CN114056320A - 新能源车辆的能耗回收比预测方法、节能控制方法和系统 - Google Patents

新能源车辆的能耗回收比预测方法、节能控制方法和系统 Download PDF

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CN114056320A
CN114056320A CN202010790881.7A CN202010790881A CN114056320A CN 114056320 A CN114056320 A CN 114056320A CN 202010790881 A CN202010790881 A CN 202010790881A CN 114056320 A CN114056320 A CN 114056320A
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energy
vehicle
recovery ratio
energy consumption
new energy
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涂岩恺
赖俊芳
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Xiamen Yaxon Networks Co Ltd
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Xiamen Yaxon Networks Co Ltd
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Priority to EP21852315.7A priority patent/EP4194249A1/en
Priority to PCT/CN2021/108013 priority patent/WO2022028257A1/zh
Priority to US18/020,101 priority patent/US20230264578A1/en
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Abstract

新能源车辆的能耗回收比预测方法、节能控制方法和系统，包括如下步骤：1)通过电子地平线系统获取新能源车辆当前位置的道路前方K米的地理信息数据；2)统计新能源车辆行驶S米后到达当前位置的过程中的车速信息，K与S的数值相同；3)将所述地理信息数据和所述车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比。本发明在优化车辆的能源控制，提高电能利用率，增加行驶里程等方面均有积极的作用。

Description

新能源车辆的能耗回收比预测方法、节能控制方法和系统
技术领域
本发明涉及新能源车领域，特别是指新能源车辆的能耗回收比预测方法、节能控制方法和系统。
背景技术
新能源车(纯电动或混动车)不仅可以靠电池等蓄能原件输出能量提供车辆行驶，还可以靠车辆制动或滑行回收能量到蓄能原件，比传统汽车有更高的能源经济性。制动能量回收包括松开加速踏板的滑行回馈能量回收和踩下制动踏板的制动回馈能量回收两种。通过制动能量回收技术，将电动汽车在制动或惯性滑行中释放出的多余能量转化为电能，再储存在EV的蓄电池中，从而将释放的多余能量回收利用，为EV的后续行驶提供驱动能量。可见，制动能量回收对于提高电动汽车的能量利用率具有非常重要的意义。
目前，影响新能源车能耗和回收的情况多而复杂，包括道路坡度、交通标志、交通流拥堵情况、道路曲率、交叉路口等等，因此较难靠外部条件进行简单判断，来预测车辆的能耗和回收情况。同时，除了外部客观环境，车辆的能耗和回收和车辆自身的配置、性能甚至自身的驾驶特性也有紧密关系，因此，如果要对车辆的能耗和回收情况做出准确预测，并不能用外部条件进行简单逻辑判断。
因此，研究新能源车在不同地形、路况、交通环境等工况下的能耗与能量回收规律，并能进行准确预测，对于优化车辆的能源控制，提高电能利用率，增加行驶里程，优化驾驶舒适度等方面均有积极的作用。
发明内容
本发明的主要目的在于，提出一种车辆的能耗回收比预测方法、节能控制方法和系统，在优化车辆的能源控制，提高电能利用率，增加行驶里程等方面均有积极的作用。
本发明采用如下技术方案：
一种新能源车辆的能耗回收比预测方法，其特征在于，包括如下步骤：
1)通过电子地平线系统获取新能源车辆当前位置的道路前方K米的地理信息数据；
2)统计新能源车辆行驶S米后到达当前位置的过程中的车速信息，K与S的数值相同；
3)将所述地理信息数据和所述车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比。
优选的，所述人工神经网络的训练方法如下：
3.1)通过电子地平线系统获取道路前方K米的地理信息数据；
3.2)新能源车辆在所述K米内行驶时，统计能耗和能量回收量计算得到能耗回收比P,并能获取车速信息；
3.3)将地理信息数据和车速信息作为输入向量，输入人工神经网络进行训练，则输出向量为预测的能耗回收比P'；
3.4)根据能耗回收比P和预测的能耗回收比P'计算损失函数，修正人工神经网络中间各层节点的权值，回到步骤3.1)继续训练直至人工神经网络收敛。
优选的，所述人工神经网络为误差反馈型神经网络。
优选的，设定所述地理信息数据包括N类数据，每类数据的分辨率为T米，则建立包含N*K/T+4个元素的一维输入向量，每一类数据按照车辆由近到远的顺序填入该一维输入向量，车速信息包括最小车速、最大车速、平均速度和车速标准差且作为该一维输入向量的后四位元素。
一种新能源车辆的节能控制方法，其特征在于：新能源车辆行驶时，采用上述中任一项所述的一种新能源车辆的能耗回收比预测方法，预测当前位置的道路前方K米的能耗回收比，并根据该能耗回收比进行节能控制。
优选的，在混合动力车辆中，若能耗回收比小于预设的阈值，则控制车辆先消耗易于回收能量的能量存储介质中的能量。
优选的，对于柴油机和电池的混合动力车辆，若能耗回收比小于预设的阈值，则控制切换到纯电动模式行驶或增加电力输出比例。
优选的，对于电池和超级电容的混合动力车辆，若能耗回收比小于预设的阈值，则控制切换到超级电容输出或增加超级电容输出比例。
优选的，所述能量存储介质包括动力电池、超级电容或液压储能罐。
一种新能源车辆的节能控制系统，其特征在于：包括
地理信息数据获取装置，获取电子地平线系统中，新能源车辆的当前位置的道路前方K米的地理信息数据；
车速信息获取装置，读取车辆行驶S米后到达所述当前位置的过程中的车速信息；
能耗回收比预测装置，将所述地理信息数据和所述车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比；
节能控制装置，根据能耗回收比对所述新能源车辆进行节能控制。
由上述对本发明的描述可知，与现有技术相比，本发明具有如下有益效果：
1、本发明的预测方法，本发明提出能耗回收比的定义，通过电子地平线系统获取道地理信息数据，将其与车速信息输入已训练好的人工神经网络，得到预测的能耗回收比，用于新能源车的预测性整车优化控制，相对于传统的实时性控制，具有更为经济的效果。
2、本发明的预测方法，根据电子地平线系统提供的车辆前方某段道路环境的格式化信息做为人工神经网络的输入，结合车辆行驶完这段道路之后统计的车速信息作为人工神经网络输入，车辆行驶完这段道路之后统计能耗回收比做为人工神经网络的输出，进行车辆运行中的动态训练，训练出收敛的神经网络，提高预测准确率，具有自适应、自组织和实时学习的优点。
3、本发明的控制方法和系统，预测的能耗回收比进行节能控制，提高电能利用率、增加行驶里程等。
4、本发明的控制方法和系统，将能耗回收比与预设的阈值进行比较，判断是否控制车辆先消耗易于回收能量的能量存储介质中的能量，这样到达前方工况后，电池就有较多的空间可回收能量，判断方式简单，易于实现。
附图说明
图1为本发明方法原理图；
图2为本发明的地理信息数据坡度点示意图；
图3为本发明的一维输入向量示意图；
以下结合附图和具体实施例对本发明作进一步详述。
具体实施方式
以下通过具体实施方式对本发明作进一步的描述。
一种新能源车辆的能耗回收比预测方法，定义了能耗回收比，其为新能源车辆上的做为车辆行驶驱动的能量存储介质，在某段路程中消耗的能量与回收的能量之比P＝Pw/Pr，其中Pw为能耗,Pr为能量回收量，可从车辆上的相关能量器件中测量得到。比如，电池输出多少能量，回收多少能量，通过电池输出或输入的电流电压计算功率得到。能量存储介质包括但不限于动力电池、超级电容、液压储能罐等公知的元件。
参见图1，本发明的方法包括如下步骤：
1)通过电子地平线系统获取新能源车辆的当前车辆位置的道路前方K米的地理信息数据。电子地平线技术是指依靠高精度地图数据和GPS信号，为车辆提供前方道路准确的信息，使得车辆具有预测前方道路状况的能力。电子地平线技术能为车辆动力和其他电子控制提供可预见性的道路信息。
将电子地平线提供的数据，转化为以道路偏移结合数值的形式。如电子地平线输出的前方坡度数据为与道路偏移值(偏移值为相对道路起点需要行驶的距离，单位为米)建立了关系的坡度点。参见图2中显示的两个坡度点P1，P2，坡度P1，P2在电子地平线的表示方式为与道路起点的偏移offset值和对应坡度值slp。电子地平线播发的前方道路坡度，即是一串连续等间隔T米(如间隔5米)的坡度点。道路曲率、拥堵程度、限速等也与坡度类似，由一系列结合位置偏移的值组成。
2)统计新能源车辆行驶S米后达到当前车辆位置的过程中的车速信息，即S米是车辆当前已经行驶过路程，步骤1)中的K米是车辆当前未行驶且即将行驶的路程，S与K数值相同。
3)将地理信息数据和车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比。本发明的已训练好的人工神经网络为误差反馈型神经网络，例如BP神经网络或深度学习的CNN网络，不限于此，还可采用公知的其它人工神经网络。
本发明中，可将电子地平线系统输出数据进行前方K/T米的电子地平线数据原始格式化排列，作为神经网络的输入向量。由于电子地平线系统一般可预测前方道路距离为8000米，则K取8000。K取值不限于此，也可根据承裁神经网络训练算法的装置的硬件计算能力，对K进行增大或减小。
因此，设定所述地理信息数据包括N类数据，每类数据的分辨率(间隔)为T米，则建立包含N*K/T+4个元素的一维输入向量，每一类数据按照车辆由近到远的顺序填入该一维输入向量，车速信息包括Vmin为车辆在K米距离内行驶的最小速度，Vmax为车辆最大速度，Vave为平均速度，D为车速标准差，包括最小车速、最大车速、平均速度和车速标准差作为后四位元素，车辆行驶完K米距离后再统计得出后排入。
举例：假设对于简化的电子地平线系统,数据间隔T＝5，假设只出输道路前方K米的坡度(s)和交通拥堵程度(c)两类信息，则建立一个2*K/T+4的一维输入变量，如图3所示将前方所有坡度点值s相对车辆由近到远排入输入向量，接着把所有交通拥堵程度值c相对车辆由近到远排入输入向量。其所有电子地平线可输出的信息都可按此格式化排列，并输入神经网络。后四维向量为车速信息。
本发明的人工神经网络的训练方法如下：
3.1)通过电子地平线系统获取道路前方K米的地理信息数据，假设包括N类数据，每类数据的分辨率(或间隔)为T米，则建立包含N*K/T+4个元素的一维输入向量。
3.2)车辆在该K米内行驶时，统计能耗和能量回收量计算得到能耗回收比P,并能获取车速信息。车辆在该K米内行驶时，是指车辆在步骤3.1)中的道路前方K米内行驶时。
3.3)将地理信息数据和车速信息作为输入向量，输入人工神经网络进行训练，则输出向量为预测的能耗回收比P'。具体的，将第一类电子地平线数据，按相对车辆由近到远的顺序，填入一维向量，填完之后再填下一类地理信息数据，直到所有类别的数据均填入一维向量。将最小车速Vmin，最大车速Vmax，平均速度Vave，车速标准差D等车速信息填入一维输入向量的后四位。神经网络输入向量为N*K/T+4个元素的一维输入，输出为1维输出，1维输出即代表预测的能耗回收比P'。
3.4)根据能耗回收比P和预测的能耗回收比P'计算损失函数，修正人工神经网络中间各层节点的权值。回到步骤3.1)继续训练直至人工神经网络收敛。
具体的，神经网络的损失函数以所选神经网络适用性来选择，例如，对于BP神经网络，一般选用交叉熵损失函数：
E＝-(P'lnP+(1-P')ln(1-P))。
对于选择其它神经网络，可以选用该神经网络在工程上公知的损失函数计算。完成计算损失函数后，利用该神经网络所公知的误差修正方法，修正神经网络中间各层节点的权值，即可完成一次训练。
本发明的能耗回收比可做为新能源车优化控制的核心参数，控制优化车辆的能量分配或输出，使新能源汽车的能量控制具有根据前方能耗回收比进行预测性优化的特点，提高新能源车的经济性、行驶里程等各项性能。
因此，本发明还提出一种新能源车辆的节能控制方法，新能源车辆行驶时，采用上述的新能源车辆的能耗回收比预测方法，预测道路前方K米的能耗回收比，且根据该能耗回收比进行节能控制。具体的，在混合动力车辆中，若能耗回收比小于预设的阈值，表示对于该车辆，前方K米内有较多能量回收，则控制车辆先消耗易于回收能量的能量存储介质中的能量。
例如：对于柴油机和电池的混合动力车辆，由于电池是相对柴油来说是能够回收能量的存储介质，若能耗回收比小于预设的阈值，则控制切换到纯电动模式行驶或增加新能源中的电力输出比例，先消耗电源电量，这样到达前方工况后，电池就有较多的空间可回收能量。
对于电池和超级电容的混合动力车辆，由于超级电容回收能量的效率较高，更易于回收能量，若能耗回收比小于预设的阈值，则控制切换到超级电容输出或增加超级电容输出比例，先消耗超级电容电量，这样到达前方工况后，超级电容就有较多的空间可回收能量。
本发明中，阈值的确定，可以有多种工程上易于想到的方法。例如，对大样本进行收集，确定合理的阈值，或直接根据经验值选定，等等。
本发明还一种新能源车辆的节能控制系统，包括
地理信息数据获取装置，获取电子地平线系统中，新能源车辆的当前位置的道路前方K米的地理信息数据。
车速信息获取装置，读取新能源车辆行驶S米后到达当前位置的过程中的车速信息。
能耗回收比预测装置，将地理信息数据和车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比。
节能控制装置，根据能耗回收比对新能源车辆进行节能控制。
本发明的方法和系统，采用电子地平线数据训练学习和预测前方能耗回收比，可用于新能源车的预测性整车优化控制，相对于传统的实时性控制，本发明的基于前方电子地平线数据的预测性学习与控制可取得更为经济的控制效果。
上述仅为本发明的具体实施方式，但本发明的设计构思并不局限于此，凡利用此构思对本发明进行非实质性的改动，均应属于侵犯本发明保护范围的行为。

Claims (10)

1.一种新能源车辆的能耗回收比预测方法，其特征在于，包括如下步骤：
1)通过电子地平线系统获取新能源车辆当前位置的道路前方K米的地理信息数据；
2)统计新能源车辆行驶S米后到达当前位置的过程中的车速信息，K与S的数值相同；
3)将所述地理信息数据和所述车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比。
2.如权利要求1所述的一种新能源车辆的能耗回收比预测方法，其特征在于，所述人工神经网络的训练方法如下：
3.1)通过电子地平线系统获取道路前方K米的地理信息数据；
3.2)新能源车辆在所述K米内行驶时，统计能耗和能量回收量计算得到能耗回收比P,并能获取车速信息；
3.3)将地理信息数据和车速信息作为输入向量，输入人工神经网络进行训练，则输出向量为预测的能耗回收比P'；
3.4)根据能耗回收比P和预测的能耗回收比P'计算损失函数，修正人工神经网络中间各层节点的权值，回到步骤3.1)继续训练直至人工神经网络收敛。
3.如权利要求1所述的一种新能源车辆的能耗回收比预测方法，其特征在于，所述人工神经网络为误差反馈型神经网络。
4.如权利要求1或2所述的一种新能源车辆的能耗回收比预测方法，其特征在于，设定所述地理信息数据包括N类数据，每类数据的分辨率为T米，则建立包含N*K/T+4个元素的一维输入向量，每一类数据按照车辆由近到远的顺序填入该一维输入向量，车速信息包括最小车速、最大车速、平均速度和车速标准差且作为该一维输入向量的后四位元素。
5.一种新能源车辆的节能控制方法，其特征在于：新能源车辆行驶时，采用权利要求1至4中任一项所述的一种新能源车辆的能耗回收比预测方法，预测当前位置的道路前方K米的能耗回收比，并根据该能耗回收比进行节能控制。
6.如权利要求5所述的一种新能源车辆的节能控制方法，其特征在于：在混合动力车辆中，若能耗回收比小于预设的阈值，则控制车辆先消耗易于回收能量的能量存储介质中的能量。
7.如权利要求5所述的一种新能源车辆的节能控制方法，其特征在于：对于柴油机和电池的混合动力车辆，若能耗回收比小于预设的阈值，则控制切换到纯电动模式行驶或增加电力输出比例。
8.如权利要求5所述的一种新能源车辆的节能控制方法，其特征在于：对于电池和超级电容的混合动力车辆，若能耗回收比小于预设的阈值，则控制切换到超级电容输出或增加超级电容输出比例。
9.如权利要求5所述的一种新能源车辆的节能控制方法，其特征在于：所述能量存储介质包括动力电池、超级电容或液压储能罐。
10.一种新能源车辆的节能控制系统，其特征在于：包括
地理信息数据获取装置，获取电子地平线系统中，新能源车辆的当前位置的道路前方K米的地理信息数据；
车速信息获取装置，读取车辆行驶S米后到达所述当前位置的过程中的车速信息；
能耗回收比预测装置，将所述地理信息数据和所述车速信息作为输入向量，输入已训练好的人工神经网络，输出预测的能耗回收比；
节能控制装置，根据能耗回收比对所述新能源车辆进行节能控制。
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