CN115675175A - 电池内部短路检测和缓解 - Google Patents

电池内部短路检测和缓解 Download PDF

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CN115675175A
CN115675175A CN202210821179.1A CN202210821179A CN115675175A CN 115675175 A CN115675175 A CN 115675175A CN 202210821179 A CN202210821179 A CN 202210821179A CN 115675175 A CN115675175 A CN 115675175A
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traction battery
expected
hour
difference
controller
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朱迪
本杰明·A·塔巴托夫斯基-布什
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    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
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Abstract

本公开提供“电池内部短路检测和缓解”。控制器基于牵引电池的电池单元每单位安培小时实际增加所获取的实际充电速率和每单位安培小时预期增加所获取的预期充电速率来选择性地阻止电力从所述牵引电池流到电机。

Description

电池内部短路检测和缓解
技术领域
本公开涉及对汽车电池和电动化动力传动系统的控制。
背景技术
替代能源车辆可以包括被布置为向电机提供电力的牵引电池。电机可以将来自牵引电池的电能变换成机械能以使车辆的车轮移动。电机还可以将来自车轮的机械能变换成电能以存储在牵引电池中。
发明内容
一种车辆包括电机、牵引电池装置和控制器。所述控制器响应于差值大于阈值而阻止所述牵引电池装置为所述电机供电。所述差值介于所述牵引电池装置的电池单元在预定时间内每实际安培小时变化的实际电压变化与在所述预定时间内每预期安培小时变化的预期电压变化之间。
一种方法包括在牵引电池装置的电池单元在预定时间内每实际安培小时变化的实际电压变化与所述预定时间内每预期安培小时变化的预期电压变化之间的差值超过阈值之后，阻止电气地介于所述牵引电池装置与电机之间的至少一个接触器闭合以阻止电力从所述牵引电池装置流到所述电机。
一种动力传动系统包括电机和控制器。所述控制器基于牵引电池的电池单元每单位安培小时实际增加所获取的实际充电速率和每单位安培小时预期增加所获取的预期充电速率来选择性地阻止电力从所述牵引电池流到所述电机。
附图说明
图1是具有内部短路电阻的电池单元等效电路的示意图。
图2是具有和不具有内部短路电阻的电池单元的电池单元电压对时间的曲线图。
图3是没有内部短路电阻的电池单元的Et_pred和Et_calc的曲线图。
图4是具有内部短路电阻的电池单元的Et_pred和Et_calc的曲线图。
图5是电池系统的框图。
图6是用于控制电动化动力传动系统的算法的流程图。
图7是汽车的框图。
具体实施方式
本文描述了实施例。然而，应理解，所公开的实施例仅是示例并且其他实施例可以采用不同和替代的形式。附图不一定按比例绘制。一些特征可能会被放大或最小化以示出特定部件的细节。因此，本文所公开的具体结构细节和功能细节不应被解释为是限制性的，而是仅仅作为教导本领域技术人员的代表性基础。
任何一个附图示出并参考任何一个附图描述的各个特征可以结合一个或多个其他附图中示出的特征以产生未明确示出或描述的实施例。所示特征的组合提供用于典型应用的代表性实施例。然而，对于特定的应用或实现方式，可能期望与本公开的教导一致的对特征的各种组合和修改。
一个感兴趣的研究领域涉及检测电池内部短路电阻，这可能有许多原因。原因之一是铜溶解。溶解的铜生长穿过隔板并最终在阳极和阴极之间形成短路。图1示出了具有电池内阻14、内部短路电阻16和电池开路电压18的电池单元12的等效电路10。总电流Itotal是电池电流Ibatt和内部短路电流Iisc之和。当电池12被充电时，可能发生这种情况。能量被推入具有内部短路电阻16的电池单元中同时内部短路电阻16将电能转换成热量。如果未及早检测到，则具有内部短路电阻的电池单元可能会遇到热问题。
内部短路电阻的检测可能是困难的，因为具有内部短路电阻的电池单元没有显示出大的电压偏差，如图2所示。实线曲线表示健康电池单元的电压曲线。虚线曲线表示具有内部短路电阻的电池单元的电压曲线。两个电池单元在相同条件下充电。然而，具有内部短路电阻的电池单元的电压比健康电池单元的电压增加得更慢。这是由于一些能量被内部短路电阻转换成热量。
基于电压的检测被广泛用于各种故障检测。在一个示例中，用于检测电池单元电压偏差的电压阈值是0.715V，这对于识别内部短路电阻可能是无效的。电压阈值不能调谐到较小的数字(例如，0.25V或更小)，因为这将导致误报结果。另外，即使现有方法检测到电压偏差，也可能无法采取有效措施来防止发生热问题。相反，它可以触发指示电压偏差状况的消息。对于其他故障，这种作用水平就足够了。然而，生成消息可能不足以防止充电期间的热问题。
发明人已经认识到，上述问题可以通过使用模型预测控制来检测内部短路电阻的存在的设备和方法来解决。所提出的技术利用特性参数(即，时间t处的累积的电压变化除以累积的安培小时增加的速率)。下面列出了用于计算速率Et_calc的等式：
Et_calc＝(Vt–V0)/(Ct–C0) (1)
其中Vt、V0、Ct和C0分别表示在时间t和0处的电池单元电压和累积的安培小时增加。该速率指示对于安培小时值的给定变化，电池单元电压变化了多少。当具有内部短路电阻的电池单元被充电时，其Et_calc具有与健康电池单元不同的曲线。
关于模型预测控制，构建深度神经网络(例如，长短期记忆、自动编码器等)以预测健康电池单元的速率Et_pred。Et_pred表示在时间t处累积的电压变化除以累积的安培小时增加的预测速率。由于使用来自不具有内部短路电阻的电池单元的数据来训练神经网络，因此预测速率Et_pred与使用(1)的计算速率Et_calc之间的差相对较小，如图3所示。
然而，当来自具有内部短路电阻的电池单元的数据被馈送到神经网络中时，Et_pred与Et_calc之间的差变得显著，如图4所示。这是因为仅使用来自健康电池单元的数据来训练神经网络。如果Et_pred的曲线与Et_cal的曲线不匹配，则检测到内部短路状况的存在。
为了确保该方法的稳健性，还可以使用阈值Te和计数器阈值Tc。阈值Te和Tc可以分别设置为例如0.1V/Ah和3。当Et_pred和Et_calc之间的差大于0.1时，计数器递增1。一旦计数器达到3，就声明内部短路电阻状况。
参考图5，电池组20包括N个电池单元22、M个电池单元测量和控制板24、电池组控制器26、电压测量装置28和总线电气中心30。总线电气中心30包括主正极接触器32和主负极接触器34、与主正极接触器32并联的串联连接的预充电电阻器36和预充电接触器38、以及电流测量装置40。电流测量装置40(例如，电流传感器)向电池组控制器26提供电流数据以在充电事件期间进行安培小时计数。电池单元测量和控制板24(例如，电压传感器等)向电池组控制器26提供电池单元电压，因此可以实时计算在时间t处累积的电压变化除以累积的安培小时增加的速率。另外，将测量的电流、电池单元电压和累积的安培小时值是馈送到预构建神经网络中的输入。基于输入，经训练的神经网络实时地预测速率Et_pred。
参考图6，所述过程以在操作42处将计数器设置为0开始。之后，在操作44处，预训练的神经网络预测时间t处的速率Et_pred。一旦Et_pred变得可用，就在操作46处将Et_pred与计算出的速率Et_calc进行比较。Et_calc由电池组控制器26基于安培小时计数结果和测量的电池单元电压来计算。如果在操作48处，Et_pred与Et_calc之间的差大于阈值Te，则在操作50处计数器递增1。否则，电池组控制器26开始针对下一个时间步长的预测，然后返回到操作44。如果在操作52处计数器小于或等于阈值Tc，则在操作54处发送出指示初始检测结果的警告消息，然后返回到操作44。一旦计数器超过阈值Tc(例如，3)，就确认内部短路电阻，并且电池组控制器26在操作56处声明内部短路状况。之后，在操作58处，防止电池组20支持推进。
参考图7，汽车60包括牵引电池装置62(包括电池组20)、电机64、车轮66和一个或多个控制器68(在一些布置中包括电池组控制器26)。一个或多个控制器68与牵引电池装置62和电机64通信并对它们施加控制。电机64被布置成将来自牵引电池装置62的电力转换为机械动力以推进车轮66。电机64还被布置成在例如再生制动事件期间将来自车轮66的机械动力转换为电力以存储在牵引电池装置62中。响应于操作56(图6)中描述的状况的存在，一个或多个控制器66可以命令牵引电池装置的主正极接触器和主负极接触器中的至少一者断开以阻止牵引电池装置62通过电机62支撑车轮66的推进，如在操作58(图6)中所提及。
本文公开的算法、方法或过程可以被输送到计算机、控制器或处理装置或由所述计算机、控制器或处理装置实现，所述计算机、控制器或处理装置可以包括任何专用电子控制单元或可编程电子控制单元。类似地，算法、方法或过程可以以多种形式存储为可由计算机或控制器执行的数据和指令，包括但不限于永久存储在诸如只读存储器装置的不可写存储介质上的信息和可改地存储在诸如光盘、随机存取存储器装置或其他磁性和光学介质的可写存储介质上的信息。算法、方法或过程也可以软件可执行对象实现。替代地，可以使用合适的硬件部件(诸如专用集成电路、现场可编程门阵列、状态机或其他硬件部件或装置)或固件、硬件和软件部件的组合来整体或部分实现所述算法、方法或过程。
尽管上文描述了示例性实施例，但这些实施例并不意图描述权利要求所涵盖的所有可能形式。例如，参考图6，不需要使用计数器来实施所描述的策略。其他场景也是可能的。在说明书中使用的词语是描述性词语而非限制性词语，并且应理解，在不脱离本公开的精神和范围的情况下可以做出各种改变。
如前所述，各种实施例的特征可以组合以形成可能未明确描述或示出的本发明的另外实施例。虽然各种实施例可能已经被描述为就一个或多个期望的特性而言相较其他实施例或现有技术实现方式提供了优点或是优选的，但是本领域普通技术人员应认识到，一个或多个特征或特性可被折衷以实现期望的总体系统属性，这取决于具体的应用和实现方式。这些属性可包括但不限于：成本、强度、耐久性、生命周期成本、可销售性、外观、包装、大小、可服务性、重量、可制造性、易组装性等。这样，描述为就一个或多个特性而言相较其他实施例或现有技术实现方式不太可取的实施例不在本公开的范围外，并且对于特定应用可能是期望的。
根据本发明，提供了一种车辆，其具有：电机；牵引电池装置；和控制器，所述控制器被编程为响应于差值大于阈值而阻止所述牵引电池装置为所述电机供电，其中所述差值介于(i)所述牵引电池装置的电池单元在预定时间内每实际安培小时变化的实际电压变化和(ii)所述预定时间内每预期安培小时变化的预期电压变化之间。
根据实施例，所述控制器还被编程为实施深度神经网络，所述深度神经网络生成在所述预定时间内每预期安培小时变化的所述预期电压变化。
根据实施例，所述深度神经网络是长短期记忆。
根据实施例，阻止所述牵引电池装置为所述电机供电包括断开所述牵引电池装置的至少一个接触器。
根据实施例，所述控制器还被编程为响应于所述差值大于所述阈值而使计数器递增。
根据实施例，所述控制器还被编程为响应于所述差值大于所述阈值而生成指示所述差值的存在的消息。
根据本发明，一种方法包括：在牵引电池装置的电池单元在预定时间内每实际安培小时变化的实际电压变化与所述预定时间内每预期安培小时变化的预期电压变化之间的差值超过阈值之后，阻止电气地介于所述牵引电池装置与电机之间的至少一个接触器闭合以阻止电力从所述牵引电池装置流到所述电机。
在本发明的一个方面，所述方法包括经由深度神经网络生成在所述预定时间内每预期安培小时变化的所述预期电压变化。
在本发明的一个方面，所述深度神经网络是长短期记忆。
在本发明的一个方面，所述方法包括在所述差值超过所述阈值之后使计数器递增。
在本发明的一个方面，所述方法包括在所述差值超过所述阈值之后生成指示所述差值的存在的消息。
根据本发明，提供了一种动力传动系统，其具有：电机；和控制器，所述控制器被编程为基于牵引电池的电池单元每单位安培小时实际增加所获取的实际充电速率和每单位安培小时预期增加所获取的预期充电速率来选择性地阻止电力从所述牵引电池流到所述电机。
根据实施例，所述控制器还被编程为实施深度神经网络，所述深度神经网络生成每单位安培小时预期增加所获取的所述预期充电速率。
根据实施例，所述深度神经网络是长短期记忆。
根据实施例，选择性地阻止电力从所述牵引电池流到所述电机包括选择性地断开所述牵引电池装置的至少一个接触器。
根据实施例，由所述牵引电池的电池单元每单位安培小时实际增加所获取的所述实际充电速率被定义为所述电池单元的电压变化与所述电池单元的安培小时变化的商。

Claims (15)

1.一种车辆，其包括：
电机；
牵引电池装置；和
控制器，所述控制器被编程为响应于差值大于阈值而阻止所述牵引电池装置为所述电机供电，其中所述差值介于(i)所述牵引电池装置的电池单元在预定时间内每实际安培小时变化的实际电压变化和(ii)所述预定时间内每预期安培小时变化的预期电压变化之间。
2.如权利要求1所述的车辆，其中所述控制器还被编程为实施深度神经网络，所述深度神经网络生成在所述预定时间内每预期安培小时变化的所述预期电压变化。
3.如权利要求2所述的车辆，其中所述深度神经网络是长短期记忆。
4.如权利要求1所述的车辆，其中阻止所述牵引电池装置为所述电机供电包括断开所述牵引电池装置的至少一个接触器。
5.如权利要求1所述的车辆，其中所述控制器还被编程为响应于所述差值大于所述阈值而使计数器递增。
6.如权利要求1所述的车辆，其中所述控制器还被编程为响应于所述差值大于所述阈值而生成指示所述差值的存在的消息。
7.一种方法，其包括：
在牵引电池装置的电池单元在预定时间内每实际安培小时变化的实际电压变化与所述预定时间内每预期安培小时变化的预期电压变化之间的差值超过阈值之后，阻止电气地介于所述牵引电池装置与电机之间的至少一个接触器闭合以阻止电力从所述牵引电池装置流到所述电机。
8.如权利要求7所述的方法，其还包括经由深度神经网络生成在所述预定时间内每预期安培小时变化的所述预期电压变化。
9.如权利要求8所述的方法，其中所述深度神经网络是长短期记忆。
10.如权利要求7所述的方法，其还包括在所述差值超过所述阈值之后使计数器递增。
11.如权利要求7所述的方法，其还包括在所述差值超过所述阈值之后生成指示所述差值的存在的消息。
12.一种动力传动系统，其包括：
电机；以及
控制器，所述控制器被编程为基于牵引电池的电池单元每单位安培小时实际增加所获取的实际充电速率和每单位安培小时预期增加所获取的预期充电速率来选择性地阻止电力从所述牵引电池流到所述电机。
13.如权利要求12所述的动力传动系统，其中所述控制器还被编程为实施深度神经网络，所述深度神经网络生成每单位安培小时预期增加所获取的所述预期充电速率。
14.如权利要求13所述的动力传动系统，其中所述深度神经网络是长短期记忆。
15.如权利要求12所述的动力传动系统，其中选择性地阻止电力从所述牵引电池流到所述电机包括选择性地断开牵引电池装置的至少一个接触器。
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