块72，所述其它信息对于基于所使用的模型的模型参数的确定可以是有用的。在框712之后，控制可行进到判定点702。
在判定点714处，BCM 36确定SOC确定间隔时长是否已经过去。例如，BCM 36可被配置为确定供车辆系统(诸如混合动力传动系)使用的SOC或用于指示当前的电荷水平的车辆的用户接口元素。也可周期性地(例如，每秒、每15秒、每3分钟等)执行SOC确定。SOC确定间隔时长已经到期，则控制行进到框716。否则，控制保持在判定点714处。
在框716处，BCM 36更新模型参数92。例如，利用图4中示出并在上面详细解释的修改的电池单元等效模型60的结构，参数估计模块84可根据从输入模块82接收的参数估计输入90的平滑函数按照前馈形式来预测每个电池参数的标称值或理想值。例如，参数估计输入90可包括电池温度和可能的其它变量(诸如累积的安培小时吞吐量或SOC 98)。参数估计模块84还可利用最新的变化量参数值64，以通过向所确定的标称值62施加云计算资源74识别的变化量来更新模型参数92的计算。例如，利用修改的电池单元等效模型60，可将电阻52、电阻54和电容56的值确定为相对应的标称参数值62和变化量参数值64的总和。或者，利用可替换的修改的电池等效模型60，可通过将变化量参数值64对相应的标称参数值62应用乘法效应而确定电阻52、电阻54和电容56的值。接着，模型参数92可被提供至基于模型的SOC估计模块86。
在框718处，BCM 36确定SOC。例如，BCM 36的SOC估计模块86可从参数估计模块84接收模型参数92，并且可估计电池组14的SOC 98。可根据从输入模块82接收的SOC输入94而驱动模型参数92的SOC估计模块86的观测器。可根据模型(诸如上面详细讨论的修改的电池等效电路模型60)来实现观测器。在框718之后，控制可行进到判定点714处。
在判定点720处，BCM 36确定BCM 36是否接收到更新的变化量参数值64。例如，BCM36可识别BCM 36是否从云模块72接收到变化量参数值64，或者可识别所接收到的变化量参数值64是否比当前被BCM 36保持的变化量参数值64新。如果确定BCM 36接收到更新的变化量参数值64，则控制行进到框722。否则，控制保持在判定点720处。
在框722处，BCM 36更新被BCM 36保持的变化量参数值64。在框722之后，控制行进到判定点720处。
图8是用于从车辆卸载参数识别任务的方法800的示意图。例如，可通过与接口连接到BCM 36的云模块72通信的云计算资源74来执行方法800。
在判定点802处，云计算资源74确定是否已经从车辆接收电池测量值。例如，车辆的云模块72可能已经将消息提供至云计算资源74(诸如上面关于方法700所讨论的)。如果已经接收到消息，则控制行进到框804，以处理消息。否则，控制保持在判定点802处。
在框804处，云计算资源74利用所接收的测量值计算变化量参数值64。例如，云计算资源74可根据上面详细讨论的修改的电池单元等效电路模型60来执行变化量参数值64的计算。云计算资源74可利用卡尔曼滤波器来确定模型参数92，并且可将变化量参数值64确定为模型参数92与标称参数值62之间的差异(例如，标称参数值62也通过云计算资源74进行计算而得到，或者通过云计算资源74从BCM 36接收而得到，等等)。
在框806处，云计算资源74向车辆提供响应(包括所计算的模型参数)。例如，云计算资源74可被配置为将确定的变化量参数值64返回至云模块72，以通过车辆返回至参数估计模块84。因此，通过利用基于云的结构70，车辆能够卸载模型参数的确定，这样的模型参数的确定对于BCM 36利用其自身的计算能力来计算可能是代价昂贵或困难的。在框806之后，控制行进到判定点802处。
因此，通过将模型参数92的确定划分为已知的标称值62前馈函数部分和未知的变化量参数值64部分，车辆可被构造为提高SOC计算的准确度，以超出车载BCM 36的计算和存储约束，同时，根据用于卸载整个模型参数92的计算的方法可能的需要，还允许车辆与云计算资源74之间的更慢的云更新速率。由于模型参数92相对较慢的变动和所涉及的计算复杂性，根据修改的电池等效电路模型60的变化量参数值64的更新值的计算可适合于被卸载到云计算资源74。此外，更新的标称参数值62的计算值可适合于通过车辆来执行，从而与简单依靠远程计算的模型参数相比，提供更好的SOC估计，同时还使车辆与云计算资源74之间的潜在的断断续续的数据丢失的影响最小化。
虽然上面描述了示例性实施例，但是并不意味着这些实施例描述了本发明的所有可能的形式。更确切地说，说明书中使用的词语为描述性词语，而非限制性词语，并且应理解的是，在不脱离本发明的精神和范围的情况下，可做出各种改变。此外，可组合多个实施例的特征以形成本发明的进一步的实施例。

Claims (6)

1.一种车辆，包括：
电池单元；
控制器，被配置为基于电池单元的状态观测利用模型参数来控制车辆，其中，根据通过远程确定的电池单元变化量参数而调整的控制器确定的电池单元标称参数来计算模型参数，响应于与被发送至外部计算装置的电池单元模型相关的电池单元测量值从外部计算装置接收电池单元变化量参数，电池单元变化量参数指示用于提高电池单元标称参数的准确度的调整量。
2.根据权利要求1所述的车辆，其中，所述控制器还被配置为根据电池单元测量值的函数来估计电池单元标称参数，并且电池单元测量值包括电池单元电压、电池单元电流和电池单元温度。
3.根据权利要求1所述的车辆，其中，每个电池单元标称参数具有相关联的电池单元变化量参数，所述控制器还被配置为将模型参数中的每个识别为电池单元标称参数与相关联的电池单元变化量参数的总和以及电池单元标称参数与相关联的电池单元变化量参数的乘积中的一个。
4.根据权利要求1所述的车辆，其中，电池单元模型包括电池单元等效电路模型，所述控制器还被配置为执行状态观测，以估计电池荷电状态。
5.根据权利要求4所述的车辆，其中，电池单元测量值还包括电池荷电状态的先前估计值。
6.根据权利要求4所述的车辆，其中，所述控制器还被配置为根据安培小时积分而计算电池荷电状态的初始估计值。
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