CN110450786B - 用于确定电动车辆中的预测的加速信息的方法和电动车辆 - Google Patents

用于确定电动车辆中的预测的加速信息的方法和电动车辆 Download PDF

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CN110450786B
CN110450786B CN201910369894.4A CN201910369894A CN110450786B CN 110450786 B CN110450786 B CN 110450786B CN 201910369894 A CN201910369894 A CN 201910369894A CN 110450786 B CN110450786 B CN 110450786B
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acceleration
information
vehicle
predicted
electric vehicle
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CN110450786A (zh
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C·朔恩胡贝尔
J·克诺费尔
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Audi AG
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Audi AG
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    • 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
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Abstract

本发明涉及一种用于在电动车辆(1)中确定预测的加速信息(9)的方法，该加速信息描述具有作为驱动装置的电机(2)的电动车辆(1)的未来的加速潜力，由电池(5)为电机(2)馈电，该方法包括如下步骤:‑提供电机(2)的功率预测信息(11)，该功率预测信息描述在至少一个未来的时段内电机(2)的预计可用的加速功率，‑在使用车辆模型(13)的情况下和/或在使用尤其由导航***(7)针对该时段提供的预测的道路数据(14)的情况下，从功率预测信息(11)中确定加速信息(9)，所述车辆模型描述电动车辆(1)的当前运行状态并提供对能基于加速功率实现的加速度进行描述的至少一个车辆参数。

Description

用于确定电动车辆中的预测的加速信息的方法和电动车辆
技术领域
本发明涉及一种用于在电动车辆中确定预测的加速信息的方法，所述加速信息描述电动车辆的未来的加速潜力，该电动车辆具有由电池馈电的、作为驱动装置的电机，本发明还涉及一种电动车辆。
背景技术
仅具有至少一个由作为电蓄能器的电池馈电的电机作为驱动装置的电动车辆由于较小的有害物质排放量和其他优点而成为一个重要的研究领域电机。为这种电动车辆提供在行驶时辅助于驾驶员的各种驾驶员辅助***。这类驾驶员辅助***的例子包括纵向控制***/纵向引导***，尤其是ACC***(Adaptive Cruise Control(自适应巡航控制))和速度调节***。对于这类驾驶员辅助***，当然也对于其他车辆***而言，与未来可在机动车中提供何种加速潜力(Beschleunigungspotential)有关的信息是有用的，从而能够产生相应的预测的加速信息且将其传输给相应的车辆***，尤其是驾驶员辅助***。基于这种加速信息，可以调整车辆***的运行参数，尤其是可以调整未来的运行策略和/或可设定的速度，尤其是最大速度。因此例如在纵向控制***中可想到的是，根据电动车辆的现在的加速潜力来选择相对于前方行驶的交通参与者的运行模式和/或转换到新的最大速度。
在此的问题是，在电动车辆中即使除了故障状态之外也存在多种影响因素，这些影响因素使得当前可供使用的加速潜力与可相当于电动车辆的最大有效功率的理论加速潜力有偏差理论。如果这时仅以加速信息中的理论值为出发点，则这会在车辆***中、尤其是驾驶员辅助***中导致该驾驶员辅助***的不可预见的且过强控制的驾驶行为。例如所设定的设置速度、例如期望速度和/或新的最大允许的最高速度可能会被驾驶员辅助***以不同速度进行调节，或者根本无法达到。使得更好的前瞻性规划显得有利的另一方面是：通过前瞻性的行驶方式/运行策略能够节省能量，从而尤其是在电动车辆中也能够增加续航里程。例如在文献DE 10 2011 116 184 A1中说明了能够通过纵向控制***实现的行驶策略的这种确定，其中为了实现该行驶策略而在此使用不同信息，但这些信息不包括预测的加速潜力。
文献DE 10 2011 116 773 A1涉及在车辆在半自动行驶模式中运行期间纵向调节车辆的方法和设备，其中执行对距离进行限制的制动干预以用于保持与前方行驶的车辆的可规定的最小间距。其中的方法特别适合应用在电动车辆中，这是因为例如在响应于前方车辆的制动期间电机能够转换至发电机运行模式。
发明内容
本发明基于的任务是，提供一种用于特定于情况地确定电动车辆的未来的加速潜力、尤其是用于在驾驶员辅助***中作为加速信息使用的可行方案。
为了解决该任务，开头所述类型的方法根据本发明包括如下步骤：
-提供电机的功率预测信息，该功率预测信息描述在至少一个未来的时段中电机预计的可用加速功率，
-在使用车辆模型的情况下和/或在使用尤其由导航***针对该时段提供的预测的道路数据的情况下，从功率预测信息中确定加速信息，该车辆模型描述机动车的当前运行状态并提供对基于加速功率可实现的加速度进行描述的至少一个车辆参数。
根据本发明认识到，存在大量影响因素，这些影响因素描述了在未来的、尤其是马上面临的时段内、例如在未来的确定的数秒内的加速潜力，但在当今机动车中能够存在或确定关于这些影响因素的信息。例如可供使用的驱动功率(以及进而可供使用的加速功率)可能在不存在故障状态的情况下受到能量***的各种条件或环境条件的限制。这些条件的例子包括外部温度、电池的荷电状态、功率电子件的状态等。其他影响因素涉及如下事实：由于机动车本身的特性变化，例如由于装载而改变的质量，以及由于机动车所行驶的道路路段的特性变化，例如由于坡道、弯路和/或改变的摩擦特性，可能出现与理论值、尤其是各种限制的偏差。根据本发明在此提出，汇总这些信息——电机的功率预测、车辆模型和预测的道路数据，以便能够准确地、特定于情况地计算电动车辆的未来的加速潜力。
特别有利地，使用加速信息来控制机动车的至少一个驾驶员辅助***，尤其是纵向控制***，尤其是来确定驾驶员辅助***的预测的运行策略。加速信息允许更加可靠的预测，进而能及时地调整运行策略或一般而言的运行参数，以便实现驾驶员辅助***的尽可能理想的、以舒适性为指导的且满足驾驶员需求的运行方式。针对以ACC***为纵向控制***的情况，可以例如参照前方行驶的交通参与者进一步优化机动车的减速度或加速度，而同时无论是对于ACC***还是对于速度调节***而言都适合的是，检查：究竟是否能够达到所设定的调节速度，究竟能够将例如在驾驶员方面设定的设置速度调整到哪个数值。也就是说，尤其是可以提前阻止不能够达到的设置速度，以避免纵向控制***的不被理解的行为。此外为了通过驾驶员辅助***、尤其是纵向控制***来实现尽可能高能效的未来行驶方式，可以评估关于可用的未来加速潜力的信息。
根据本发明在此利用到了：在现有技术中，已经提出了或能够容易地实现/实施作为确定预测的加速信息的基础的部分功能。也就是说，提出了在机动车内对信息的新式的组合，以便确定与情况相适配的、当前的预测的加速信息且将其提供给相应的车辆***，尤其是驾驶员辅助***。在此尤其是以合适的模型为基础，以便了解在电机中所能提供的加速功率在可能的速度变化上的对应关系，在此可以考虑所基于的、专业人员原则上已知的物理的总关联性，尤其是为了在所行驶的道路路段上实现运动还考虑电机在路径上的效率或损耗。
在此考虑电机的功率预测信息作为确定加速信息的基础，其中相应的用于预测电机功率潜力的操作方式已经在现有技术中得到公开且也在本发明中能够在必要时以优化的方式被使用。
因此可以提出，为了确定功率预测信息，使用对电池的当前状态和/或对配设给电池和/或电机的功率电子件的当前状态进行描述的至少一个能量信息，和/或对电机和/或电池和/或功率电子件的运行条件进行描述的至少一个环境信息。尤其能够限制电机功率的主要因素在此是：电池的特性和在电机或配设给电机的能量***(电池和功率电子件)的范围内的温度情况，这是因为此处的温度会强烈影响实际可供使用的驱动功率，进而也会强烈影响加速功率。因此本发明的合适的改进方案提出，所述能量信息包括电池的荷电状态(SOC–State of Charge)和/或电池的老化状态，和/或所述环境信息包括至少一个温度、尤其是外部温度和/或电池温度，和/或使用通过能量信息和/或环境信息参数化的效率模型，该效率模型描述了存储在电池中的电能转化为可在电机处获得的驱动功率且进而可获得的加速功率。在此该效率模型可以非常有利地包括温度模型，该温度模型描述对能量***和电机的温度影响，尤其是还主要能够通过外部温度参数化，据此根据实际进行的运行情况能够建立在能量***和电机本身中进行的生热过程相对于时间的模型。
结果得到了高质量的功率预测信息，该功率预测信息例如通常也能够描述对于一个时段可供使用的驱动功率，和/或说明：目前在多长时间间隔内，在例如x秒内，能够保持最大驱动功率。
车辆模型描述机动车的与加速可能性有关的状态。在此可以特别有利地提出，使用提供至少一个动态的车辆参数和至少一个静态的车辆参数的车辆模型。如已经从所基于的物理关系中得出的，在加速功率向电动车辆本身的速度变化转化时，电动车辆的速度和质量是主要的、原则上可变化的影响参数(例如由于装载改变车辆质量)。因此可以优选使用机动车的当前的车辆质量和/或当前的质量分布和/或机动车的当前的和/或预测的速度作为车辆参数。车辆模型可以尤其还包括效率，用于实现电机的驱动功率/加速功率向车轮功率的换算。
优选可以使用对前方道路的弯道信息和/或坡度信息进行描述的预测的道路数据，其中确定潜在的、提高机动车纵向速度的加速度纵向分量，和/或尤其是基于预测的道路数据的道路状况信息和/或摩擦系数信息考虑车轮打滑情况。例如机动车必须选择坡道时，使用一部分的加速功率用于克服重力加速度，从而仅实际提供在车轮上的另一部分机动车加速度才能够起到提高速度的作用。此外在通过弯道信息描述的弯道时，对抗离心力的横向加速度分量不用于提高速度。弯道信息和坡度信息可以特别有利地由导航***的数字地图数据调取。也可以从该处获取其他的预测的道路数据，其中，如现有技术中原则上公开的那样，可以考虑传感器，例如指向机动车前方区域的摄像头等作为预测的道路数据的数据源。
另一可考虑的影响因素可以是车轮打滑或摩擦损耗，也就是说，在车轮转矩相对于所行驶的地面的转化效果。在此现有技术中也公开了相应能够在本发明中使用的模型。这些模型可以例如包括道路状况信息和/或摩擦系数信息，例如所行驶的路面的类型和/或天气条件。
在本发明的特别有利的改进方案中可以提出，预测的道路数据包括机动车的预测的速度变化，和/或从预测的道路数据中确定的机动车的预测的速度变化，其中电动车辆的在所述时段的一个时刻的预测的速度被考虑用于确定该时刻的加速潜力。对预测的速度变化的确定在此可以尤其是还考虑驾驶员辅助***、尤其是纵向控制***的运行数据等，所述驾驶员辅助***例如已经在运行策略的范围内预先规划了机动车的速度变化。就机动车未来的速度变化给出启示的其他数据源是预测的道路数据本身，该预测的道路数据例如还可以包括关于许可的最大速度或其他影响速度的边界条件、例如急转弯的信息。在确定预测的速度变化时尤其是还可以考虑历史数据，该历史数据再现了驾驶员过去实践了的典型的速度变化。这类数据允许例如以如下方式提供例如预测的加速信息：在最大速度改变的前方位置处能够检查，预计能否加速至适合作为新的设置速度的新的最大速度。
在此仍要再次说明的是，对加速潜力进行描述的加速信息可以必要时考虑可能的加速过程本身，尤其是以如下方式：例如对于所覆盖的时段的不同时刻分别检查最大能够加速至哪个速度以及类似于此。为此可以例如额外对该时段规定一前瞻时段，在该前瞻时段中评估最大的加速度。
原则上合适的是，将对在该时段内最大可能的加速度进行描述的加速度变化确定为加速信息。最大可能的加速度可以通过加速度值本身进行描述，但在此还可行的是(如已经阐述的那样)，例如对于某一时刻确定通过加速度措施最大能够达到的最大速度，这仅限于在前瞻时段内能够实现，以便排除仅以渐进的方式能够达到的最大速度。对于该时段中的各时刻，加速度可以表明：确定的加速度值、尤其是在该时刻的最大可能的加速度值最长能保持多长时间。以此方式，最终能够精确地针对接下来的评估，尤其是在驾驶员辅助***中的评估提供加速信息。此外还适宜的是，为加速信息分配有至少一个与理论加速潜力的偏差的原因信息。在考虑计算加速信息时的不同效果时，可以因此监测：哪个效果对于限制理论加速潜力有最大影响，从而相应的信息可以例如是陡峭的坡道、电机和/或能量***的过高温度等。
除了所述方法，本发明还涉及一种电动车辆，该电动车辆具有由电池馈电的、作为驱动装置的电机和被设计用于实施根据本发明的方法的控制装置。关于根据本发明的方法的全部实施方式够可以类似地转用于根据本发明的电动车辆，因此能够利用电动车辆获得所述的优点。控制装置尤其是可以具有至少一个电动车辆控制器。例如在此可以想到，在能量管理控制器和/或电机的动力装置控制器方面确定功率预测信息，而例如另一控制器，例如中央的驾驶员辅助***的控制器使用该功率预测信息作为用于在考虑车辆模型和预测的道路数据的情况下确定加速信息的基础。
附图说明
本发明的其他优点和细节由下面所述实施例以及借助附图得出。附图示出：
图1示出根据本发明的电动车辆的原理草图，和
图2示出用于论述根据本发明的方法的草图。
具体实施方式
图1示出根据本发明的电动车辆1的原理草图，该电动车辆具有作为驱动装置的电机2，电机的驱动功率且进而必要时还有其加速功率能够通过在此仅简略示出的剩余驱动传动系3传递至机动车1的未详细示出的车轮上。在使用相应的功率电子件4的情况下，由高压电池5为电机2馈电。
机动车1还具有控制装置6，其被设计用于执行根据本发明的方法且能够具有至少一个控制器，尤其是能量管理控制器和/或动力装置控制器和/或中央的驾驶员辅助***的控制器。控制装置6与其他车辆***通信，在此仅纯例举地示出了这些其他车辆***中的导航***7和驾驶员辅助***8，该驾驶员辅助***被实施为纵向控制***，尤其是ACC***或速度调节***。
图2示出了用于执行根据本发明的方法的原理草图，该方法用于确定预测的加速信息9，所述加速信息能够被提供给驾驶员辅助***8以及必要时提供给其他车辆***10，尤其是其他驾驶员辅助***。在此以功率预测信息11为出发点，该功率预测信息描述电机2在未来的时段内、例如在未来的数秒内的预测的可用加速功率。为了确定功率预测信息，最终要检查：电机2的所能设想的最大可提供的加速功率是否会受到能量***12(图1)本身的状态或环境的状态的限制。为此检测电池5的当前的状态、尤其是其荷电状态以及检测外部温度，该外部温度描述冷却条件等且能够被输入到对能量***12和电机2的部件的生热进行描述的温度模型中，该温度模型根据单个部件的运行追踪相应的温度。该温度模型是一般的效率模型的一部分，该效率模型描述了电池5的电能转化为电机2上的驱动功率以及加速功率。
功率预测信息例如能够针对时段的不同时刻描述：在哪个时间间隔内能够提供哪个最大加速功率，如果已知了驱动传动系3的当前的特性(这能够通过另一效率模型绘示)，那么可以在此在原则上首先将该功率预测信息11换算成在电动车辆1的车轮处的车轮功率。在考虑到车轮打滑或摩擦损耗的情况下相对于所行驶的地面实现该车轮功率。
电机2的加速功率在此根据物理原理导致电动车辆1的速度改变。在此一方面使用包含了作为动态车辆参数的车辆速度以及作为静态车辆参数的车辆质量的车辆模型13，当然，在更准确的转化和建模的情况下也能够参考其他车辆特性。此外，为了能够确定加速信息9，使用尤其是至少由导向***7获取的预测的道路数据14，并且该道路数据例如包含前方道路路段的坡度信息和弯道信息。坡度和弯道对于电动车辆1的可能的加速度而言是重要的，这是因为在知道坡度和弯道的情况下能够计算出起到补偿重力加速度和离心力作用的分量。
在本实施例中，基于预测的速度变化加速信息9被确定为在该时段期间在不同的时刻的可能的加速度。也就是说，对于该时段中的每个要为之预测加速信息的待检查时刻，机动车1的预测的速度都是已知的，且可以从所示出的信息中、尤其是由功率预测信息11、车辆模型13和预测的道路数据14确定在该时刻的最大可能的加速度以及该加速度的最大的时长，这可以一直持续至存在电动车辆1的最大可达到的速度，该最大可达到的速度也可以被规定是加速信息9的一部分，但也能够在驾驶员辅助***8中被确定，例如为了阻止确定的设置速度。也可以想到其他的或者另外的数据来作为描述电动车辆1的最大可能的加速度的预测的加速信息9或其一部分，例如作为加速度在待预测的时段上的变化。
相应的加速信息9可以在驾驶员辅助***8或其他用于选择合适的运行策略的车辆***10中加以评估，这些车辆***尤其是还针对能耗最小化和/或可预测、舒适地使用驾驶员辅助***方面来选择合适的运行策略。

Claims (12)

1.一种在电动车辆(1)中确定预测的加速信息(9)的方法，该加速信息描述具有作为驱动装置的电机(2)的电动车辆(1)的未来的加速潜力，该电机(2)由电池(5)馈电，该方法包括如下步骤:
-提供电机(2)的功率预测信息(11)，该功率预测信息描述在至少一个未来的时段内电机(2)预计可用的加速功率，
-在使用由导航***(7)针对该时段提供的预测的道路数据(14)的情况下，从功率预测信息(11)中确定加速信息(9)，其中，使用对前方路段的弯道信息进行描述的预测的道路数据(14)，其中，在通过由弯道信息描述的弯道时，对抗离心力的横向加速度分量不用于提高速度，而将提高电动车辆(1)纵向速度的加速度潜在纵向分量确定为加速信息(9)。
2.根据权利要求1所述的方法，其特征在于，附加地在使用车辆模型(13)的情况下从功率预测信息(11)确定加速信息(9)，所述车辆模型描述电动车辆(1)的当前运行状态并提供对基于加速功率可能实现的加速度进行描述的至少一个车辆参数。
3.根据权利要求1或2所述的方法，
其特征在于，
所述加速信息(9)用于控制电动车辆(1)的至少一个驾驶员辅助***(8)，用于确定驾驶员辅助***(8)的预测的运行策略。
4.根据权利要求3所述的方法，其特征在于，所述驾驶员辅助***(8)是纵向控制***。
5.根据权利要求1或2所述的方法，
其特征在于，
为了确定功率预测信息(11)，使用对电池(5)的当前状态和/或被配设给电池(5)和/或电机(2)的功率电子件(4)的当前状态进行描述的至少一个能量信息，和/或对电机(2)和/或电池(5)和/或功率电子件(4)的运行条件进行描述的至少一个环境信息。
6.根据权利要求5所述的方法，
其特征在于，
所述能量信息包括电池(5)的荷电状态和/或电池(5)的老化状态，和/或所述环境信息包括外部温度和/或电池温度，和/或使用通过能量信息和/或环境信息参数化的效率模型，该效率模型描述了存储在电池(5)中的电能转化为可在电机(2)处获得的加速功率。
7.根据权利要求2所述的方法，
其特征在于，
使用提供至少一个动态的车辆参数和至少一个静态的车辆参数的车辆模型(13)。
8.根据权利要求7所述的方法，
其特征在于，
使用电动车辆(1)的当前的车辆质量和/或当前的质量分布和/或当前的和/或预测的速度作为车辆参数。
9.根据权利要求1或2所述的方法，
其特征在于，
使用对前方的路段的坡度信息进行描述的预测的道路数据(14)，和/或在基于预测的道路数据(14)的道路状况信息和/或摩擦系数信息的情况下考虑车轮打滑。
10.根据权利要求1或2所述的方法，
其特征在于，
预测的道路数据(14)包括电动车辆(1)的预测的速度变化和/或从预测的道路数据(14)中确定电动车辆(1)的预测的速度变化，其中电动车辆(1)在所述时段的一个时刻的预测速度被考虑用于确定在该时刻的加速潜力。
11.根据权利要求1或2所述的方法，
其特征在于，
对在所述时段内的最大可能的加速度进行描述的加速度变化被确定为加速信息(9)，和/或为所述加速信息(9)分配有至少一个对于与理论加速潜力产生偏差的原因信息。
12.一种电动车辆(1)，具有由电池(5)馈电的电机(2)作为驱动装置和被设计用于执行根据权利要求1至11之一所述的方法的控制装置(6)。
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