CN104723894B - 用于电池参数估计的车辆、方法和系统 - Google Patents
用于电池参数估计的车辆、方法和系统 Download PDFInfo
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Abstract
提供一种用于电池参数估计的车辆、方法和系统。一种车辆包括电池组和至少一个控制器,所述至少一个控制器被编程为实施电池组的模型。所述模型包括表示电池组的电荷转移阻抗和内阻的参数,并且所述参数从具有增广状态矢量的扩展卡尔曼滤波器被识别。所述增广状态矢量至少部分地由与电荷转移阻抗相关联的时间常数和表示电荷转移阻抗的电阻项与内阻之间的比例的变量定义,以减小所述参数的观察到的可变性。
Description
技术领域
本公开涉及一种电池管理方法和系统,所述方法和系统能够估计形成电池的元件的参数以提供对电池的控制。
背景技术
混合电动车辆(HEV)使用内燃机与电动机的组合来提供驱使车辆所需的动力。该配置提供了超过只具有内燃机的车辆的提高的燃料经济性。在HEV中提高燃料经济性的一种方法在于在发动机工作效率低下且不被另外需要来驱使车辆的时间期间关闭发动机。在这些情况下,与电池系统连接的电动机用于提供驱使车辆所需的全部动力。当驾驶员动力需求增加使得电动机不能够再提供满足需求的足够动力时,或者在诸如电池充电状态(SOC)低于特定水平时的其他情况下,发动机应当以对驾驶员几乎透明的方式快速且平稳地起动。
发明内容
一种车辆包括电池组和至少一个控制器,所述至少一个控制器被编程为实施电池组的模型。所述模型包括表示电池组的电荷转移阻抗和内阻的参数。所述参数由具有增广状态矢量的扩展卡尔曼滤波器识别,其中,所述增广状态矢量与引入的用于提高所述参数的可观察性的因数相关。所述因数包括与电荷转移阻抗相关联的时间常数和表示电荷转移阻抗的电阻项与内阻之间的比例的变量。所述至少一个控制器还被编程为根据所述参数操作电池组。
一种方法包括:基于表示电池组的电荷转移阻抗和内阻的参数对电池组进行充电和放电。所述参数由具有增广状态矢量的扩展卡尔曼滤波器识别,其中,所述增广状态矢量部分地由与电荷转移阻抗相关联的时间常数和表示电荷转移阻抗的电阻项与内阻之间的比例的变量定义,以减小所述参数的观察到的可变性。
所述变量是电荷转移阻抗的电阻项和内阻的商。
电荷转移阻抗与电池组的至少一个R-C电路表示法相关联。
将所述增广状态矢量应用于雅克比矩阵,其中,雅克比矩阵描述动态模型的电池参数值的变化。
所述增广状态矢量还包括表示电池动态响应的其他状态变量或表征电池动态的模型参数。
一种牵引电池系统包括具有一个或多个电池单元的电池组和被编程为实施所述一个或多个电池单元的模型的至少一个控制器。所述模型具有由滤波器识别的参数,其中,所述滤波器至少部分地由表示所述一个或多个电池单元的电荷转移阻抗的电阻项与所述一个或多个电池单元的内阻之间的比例的变量定义,以减小所述参数的观察到的可变性。所述至少一个控制器还被编程为根据所述参数控制所述一个或多个电池单元。
所述滤波器还由与电荷转移阻抗相关联的时间常数定义。
所述变量是电荷转移阻抗的电阻项和内阻的商。
电荷转移阻抗与电池组的至少一个R-C电路表示法相关联。
附图说明
图1是示出典型的动力传动系统和能量存储组件的混合电动车辆的示图;
图2是用于对锂离子电池建模的简单等效电路的示意图;
图3是用于识别在电池管理方法中使用的一个或多个电池模型参数的算法的流程图;
图4是示出在车辆中测量的或通过电池测试测量的电池电流输入分布和电压输出分布的曲线图;
图5是示出识别的电池内阻的比较的曲线图;
图6是示出R-C电路中的识别的电池模型参数的比较的曲线图。
具体实施方式
如需要的,在此公开本发明的具体实施例;然而,应理解的是,所公开的实施例仅是本发明的示例,本发明可以以各种替代形式实现。附图不必按比例绘制;一些特征可被夸大或最小化以示出特定组件的细节。因此,在此公开的特定的结构和功能的细节不应被解释为限制,而仅作为用于教导本领域技术人员不同地采用本发明的代表性基础。
HEV电池系统可执行估计描述电池组和/或一个或多个电池单元的当前操作条件的值的电池管理策略。电池组和/或一个或多个电池单元操作条件包括:电池充电状态、电力衰减(power fade)、容量衰减(capacity fade)和瞬时可用电力。电池管理策略能够估计电池单元随着电池组的寿命老化时的值。对一些参数的精确估计可提高性能和稳健性,并可最终延长电池组的使用寿命。对于这里描述的电池系统,可如下所述实现对一些电池组和/或电池单元参数的估计。
图1描绘典型的混合电动车辆。典型的混合电动车辆2可包括机械地连接到混合传动装置6的一个或多个电动机4。另外,混合传动装置6机械地连接到发动机8。混合传动装置6还机械地连接到传动轴10,传动轴10机械地连接到车轮12。在示图中未描绘的另一实施例中,混合传动装置可以是可包括至少一个电机的非可选的齿轮传动装置。电动机4可在发动机8开启或关闭时提供推进和减速能力。电动机4还充当发电机,并可通过回收在摩擦制动系统中通常将会作为热量损失的能量来提供燃料经济性利益。由于混合电动车辆2可在特定条件下在电动模式下运转,因此电动机4还可提供减少的污染物排放。
电池组14可包括存储可被电动机4使用的能量的一个或多个电池单元。车辆电池组14通常提供高电压DC输出。电池组14电连接到电力电子模块16。电力电子模块可包括组成车辆计算系统的一个或多个控制模块。车辆计算系统可控制若干车辆功能、系统和/或子系统。所述一个或多个控制模块可包括(但不限于)电池管理系统。电力电子模块16还电连接到电动机4,并提供在电池组14和电动机4之间双向传输能量的能力。例如,典型的电池组14可提供DC电压,而电动机4可能需要三相AC电流来运行。电力电子模块16可将DC电压转换成电动机4需要的三相AC电流。在再生模式下,电力电子模块16将把来自充当发电机的电动机4的三相AC电流转换成电池组14需要的DC电压。
除了提供用于推进的能量之外,电池组14还可提供用于其他车辆电系统的能量。典型的系统可包括DC/DC转换器模块18,DC/DC转换器模块18将电池组14的高电压DC输出转换成与其他车辆负载兼容的低电压DC供应。可不使用DC/DC转换器模块18而直接连接其他高电压负载。在典型的车辆中,低电压系统电连接到12V电池20。
电池组14可由车辆计算系统使用一个或多个控制模块控制。所述一个或多个控制模块可包括(但不限于)电池控制模块。所述一个或多个控制模块可被校准以使用电池模型参数估计方法控制电池组14,所述电池模型参数估计方法包括(但不限于):在操作期间对有效电池内阻的平均感测以确定电池电力容量。电力容量预测使电池组14能够防止可导致减少电池寿命的过充电和过放电、与车辆动力系统有关的性能问题和/或电池劣化。
车辆电池测量方法/算法可被实现为消除广泛的离线测试的需要。车辆电池测量方法可使用用于在车辆中测量电池组的一个或多个简单等效电路来获得操作期间的电化学阻抗。
图2是锂离子电池的简单等效电路的示意图。在此示例中,简单等效电路模型200包括Randles电路模型和/或至少一个R-C电路。Randles电路(和/或R-C电路)包括与并联的电容C1204和有源电荷转移电阻R1206串联的活性电解质电阻R0202。Randles电路可实现在HEV电池管理系统中以提供对于电池参数的预测计算。
HEV电池管理系统可使用扩展卡尔曼滤波器实现Randles电路模型以估计电池参数。电池参数可包括(但不限于)电池模型的电池电阻、电容和/或其他状态。估计的电池参数可包括当车辆处于包括电池充电、电池维持电荷或电池消耗电荷的特定系统模式下时增加的波动轨迹。当使用Randles电路估计这些电池参数时,这些电池参数往往对内部和外部噪声以及环境条件是敏感的。
系统可使用动态模型消除一个或多个电池参数的波动轨迹,其中,所述动态模型将模型电阻参数关联在一起并将系统时间常数设置为用于系统识别的独立变量。系统可观察性是可通过仅使用外部系统输出来推断内部状态如何的量度。然而,在使用动态模型的情况下,可观察性可提高,并且估计的参数可以对噪声较不敏感。
电池管理系统和方法可以基于Randles电路模型以提供系统的提高的可观察性和电池参数估计方法中的后续稳健性,而不增加系统复杂性。等效电路模型200可允许计算预测性的电池系统参数。Randles电路模型通过下面的
其中,如图2中所示,v1210是作为时间函数的包括R1206和C1204的R-C电路的电压,i208是激发电路的电流,R1206和C1204表示在车辆运行期间电池的动态变化。等效电路模型可允许使用下面的等式计算电池端电压:
vt=vOC-v1-R0i (2)
其中,vt212是端电压,vOC214是电池开路电压,R0202是电池内阻。用于计算/预测/估计电池参数的电池系统动态控制可包括形成电化学电池模型的材料属性的函数。因此,各个参数可利用下面的等式中示出的关系关联:
R1=k1R0 (3)
τ1=R1C1=k1R0C1 (4)
其中,k1是在车辆运行期间实时识别的引入参数,τ1是电路的实时动态响应。参数k1是电荷转移阻抗R1的电阻项与内阻R0的商。参数τ1是与电荷转移阻抗相关联的时间常数以减小参数的观察到的可变性。基于等式(3)和(4),从等式(1)导出下面的等式:
其中,v1210是在表示电路的动态响应的时间索引k1处的作为时间函数的R-C电路两端的电压。因此,在时间索引k处设置下面的变量:
v1=v1,k (5c)
i=ik (5d)
将变量设置在时间索引处能够使等式(5a)被如下重新整理:
yk=vOC,k-vt,k-R0ik=v1,k (7)
其中,yk是R-C电路两端的电压。模型参数R0和引入的模型参数k1和τ1增大了状态变量v1210。因此,在下面的等式中示出增广状态矢量X:
X=[v1 R0 k1 τ1]T (8)
增广状态矢量不限于等式(8)中公开的电池动态响应v1的参数和表征电池动态R0、k1和τ1的其他模型参数。例如,如果等效电路模型包括其他R-C电路和/或电池模型具有其他形式的电池动态表现,则一个或多个增广状态矢量参数可改变。
在下面的雅克比矩阵等式中示出使用增广状态矢量X的等式(8)的新的表达式:
其中,Fk是用于描述系统动态和模型参数改变的系统矩阵。
在下面的等式中示出使用增广状态矢量X的等式(7)的新的表达式:
其中,Hk是用于计算系统响应的输出矩阵。例如,该输出矩阵可实时计算电池端电压vt作为估计的系统响应。在下面的等式中示出系统矩阵Fk和输出矩阵Hk:
Hk=[1 ik 0 0] (10b)
如下面的等式中所示,模型参数用于预测在时间(t)期间施加恒定电流(i)时的电压响应:
可通过下面的等式计算电池电流限度:
可通过下面的等式计算电池电力容量Pcap:
Pcap=|ilim|vlim (14)
其中,等式(13)和等式(14)中的vlim是在放电条件下电池端电压的下限电压,等式(13)和等式(14)中的vlim是在充电条件下电池端电压的上限电压。例如,在电池放电事件期间,从等式(13)计算在时间段Δtd期间的电池放电电流限度。使用从等式(13)计算的电池放电电流限度,针对放电事件,从等式(14)计算电池可用电力。
改进的电池参数计算方法允许在实现减小对于电池使用的安全余量的同时对电池电力容量的增强估计,从而可在动力传动系统中更加积极地应用电池硬件。基于改进的方法,HEV控制变得更加灵活,从而导致动力传动系统性能和效率的提高。
图3是用于识别在电池管理方法中使用的一个或多个电池模型参数的算法的流程图。可使用在车辆控制模块内包含的软件代码来实现该方法。在其他实施例中,方法300可在其他车辆控制器中实现,或者分布在多个车辆控制器之中。
再参照图3,在对方法的整个论述中参考图1和图2中示出的车辆及其组件,以便于理解本公开的各个方面。在混合电动车辆中控制电池参数预测的方法可通过计算机算法、机器可执行代码或被编程到车辆的合适的可编程逻辑器件(诸如车辆控制模块、混合控制模块、与车辆计算系统通信的其他控制器或其组合)中的软件指令来实现。虽然流程图300中示出的各个步骤看似按时间顺序发生,但是至少一些步骤可按不同顺序发生,并且一些步骤可被同时执行或不被执行。
在步骤302,在允许车辆被通电的接通(key-on)事件期间,车辆计算系统可开始对一个或多个模块上电。对一个或多个模块上电可使得与电池管理系统相关的变量在步骤304在使一个或多个算法能够在车辆计算系统中执行之前初始化。
例如,由于在休息时具有自放电/电荷消耗状态的电池单元的动态,电池参数需要在接通事件期间被初始化。电池管理方法可在预测和更新电池端电压、电流限度和/或其他电池相关参数之前初始化增广状态矢量,以确保在电池组经历电荷消耗状态之后的系统可观察性和估计稳健性。电池电荷消耗状态可基于包括不充电时车辆掉电的时间长度、电池的寿命和/或环境条件的若干因素而变化。
在步骤306,一旦电池管理系统已经初始化一个或多个参数,系统就可预测电池电力容量。预测电池电力容量的操作允许系统确定在那时车辆计算系统可从电池转移多少电力。使用精确的预测算法提高电池的寿命、动力传动系统的性能以及在电池电动和/或混合电动车辆中由电池组供电的其他系统/子系统的性能。
用于电池管理系统的预测等式可包括扩展卡尔曼滤波器,并如以上等式中所呈现的使用引入的参数。基于模型的等式如下:
其中,是增广状态矢量,uk-1是输入电流,f是表示系统动态的非线性函数。
在步骤308,输入电流uk在特定操作点被传送至算法以允许系统预测该时间变量处的电池参数。模型参数用于预测在特定时间段期间施加恒定电流时的电压响应。基于以上等式和扩展卡尔曼滤波器(EKF)已知的变量,更新的滤波器等式现在可使用下面的等式预测下一状态的电池电力容量:
其中,(16a)是从函数f导出的雅克比矩阵,k-1是雅克比矩阵被计算的时间,P是状态估计误差的协方差矩阵,Fk-1包括具有电池参数和系统动态之间的物理关系的新参数。
在步骤310,在电池管理系统已经预测电池电力容量之后,系统可利用新的电池测量值更新电池管理系统。新的测量值基于预测的电池电力容量和下面的等式:
其中,是在步骤310中对测量的估计。电池系统的yk测量被接收和发送以用于在步骤312对新的电池测量预测值的更新。
下面的等式是用于确定卡尔曼增益Kk的中间等式:
用于确定卡尔曼增益Kk的等式如下:
其中,卡尔曼增益如下从确定更新状态矢量
状态估计误差的协方差如下面的等式:
Pk|k=(I-KkHk)Pk|k-1 (22)
在步骤314,系统可基于预测和更新的卡尔曼等式接收电池组和/或电池单元的增广状态矢量,并可计算电池模型的电压响应。增广状态矢量部分地由表示电荷转移阻抗的电阻项与内阻之间的比例的变量定义。在步骤316,如果电池管理系统被启用,例如,车辆仍处于接通状态下,则系统可继续使用更新的卡尔曼滤波器与引入的变量来实时预测和更新电池的电力容量。
在步骤318,如果系统检测到可禁用车辆计算系统的电力的切断(key-off)事件,则系统可结束用于管理电池组和/或一个或多个电池单元的一个或多个算法。车辆计算系统可具有车辆切断模式以允许系统将一个或多个参数存储在非易失性存储器中,使得这些参数可在下一接通事件时被系统使用。
图4是示出在车辆中测量的或通过电池测试测量的电池电流输入分布和电压输出分布的曲线图400。电池电流输入402曲线具有表示时间406的x轴和表示电流404的y轴。电池组的电流输入408基于包括系统的电荷消耗(CD)驾驶模式和电荷维持(CS)驾驶模式之间的转变的车辆驾驶模式418而波动。
电压输出分布通过具有表示时间414的x轴和表示电压412的y轴的端电压410曲线来描绘。端电压是电池组的电池内部电压416,并基于包括系统的电荷消耗(CD)驾驶模式和电荷维持(CS)驾驶模式之间的转变的车辆驾驶模式418而波动。
在车辆中测量的或通过电池测试测量的电池电流输入曲线和电压输出曲线展示了电池系统参数的波动。波动的参数可导致与电池性能、混合动力系统功能和/或由电池供电的其他系统相关的不精确的计算。
图5是示出基于识别的电池模型参数计算的电池电阻的比较的曲线图500。使用EKF测量电池电压和/或电流的先前方法在电池管理系统中执行;然而,预测的电池参数往往表现出一定程度的波动轨迹。例如,在混合电动车辆中,当车辆驾驶模式在电荷消耗模式和电荷维持模式之间转变508时,电池参数往往表现出波动轨迹。
使用EKF在电池管理系统中估计电池参数的先前方法往往对由Randles电路参数的弱观察性导致的内部和外部噪声敏感。使用先前EKF方法的Randles电路参数在各个参数之间具有弱的电化学关系。
例如,在图5中的曲线图500上示出了如通过车辆系统或电池测试较弱地可观察的对电池组和/或电池单元的内阻R0501的先前估计。对电池组和/或电池单元的内阻501的估计由具有表示时间506的x轴和表示欧姆504的y轴的曲线表示。该曲线展示了当车辆驾驶模式在电池电荷消耗模式和电池电荷维持模式之间转变508时测量的灵敏度。
在图5中的曲线图上示出了由所提出的算法使用EKF与电池组和/或电池单元的引入的变量所估计的R0502。所提出的对内阻502的估计示出了与先前方法相比系统可观察性和估计的提高。所提出的对内阻R0502的估计通过根据从CD到CS转变508的驾驶模式跟踪变化的时间常数,来有效地消除噪声。如以上解释的,通过将模型电阻参数关联在一起并将系统时间常数设置为用于系统识别的独立变量,参数可对噪声较不敏感。
图6是示出电池等效电路模型的R-C电路中的估计的电池模型参数的比较的曲线图。曲线图600表示电池管理系统的由R-C电路表示的电荷转移阻抗的电阻项。如曲线图中描绘的,将先前估计值与所提出的估计值比较,以示出使用调整的EKF方法的电池参数的可观察性的提高。
在具有表示时间608的x轴和表示欧姆606的y轴的曲线图中描绘由R-C电路表示的电荷转移阻抗的电阻项的先前估计。在电池管理系统中对电荷转移阻抗R1602的电阻项的先前估计基于对Randles电路的内部和外部噪声的灵敏度而波动。所提出的对电荷转移阻抗R1604的电阻项的估计通过将模型电阻参数关联在一起并将系统时间常数设置为用于系统识别的独立变量来消除噪声。
例如,在对电荷转移阻抗602的电阻项的先前估计下,当车辆驾驶模式在CD到CS转变618时,系统会具有波动轨迹。在所提出的基于调整的EKF方法对电荷转移阻抗604的电阻项的估计下,改进了估计并且显著地消除了估计对于系统噪声的敏感性。
通过C1表示的电荷转移阻抗的电容项确定在车辆运行期间变化的电池动态。在车辆运行期间对电池动态的先前估计610由x轴为时间616且y轴为法拉614的波动轨迹来示出。所提出的对电池动态的估计612被示出为电池系统的显著改进的测量计算。
用于电池管理系统的精确的电池参数估计方法通过具有较小的电池系统而提供电池组/电池单元的设计上的密度、可靠性、性能和/或经济性。精确的电池参数估计方法可包括EKF方法中的改进与操作条件相关的电池控制的调整的状态矢量,其中,所述操作条件包括(但不限于)充电的状态、电力衰减、容量衰减和瞬时可用电力。电池参数的估计方法可最终延长电池系统的使用寿命。
尽管以上描述了示例性实施例,但是这些实施例不意在描述本发明的所有可行形式。相反,在说明书中使用的词语是描述性词语而不是限制性词语,并且应理解,在不脱离本发明的精神和范围的情况下,可进行各种改变。另外,各种实现实施例的特征可被组合以形成本发明的进一步实施例。
Claims (5)
1.一种车辆,包括:
电池组;
至少一个控制器,被编程为:
实施具有以下参数的电池组的模型:(i)所述参数表示电池组的电荷转移阻抗和内阻,(ii)所述参数从具有增广状态矢量的扩展卡尔曼滤波器被识别,其中,所述增广状态矢量至少部分地由与电荷转移阻抗相关联的时间常数和表示电荷转移阻抗的电阻项与内阻的商的变量定义,以减小所述参数的观察到的可变性;
根据所述参数控制电池组。
2.如权利要求1所述的车辆,其中,电荷转移阻抗与电池组的至少一个R-C电路表示法相关联。
3.如权利要求1所述的车辆,其中,所述至少一个控制器还被编程为:将所述增广状态矢量应用于雅克比矩阵,其中,雅克比矩阵描述所述模型的电池参数值的变化。
4.如权利要求1所述的车辆,其中,所述增广状态矢量还包括表示电池动态响应的其他状态变量或表征电池动态的模型参数。
5.如权利要求1所述的车辆,其中,所述至少一个控制器还被编程为:基于所述参数在给定时间段内输出电力容量。
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US10566811B2 (en) | 2017-01-11 | 2020-02-18 | Samsung Electronics Co., Ltd. | Method and apparatus estimating and controlling battery state |
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US11069926B1 (en) * | 2019-02-14 | 2021-07-20 | Vcritonc Alpha, Inc. | Controlling ongoing battery system usage via parametric linear approximation |
US11498446B2 (en) * | 2020-01-06 | 2022-11-15 | Ford Global Technologies, Llc | Plug-in charge current management for battery model-based online learning |
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