CN109716151A - 用于估计电池电压的方法和设备 - Google Patents

用于估计电池电压的方法和设备 Download PDF

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CN109716151A
CN109716151A CN201680087154.6A CN201680087154A CN109716151A CN 109716151 A CN109716151 A CN 109716151A CN 201680087154 A CN201680087154 A CN 201680087154A CN 109716151 A CN109716151 A CN 109716151A
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本诺·施魏格尔
马蒂亚斯·弗莱肯施泰因
爱德温·克诺贝
穆罕默德·法拉格
赛义德·哈比比
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Abstract

在一种估计电池电压的方法中，提供给定的电池模型(BM)，其中，所述给定的电池模型(BM)是单粒子模型。确定电池的C速率。基于C速率对电池模型(BM)进行调整。借助于调整后的电池模型(BM)估计电池的电压。

Description

用于估计电池电压的方法和设备
本发明涉及用于估计电池电压的方法。本发明还涉及相应的设备。
在过去的几年中，汽车制造商已经经历了采用电动汽车的初步阶段。电动汽车(EV)采用后逐渐增加的趋势表明，电能存储系统将在电动汽车的发展中发挥重要作用。锂离子电池由于其轻质、高比能、低自放电率和无记忆效应，已成为电动汽车储能系统的最具吸引力的选择之一。为了充分地利用锂电子储能系统同时避免它的物理局限性，需要精确的电池管理系统(BMS)。在EV中，BMS负责性能管理，该性能管理包括但不限于充电状态(SOC)、健康状态(SOH)、功能状态(SOF)估计算法、功率管理和热管理等。BMS的关键问题中的一个是电池模型。为了模拟在恶劣环境中的电池动态行为，需要一种稳固、精确和高保真的电池模型。
本发明的目的是借助于精确的模型估计电池的电压。
该目的通过独立权利要求的特征来实现。本发明的有利实施例在从属权利要求中给出。
本发明的特征在于一种估计电池电压的方法。本发明的特征还在于用于估计电池电压的相应设备。在该方法中，提供了给定的电池模型，其中所述给定的电池模型是单粒子模型。确定电池的C速率。基于C速率对电池模型进行调整。借助于调整后的电池模型估计电池电压。
例如，估计的电池电压能够用于估计电池的充电状态。
C速率与电池容量、充电或放电电流有关。例如，1C的C速率指当电池容量为2000mAh时充电或放电电流为2000mAh或当电池容量为700mAh时充电或放电电流为700mAh，并且2C的C速率指当电池容量为2000mAh时充电或放电电流为4000mA或当电池容量为700mAh时充电或放电电流为1400mA。
精确的电池充电状态(SoC)估计算法由于其在电力输送和能量存储系统中的应用而具有极其重要的意义。为了确保安全性、耐用性和性能，这些先进的输送和能源基础设施中的电池管理系统必须精确了解内部电池的能量水平。这样的了解能使它们能够高效地再利用能量同时满足功率需求和设备级操作约束。
由于几个技术原因，监测电池的SOC和健康状态(SoH)特别具有挑战性。首先，在专业实验室环境之外直接测量电池成分的Li浓度或电池成分的物理检查是不切实际的。其次，动力学由根据电化学原理导出的偏微分代数方程控制。仅有的可测量量(电压和电流)通过边界值与状态相关。最后，模型的参数随电极化学、电解质、封装和时间而变化很大。
在电池SOC/SOH估计方面的研究经历了相当大的发展。这可按电池模型来划分，每个算法采用：第一类别，其考虑基于等效电路模型(ECM)的估计量。这些模型使用电路元件以模拟电池的现象行为。ECM的主要优点是它们的简单性。然而，它们常常需要大量的参数来进行精确的预测。这通常产生具有非物理参数的模型，其复杂性变得与电化学模型相当。第二类别，其考虑电化学模型，该模型考虑扩散、插层和电化学动力。虽然这些模型能够精确地预测内部状态变量，但是它们的数学结构对于控制器/观测器设计来说通常过于复杂。因此，这些方法结合了模型简化和估计技术。
这一类别中的一个可能的模型是使用结合扩展卡尔曼滤波器的电化学电池动力学的“单粒子模型”(SPM)。另一种方法是使用残留分组进行模型简化和对观测器使用线性卡尔曼滤波器。此外，简化可应用于电解质和固相浓度动力学以执行SOC估计。
然而，由于已经在该模型上实现了简化，所有简化的电化学模型都在低C速率下进行参数化和测试。
SPM的核心思想是，每个电极的固相可以理想化为单个球形粒子。如果假设电解质锂浓度在空间和时间上是恒定的，则该模型的结果是精确的。这种假设对于小电流或具有大电子电导率的电解质很有效。然而，它在大电流充放电速率(C速率)下会引起误差，导致在较高C速率下的SOC估计较差。
对于较高的C速率，复杂模型具有更好的精度，但是不能够在实时应用中实现。
上述用于估计电池电压的方法直接解决了前面所述的技术挑战。借助于C速率适应模型，该模型能够减少高C速率工作范围内的误差，并且从而改善充电状态估计精确性。
根据一个实施例，将电池的C速率与第一阈值进行比较。如果C速率高于第一阈值，则调整电池模型，使得实现复杂模型，如果C速率低于第一阈值，则调整电池模型，使得实现简化模型。
由于复杂模型对于较高的C速率也具有更好的精确性，但是不能够在实时应用中实现，而简化模型能够用于实时应用中，并且对于低C速率也是精确的，所以将C速率与阈值进行比较以选择能够使用复杂模型还是简单模型是有利的。
根据另一实施例，将电池的C速率与第一阈值和比第一阈值小的第二阈值进行比较。如果C速率高于第一阈值，则调整电池模型使得实现复杂模型。如果C速率低于第一阈值且高于第二阈值，则调整电池模型使得实现简化模型。如果C速率低于第一阈值且高于第二阈值，则调整电池模型使得实现比简化模型更简单的模型。
借助于两个阈值，可以对电池模型进行更详细的修改。
根据另一实施例，第二阈值的值为2C至4C，例如是2C、3C或4C。
特别是对于低于2C到4C的C速率，一个非常简单的电池模型对于电压的估计是足够精确的。因此，选择2C到4C的第二阈值的值是有利的。
由于该方法能够用于充电和放电，所以第二阈值也能够为-2C到-4C，例如是-2C、-3C或-4C。
根据另一实施例，第一阈值的值为8C至12C，例如是8C、9C、10C、11C或12C。
特别是对于高于8C到12C的C速率，需要一个非常复杂的电池模型来估计电压。因此，选择8C到12C的第一阈值的值是有利的。
由于该方法能够用于充电和放电，所以第二阈值也能够为-8C到-12C，例如是-8C、-9C、-10C、-11C或-12C。
根据另一实施例，基于C速率对电池模型的给定模型参数进行调整。
根据另一实施例，基于C速率对电池模型的给定方程组进行调整。
以下借助于附图对本发明的示例性实施例进行说明。
这些附图如下：
图1是单粒子模型的示意图；
图2是用于估计电池电压的程序的流程图；和
图3是电池模型的调整的实施例。
图1是单粒子模型(SPM)的示意图。
SPM于2005年通过“Review of models for predicting the cyclingperformance of lithium ion batteries”首次应用于锂电池系统。图1是提出了SPM概念的示意图。在数学上，该模型由控制每个电极浓度动力学的两个扩散偏微分方程(PDE)组成，其中将输入电流作为诺依曼边界条件输入。输出电压由输入电流和边界处的状态值的非线性函数给出。虽然该模型比其他基于电化学的估计模型捕获的动态行为更少，但是其数学结构适于实时实现。
该SPM对于具有大电子电导率的电解质或小电流很有效。然而，它在大电流充电和放电速率(C速率)下会引起误差，导致对较高C速率下的SOC估计较差。
复杂模型对于较高的C速率也具有更好的精确性，但是不能够在实时应用中实现。
图2示出了用于估计电池电压的程序的流程图。该程序能够由设备1执行。
在步骤S1中，启动程序，例如，初始化变量。
在步骤S3中，提供给定的电池模型(BM)，其中给定电池模型(BM)是单粒子模型，例如，如上所述的SPM。
在步骤S5中，确定电池的C速率，其中C速率与电池容量、充电或放电电流有关。
在步骤S7中，基于C速率对电池模型BM进行调整。
为了调整电池模型BM，例如，将电池的C速率与第一阈值进行比较，如果C速率高于第一阈值，则调整电池模型BM使得实现复杂模型，如果C速率低于第一阈值，则调整电池模型BM使得实现简化模型。
调整电池模型BM的另一种方式是，例如，将电池的C速率与第一阈值和比第一阈值小的第二阈值进行比较。如果C速率高于第一阈值，则调整电池模型BM使得实现复杂模型。如果C速率低于第一阈值且高于第二阈值，则调整电池模型BM使得实现简化模型。如果C速率低于第一阈值且高于第二阈值，则调整电池模型BM使得实现比简化模型更简单的模型。
例如，可以如图3所示选择第一阈值和第二阈值。第一阈值的值例如为8C到12C，例如是8C、9C、10C、11C或12C，和/或，由于该方法能够用于充电和放电，所以第一阈值也能够为-8C到-12C，例如是-8C、-9C、-10C、-11C或-12C。第二阈值的值例如为2C到4C，例如是2C、3C或4C，和/或，由于该方法能够用于充电和放电，所以第二阈值也能够为-2C到-4C，例如是-2C、-3C或-4C。
为了调整电池模型BM，例如，基于C速率对电池模型BM的给定模型参数进行调整，或者基于C速率对电池模型BM的给定方程组进行调整。
在步骤S9中，借助于调整后的电池模型BM估计电池的电压。
在步骤S11中，程序结束并能够在步骤S1中再次启动。
图3示出了调整的示例。基于第二阈值和第一阈值以及C速率，电池被划分为三个操作范围，即，高C速率操作范围HCR(C速率高于第一阈值)、中C速率操作范围MCR(C速率高于第二阈值且低于第一阈值)以及低C速率操作范围LCR(C速率低于第二阈值)。基于操作范围选择参数集，即，用于高C速率操作范围HCR的高C速率参数集HPS、用于中C速率操作范围MCR的中C速率参数集MPS以及用于低C速率操作范围LCR的低C速率参数集LPS。
基于选择的参数集对电池模型(BM)进行调整。因此，模型参数和方程组随着C速率而变化，以更好地调整电池在实时应用中的高动态操作。在需要复杂模型的高C速率下，会启用参数集以允许利用例如整个方程组来运行模型，而在简单模型就足够的低C速率下，会启用另一组参数集。
因此，借助于上述方法，该模型能够减少在高C速率操作范围下的误差，从而提高充电状态估计的精确性。

Claims (8)

1.一种用于估计电池电压的方法，其中，
-提供给定的电池模型(BM)，其中，所述给定的电池模型(BM)是单粒子模型，
-确定电池的C速率，
-基于C速率对电池模型(BM)进行调整，
-借助于调整后的电池模型(BM)估计电池的电压。
2.根据权利要求1所述的方法，其中，将电池的C速率与第一阈值进行比较，如果C速率高于第一阈值，则调整电池模型(BM)使得实现复杂模型，如果C速率低于第一阈值，则调整电池模型(BM)使得实现简化模型。
3.根据权利要求1所述的方法，其中，将电池的C速率与第一阈值和比第一阈值小的第二阈值进行比较，并且
-如果C速率高于第一阈值，则调整电池模型(BM)使得实现复杂模型，
-如果C速率低于第一阈值且高于第二阈值，则调整电池模型(BM)使得实现简化模型，
-如果C速率低于第一阈值且高于第二阈值，则调整电池模型(BM)使得实现比所述简化模型更简单的模型。
4.根据权利要求3所述的方法，其中，所述第二阈值的值为2C至4C。
5.根据权利要求2、3或4所述的方法，其中，所述第一阈值的值为8C至12C。
6.根据权利要求1至5中任一项所述的方法，其中，基于C速率对电池模型(BM)的给定模型参数进行调整。
7.根据权利要求1至6中任一项所述的方法，其中，基于C速率对电池模型(BM)的给定方程组进行调整。
8.一种用于估计电池电压的设备(1)，其中，所述设备(1)被设计用于执行根据权利要求1至7中任一项所述的方法。
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