CN114954022B - Electric automobile cloud cooperative control device and method - Google Patents
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Abstract
本申请涉及一种电动汽车车云协同控制装置和方法，它包括电动汽车管理系统、云端平台和充电机系统，通过云端平台获取同批次N辆电动汽车在一个运营周期内的充电历史数据和能耗历史数据；根据充电历史数据和能耗历史数据计算第一预估充电时间Ta、第二预估充电时间Tb和模拟续驶里程S1；计算第一平均准确率a、第二平均准确率b和第三平均准确率c；通过加权计算得到预估充电时间T和预估续驶里程S。本发明基于云端管理平台和大数据，依据车辆实际运营情况，采用在线计算和离线计算相结合方式，实现车云协同智能化管理，精确估算车辆充电剩余时间和剩余里程，消除司机里程担忧，保障车辆安全可靠运营。
The present application relates to an electric vehicle cloud collaborative control device and method, which includes an electric vehicle management system, a cloud platform and a charger system, wherein the charging history data and energy consumption history data of N electric vehicles in the same batch in one operation cycle are obtained through the cloud platform; the first estimated charging time Ta , the second estimated charging time Tb and the simulated driving range S1 are calculated according to the charging history data and the energy consumption history data; the first average accuracy a, the second average accuracy b and the third average accuracy c are calculated; and the estimated charging time T and the estimated driving range S are obtained through weighted calculation. The present invention is based on a cloud management platform and big data, and according to the actual operation of the vehicle, a combination of online calculation and offline calculation is adopted to realize intelligent management of vehicle cloud collaboration, accurately estimate the remaining charging time and remaining mileage of the vehicle, eliminate the driver's mileage concerns, and ensure safe and reliable operation of the vehicle.
Description
技术领域Technical Field
本申请涉及电动汽车控制管理领域，具体涉及一种电动汽车车云协同控制装置和方法。The present application relates to the field of electric vehicle control management, and specifically to an electric vehicle vehicle-cloud collaborative control device and method.
背景技术Background Art
目前，司机对电动汽车的里程担忧，仍是困扰行业发展的难题，由于电动车辆的工况和运营环境的复杂性，依据当前时刻的整车状态信息估算车辆剩余里程，误差较大，经常出现显示续驶里程的跳变，突发无预期的车辆抛锚事故，引发用户的焦虑与不满，也严重影响了新能源汽车的推广应用。另外，在电动车辆充电时，由于充电时间受多种因素的影响，在当前状态下对充电剩余时间的估算难度较大，用户在对电动汽车充电时，由于充电剩余时间不准确，影响客户的等待预期，无法提前规划车辆运营路线，干扰车辆运营模式，无法实现收益最大化。At present, drivers' concerns about the mileage of electric vehicles are still a problem that plagues the development of the industry. Due to the complexity of the working conditions and operating environment of electric vehicles, the remaining mileage of the vehicle is estimated based on the current vehicle status information, which has large errors. The displayed mileage often jumps, and unexpected vehicle breakdowns occur suddenly, causing users' anxiety and dissatisfaction, and seriously affecting the promotion and application of new energy vehicles. In addition, when charging electric vehicles, since the charging time is affected by many factors, it is difficult to estimate the remaining charging time in the current state. When users charge electric vehicles, the remaining charging time is inaccurate, which affects the customer's waiting expectations, and the vehicle operation route cannot be planned in advance, which interferes with the vehicle operation mode and cannot maximize the benefits.
发明内容Summary of the invention
本发明的目的在于，提供一种电动汽车车云协同控制装置和方法，基于云端管理平台和大数据，依据车辆实际运营情况，采用在线计算和离线计算相结合方式，实现车云协同智能化管理，精确估算车辆充电剩余时间和剩余里程，消除司机里程担忧，保障车辆安全可靠运营。The purpose of the present invention is to provide an electric vehicle vehicle-cloud collaborative control device and method, which is based on a cloud management platform and big data, and adopts a combination of online and offline calculations according to the actual operation of the vehicle to achieve vehicle-cloud collaborative intelligent management, accurately estimate the remaining charging time and remaining mileage of the vehicle, eliminate the driver's mileage concerns, and ensure safe and reliable operation of the vehicle.
本申请采取的一种技术方案是：一种电动汽车车云协同控制方法，包括如下步骤：A technical solution adopted by the present application is: a vehicle-cloud collaborative control method for an electric vehicle, comprising the following steps:
S101：获取同批次N辆电动汽车在一个运营周期内的充电历史数据和能耗历史数据，所述充电历史数据包括N辆电动汽车在不同的电池电量值、充电电流值和电池温度值下的充电时间，所述能耗历史数据包括N辆电动汽车在不同的环境温度、电池电量值、电池温度值、空调开启情况和路况下的续驶里程；S101: Obtain charging history data and energy consumption history data of N electric vehicles of the same batch in an operation cycle, wherein the charging history data includes charging time of the N electric vehicles under different battery power values, charging current values, and battery temperature values, and the energy consumption history data includes driving range of the N electric vehicles under different ambient temperatures, battery power values, battery temperature values, air conditioning activation conditions, and road conditions;
S102：对步骤S101获得的充电历史数据，根据不同电池电量值和充电电流值下的充电时间计算第一预估充电时间Ta，根据不同电池电量值和电池温度值的充电时间计算第二预估充电时间Tb；S102: for the charging history data obtained in step S101, a first estimated charging time Ta is calculated according to the charging time at different battery power values and charging current values, and a second estimated charging time Tb is calculated according to the charging time at different battery power values and battery temperature values;
对步骤S101获得的能耗历史数据，将每个能耗历史数据的环境温度、电池电量值、电池温度值、空调开启情况和路况输入训练好的BP神经网络中，得到与每个能耗历史数据对应的模拟续驶里程S1；For the energy consumption history data obtained in step S101, the ambient temperature, battery power value, battery temperature value, air conditioning start status and road condition of each energy consumption history data are input into the trained BP neural network to obtain the simulated driving range S 1 corresponding to each energy consumption history data;
S103：对步骤S101中N辆电动汽车，将每辆电动汽车的每个充电历史数据与对应的第一预估时间Ta和第二预估时间Tb进行比较，分别计算每辆电动汽车的第一预估充电时间准确率和第二预估充电时间准确率；对N辆电动汽车的第一预估充电时间准确率求取平均值，得到第一平均准确率a；对N辆电动汽车的第二预估充电时间准确率求取平均值，得到第二平均准确率b；S103: for the N electric vehicles in step S101, each charging history data of each electric vehicle is compared with the corresponding first estimated time Ta and second estimated time Tb , and the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle are calculated respectively; the first estimated charging time accuracy of the N electric vehicles is averaged to obtain a first average accuracy a; the second estimated charging time accuracy of the N electric vehicles is averaged to obtain a second average accuracy b;
将每辆电动汽车的每个能耗历史数据与对应的模拟续驶里程进行比较，分别计算每辆电动汽车的预估续驶里程准确率，对N辆电动汽车的预估续驶里程准确率求取平均值，得到第三平均准确率c；Compare each energy consumption history data of each electric vehicle with the corresponding simulated driving range, calculate the accuracy of the estimated driving range of each electric vehicle respectively, and average the estimated driving range accuracy of N electric vehicles to obtain a third average accuracy c;
S104：根据第一平均准确率a和第二平均准确率b对第一预估充电时间Ta、第二预估充电时间Tb进行加权计算，得到预估充电时间T；根据第三平均准确率c对模拟续驶里程S1进行加权计算，得到预估续驶里程S。S104: performing weighted calculation on the first estimated charging time Ta and the second estimated charging time Tb according to the first average accuracy a and the second average accuracy b to obtain the estimated charging time T; performing weighted calculation on the simulated driving range S1 according to the third average accuracy c to obtain the estimated driving range S.
进一步地，所述步骤S102中计算第一预估充电时间和第二预估充电时间的具体方法为：Furthermore, the specific method for calculating the first estimated charging time and the second estimated charging time in step S102 is:
将电池电量值范围[0，100]以固定间隔进行分段，将充电电流值范围[0，Imax]以固定间隔进行分段，其中Imax为最大充电电流；将充电历史数据在不同电池电量值段和充电电流值段内的数据取平均值，作为当前电池电量值段和充电电流值段下的第一预估充电时间Ta；The battery power value range [0, 100] is segmented at fixed intervals, and the charging current value range [0, I max ] is segmented at fixed intervals, where I max is the maximum charging current; the charging history data in different battery power value segments and charging current value segments are averaged as the first estimated charging time T a under the current battery power value segment and charging current value segment;
将电池电量值范围[0，100]以固定间隔进行分段，电池温度值[Tmin，Tmax]以固定间隔进行分段，其中Tmin为动力电池正常工作的最低温度，Tmax为动力电池正常工作的最高温度；将充电历史数据在不同电池电量值段和电池温度值段内的数据取平均值，作为当前电池电量值段和充电电流值段下的第二预估充电时间Tb。The battery power value range [0, 100] is segmented at fixed intervals, and the battery temperature value [T min , T max ] is segmented at fixed intervals, where T min is the lowest temperature for normal operation of the power battery, and T max is the highest temperature for normal operation of the power battery; the charging history data in different battery power value segments and battery temperature value segments are averaged to serve as the second estimated charging time T b under the current battery power value segment and charging current value segment.
进一步地，所述电池电量值范围以5％为间隔进行分段，所述充电电流值范围以5A为间隔进行分段，所述电池温度值以5℃为间隔进行分段。Furthermore, the battery power value range is segmented at intervals of 5%, the charging current value range is segmented at intervals of 5A, and the battery temperature value is segmented at intervals of 5°C.
进一步地，所述充电历史数据满足电池电量值越低，充电电流值越小，充电时间越长的规律，充电历史数据中不满足规律的数据则舍弃，不用于计算第一预估充电时间Ta。Furthermore, the charging history data meets the rule that the lower the battery power value, the smaller the charging current value, and the longer the charging time. Data in the charging history data that does not meet the rule is discarded and is not used to calculate the first estimated charging time Ta .
进一步地，所述充电历史数据满足电池电量值越低，电池温度值在适宜充电温度下越低，或者在适宜充电温度上越高，充电时间越长的规律，充电历史数据中不满足规律的数据则舍弃，不用于计算第二预估充电时间Tb。Furthermore, the charging history data satisfies the rule that the lower the battery power value, the lower the battery temperature value at the appropriate charging temperature, or the higher the battery temperature at the appropriate charging temperature, the longer the charging time. Data in the charging history data that do not meet the rule are discarded and not used to calculate the second estimated charging time T b .
进一步地，所述步骤S103中计算每辆电动汽车的第一预估充电时间准确率和第二预估充电时间准确率的具体方法为：Furthermore, the specific method for calculating the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle in step S103 is:
在一个运营周期内，对单量电动汽车有X个充电历史数据Tr，求出每个充电历史数据对应的第一预估时间Ta和第二预估时间Tb，并根据下列公式计算单量车的单个充电历史数据的准确率：In an operation cycle, there are X charging history data Tr for a single electric vehicle. The first estimated time Ta and the second estimated time Tb corresponding to each charging history data are calculated, and the accuracy of a single charging history data of a single vehicle is calculated according to the following formula:
a1＝1-|Ta-Tr|/Tr a1 ＝1-| Ta - Tr |/ Tr
b1＝1-|Tb-Tr|/Tr b1 ＝1-|Tb -Tr | / Tr
其中，a1为单量车的单个充电历史数据的第一预估充电时间准确率，b1为单量车的单个充电历史数据的第二预估充电时间准确率；Wherein, a1 is the first estimated charging time accuracy of the single charging history data of a single vehicle, and b1 is the second estimated charging time accuracy of the single charging history data of a single vehicle;
分别对单量电动汽车X个充电历史数据的第一预估充电时间准确率和第二预估充电时间准确率求平均值，作为每辆电动汽车的第一预估充电时间准确率和第二预估充电时间准确率。The first estimated charging time accuracy and the second estimated charging time accuracy of the X charging history data of a single electric vehicle are averaged to serve as the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle.
进一步地，所述步骤S103中计算每辆电动汽车的预估续驶里程准确率的具体方法为：Furthermore, the specific method for calculating the accuracy of the estimated driving range of each electric vehicle in step S103 is:
在一个运营周期内，对单量电动汽车有Y个能耗历史数据Sr，求出每个能耗历史数据对应模拟续驶里程S1，并根据下列公式计算单量车的能耗历史数据的准确率：In an operation cycle, there are Y energy consumption historical data S r for a single electric vehicle. The simulated driving range S 1 corresponding to each energy consumption historical data is calculated, and the accuracy of the energy consumption historical data of a single vehicle is calculated according to the following formula:
c1＝1-|S1-Sr|/Sr c 1 =1-|S 1 -S r |/S r
其中，c1为单量车的单个能耗历史数据的准确率；Among them, c 1 is the accuracy of the single energy consumption historical data of a single vehicle;
分别对单量电动汽车Y个充电历史数据的的准确率求平均值，作为每辆电动汽车的预估续驶里程准确率。The accuracy of Y charging history data of a single electric vehicle is averaged and used as the accuracy of the estimated driving range of each electric vehicle.
进一步地，所述步骤S104中计算预估充电时间T的具体方法为：Furthermore, the specific method for calculating the estimated charging time T in step S104 is:
T＝a/(a+b)*T1+b/(a+b)*T2 T＝a/(a+b)*T 1 +b/(a+b)*T 2
进一步地，所述步骤S104中计算预估续驶里程S的具体方法为：Furthermore, the specific method for calculating the estimated driving range S in step S104 is:
S＝S1/cS＝S 1 /c
本发明采取的另一种技术方案是：一种电动汽车车云协同控制装置，包括电动汽车管理系统、云端平台和充电机系统；所述电动汽车管理系统包括电池管理系统BMS、整车控制系统VCU、车辆监控系统和仪表显示装置，电池管理系统BMS与仪表显示装置、车辆监控系统和充电机系统通过CAN通讯进行数据连接，整车控制系统VCU与仪表显示装置和车辆监控系统通过CAN通讯进行数据连接，车辆监控系统和云端平台通过无线传输进行数据连接。Another technical solution adopted by the present invention is: a vehicle-cloud collaborative control device for an electric vehicle, including an electric vehicle management system, a cloud platform and a charger system; the electric vehicle management system includes a battery management system BMS, a vehicle control system VCU, a vehicle monitoring system and an instrument display device, the battery management system BMS is data connected to the instrument display device, the vehicle monitoring system and the charger system via CAN communication, the vehicle control system VCU is data connected to the instrument display device and the vehicle monitoring system via CAN communication, and the vehicle monitoring system and the cloud platform are data connected via wireless transmission.
本发明的有益效果在于：The beneficial effects of the present invention are:
(1)通过调取同批次车辆在云端平台上的历史数据进行离线计算，对电动汽车的剩余充电时间和剩余里程进行估算，并根据电动汽车的运行状态参数对剩余充电时间和剩余里程进行在线计算和实时更新，准确预估剩余充电时间和剩余里程，消除司机里程担忧，保障车辆安全可靠运营；(1) The remaining charging time and remaining mileage of electric vehicles are estimated by retrieving historical data of the same batch of vehicles on the cloud platform for offline calculation. The remaining charging time and remaining mileage are calculated and updated online in real time based on the operating status parameters of the electric vehicles. The remaining charging time and remaining mileage are accurately estimated, eliminating drivers' mileage concerns and ensuring safe and reliable vehicle operation.
(2)通过筛选符合规律的历史数据、分段计算第一预估充电时间和第二预估充电时间、计算数据平均准确率以及加权计算的方式对预估充电时间和预估续驶里程进行计算和修正，有效减小数据误差，提高计算精度，从而精确估算车辆充电剩余时间和剩余里程，实现车云协同智能化管理。(2) The estimated charging time and estimated driving range are calculated and corrected by screening historical data that conforms to the rules, calculating the first estimated charging time and the second estimated charging time in segments, calculating the average accuracy of the data, and performing weighted calculations, thereby effectively reducing data errors and improving calculation accuracy, thereby accurately estimating the remaining charging time and remaining mileage of the vehicle and realizing intelligent management of vehicle-cloud collaboration.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为本发明实施例的装置结构示意图；FIG1 is a schematic diagram of the structure of a device according to an embodiment of the present invention;
图2为本发明实施例的步骤图；FIG2 is a step diagram of an embodiment of the present invention;
图3为本发明实施例计算预估充电时间T的流程图；FIG3 is a flow chart of calculating the estimated charging time T according to an embodiment of the present invention;
图4为本发明实施例计算预估续驶里程S的流程图；FIG4 is a flow chart of calculating an estimated driving range S according to an embodiment of the present invention;
图5为本发明实施例使用的BP网络模型示意图。FIG5 is a schematic diagram of a BP network model used in an embodiment of the present invention.
具体实施方式DETAILED DESCRIPTION
为了能够更清楚地理解本发明的上述目的、特征和优点，下面结合附图和具体实施方式对本发明进行进一步的详细描述。在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是，本发明还可以采用其他不同于在此描述的其他方式来实施，因此，本发明并不限于下面公开的具体实施例的限制。In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and therefore, the present invention is not limited to the limitations of the specific embodiments disclosed below.
除非另作定义，此处使用的技术术语或者科学术语应当为本申请所述领域内具有一般技能的人士所理解的通常意义。本专利申请说明书以及权利要求书中使用的“第一”、“第二”以及类似的词语并不表示任何顺序、数量或者重要性，而只是用来区分不同的组成部分。同样，“一个”或者“一”等类似词语也不表示数量限制，而是表示存在至少一个。“连接”或者“相连”等类似的词语并非限定于物理的或者机械的连接，而是可以包括电性的连接，不管是直接的还是间接的。Unless otherwise defined, the technical or scientific terms used herein shall have the usual meaning understood by persons having ordinary skills in the field described in this application. The words "first", "second" and similar words used in this patent application specification and claims do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "a" do not indicate a quantity limitation, but indicate the existence of at least one. Words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
如图1所示，本发明实施例采用了一种电动汽车车云协同控制装置，包括电动汽车管理系统、云端平台和充电机系统；所述电动汽车管理系统包括电池管理系统BMS、整车控制系统VCU、车辆监控系统和仪表显示装置，电池管理系统BMS与仪表显示装置、车辆监控系统和充电机系统通过CAN通讯进行数据连接，整车控制系统VCU与仪表显示装置和车辆监控系统通过CAN通讯进行数据连接，车辆监控系统和云端平台通过无线传输进行数据连接。As shown in Figure 1, an embodiment of the present invention adopts an electric vehicle vehicle-cloud collaborative control device, including an electric vehicle management system, a cloud platform and a charger system; the electric vehicle management system includes a battery management system BMS, a vehicle control system VCU, a vehicle monitoring system and an instrument display device, the battery management system BMS is data connected with the instrument display device, the vehicle monitoring system and the charger system through CAN communication, the vehicle control system VCU is data connected with the instrument display device and the vehicle monitoring system through CAN communication, and the vehicle monitoring system and the cloud platform are data connected through wireless transmission.
电池管理系统BMS用于实现电池系统信息监控、安全评估以及充电剩余时间在线估算，并与车辆监控系统及充电机系统进行通讯，实现数据传输；车辆监控系统用于实现整车所有信息收集，并和云端平台实现无线数据传输；仪表显示装置用于显示预估剩余充电时间和预估续驶里程；云端平台用于实现电动车辆生命周期内大数据存储以及离线计算功能。The battery management system BMS is used to realize battery system information monitoring, safety assessment and online estimation of remaining charging time, and communicate with the vehicle monitoring system and charger system to realize data transmission; the vehicle monitoring system is used to realize the collection of all vehicle information and realize wireless data transmission with the cloud platform; the instrument display device is used to display the estimated remaining charging time and estimated driving range; the cloud platform is used to realize big data storage and offline computing functions during the life cycle of the electric vehicle.
如图2所示，基于图1所示的控制装置，本发明实施例采用了一种电动汽车车云协同控制方法，包括如下步骤：As shown in FIG2 , based on the control device shown in FIG1 , the embodiment of the present invention adopts an electric vehicle vehicle-cloud collaborative control method, including the following steps:
S101：从云端平台上获取同批次N辆电动汽车在一个运营周期内的充电历史数据和能耗历史数据，所述充电历史数据包括N辆电动汽车在不同的电池电量值、充电电流值和电池温度值下的充电时间，所述能耗历史数据包括N辆电动汽车在不同的环境温度、电池电量值、电池温度值、空调开启情况和路况下的续驶里程；在本发明实施例中，所述充电历史数据和能耗历史数据来源于同批次的5辆电动汽车在100天内的历史数据。S101: Obtain charging history data and energy consumption history data of N electric vehicles from the same batch within an operation cycle from a cloud platform, wherein the charging history data includes the charging time of the N electric vehicles at different battery power values, charging current values, and battery temperature values; and the energy consumption history data includes the driving range of the N electric vehicles at different ambient temperatures, battery power values, battery temperature values, air conditioning activation conditions, and road conditions. In an embodiment of the present invention, the charging history data and energy consumption history data are derived from historical data of 5 electric vehicles from the same batch within 100 days.
S102：对步骤S101获得的充电历史数据，根据不同电池电量值和充电电流值下的充电时间计算第一预估充电时间Ta，根据不同电池电量值和电池温度值的充电时间计算第二预估充电时间Tb，具体方法为：S102: For the charging history data obtained in step S101, a first estimated charging time Ta is calculated according to the charging time at different battery power values and charging current values, and a second estimated charging time Tb is calculated according to the charging time at different battery power values and battery temperature values. The specific method is:
将电池电量值范围[0，100]以固定间隔进行分段，将充电电流值范围[0，Imax]以固定间隔进行分段，其中Imax为最大充电电流；将充电历史数据在不同电池电量值段和充电电流值段内的数据取平均值，作为当前电池电量值段和充电电流值段下的第一预估充电时间Ta。The battery power value range [0, 100] is segmented at fixed intervals, and the charging current value range [0, I max ] is segmented at fixed intervals, where I max is the maximum charging current; the charging history data in different battery power value segments and charging current value segments are averaged as the first estimated charging time T a under the current battery power value segment and charging current value segment.
将电池电量值范围[0，100]以固定间隔进行分段，电池温度值[Tmin，Tmax]以固定间隔进行分段，其中Tmin为动力电池正常工作的最低温度，Tmax为动力电池正常工作的最高温度；将充电历史数据在不同电池电量值段和电池温度值段内的数据取平均值，作为当前电池电量值段和充电电流值段下的第二预估充电时间Tb。The battery power value range [0, 100] is segmented at fixed intervals, and the battery temperature value [T min , T max ] is segmented at fixed intervals, where T min is the lowest temperature for normal operation of the power battery, and T max is the highest temperature for normal operation of the power battery; the charging history data in different battery power value segments and battery temperature value segments are averaged to serve as the second estimated charging time T b under the current battery power value segment and charging current value segment.
由于在车辆在充电过程中，电池电量值(即SOC)、充电电流值和电池温度值为影响充电时间的主要因素，且电池电量值的影响程度更大，因此以电池电量值为首要因素，分别结合充电电流值和电池温度值对充电时间进行分段分析，提高估算精度。通常来说，分段范围越小，估算精度越高，但数据的计算量也越大，因此在本发明实施例中，综合考虑估算精度和计算效率，所述电池电量值范围以5％为间隔进行分段，所述充电电流值范围以5A为间隔进行分段，所述电池温度值以5℃为间隔进行分段。并且所述充电历史数据满足电池电量值越低，充电电流值越小，充电时间越长的规律，充电历史数据中不满足规律的数据则舍弃，不用于计算第一预估充电时间Ta。所述充电历史数据还满足电池电量值越低，电池温度值在适宜充电温度下越低，或者在适宜充电温度上越高，充电时间越长的规律，充电历史数据中不满足规律的数据则舍弃，不用于计算第二预估充电时间Tb。通过对历史数据进行筛选和分段处理，有效减小数据误差，提高计算精度。按照上述步骤进行计算，可得到如表1所示的不同电池电量值段和充电电流值段对应的第一预估充电时间Ta以及如表2所示的不同电池电量值段和电池温度值段对应的第二预估充电时间Tb。表1和表2中的充电电流值范围和电池温度值范围，以及电池适宜充电温度依据具体使用的电池种类进行标定。Since the battery power value (i.e., SOC), charging current value, and battery temperature value are the main factors affecting the charging time during the charging process of the vehicle, and the battery power value has a greater impact, the battery power value is taken as the primary factor, and the charging time is analyzed in segments in combination with the charging current value and the battery temperature value to improve the estimation accuracy. Generally speaking, the smaller the segmentation range, the higher the estimation accuracy, but the larger the amount of data calculation. Therefore, in the embodiment of the present invention, the estimation accuracy and calculation efficiency are comprehensively considered, the battery power value range is segmented at intervals of 5%, the charging current value range is segmented at intervals of 5A, and the battery temperature value is segmented at intervals of 5°C. In addition, the charging history data meets the rule that the lower the battery power value, the smaller the charging current value, and the longer the charging time. Data that does not meet the rule in the charging history data is discarded and is not used to calculate the first estimated charging time Ta . The charging history data also meets the rule that the lower the battery power value, the lower the battery temperature value at the appropriate charging temperature, or the higher the battery temperature at the appropriate charging temperature, the longer the charging time. Data that does not meet the rule in the charging history data is discarded and is not used to calculate the second estimated charging time T b . By screening and segmenting the historical data, the data error is effectively reduced and the calculation accuracy is improved. According to the above steps, the first estimated charging time T a corresponding to different battery power value segments and charging current value segments as shown in Table 1 and the second estimated charging time T b corresponding to different battery power value segments and battery temperature value segments as shown in Table 2 can be obtained. The charging current value range and battery temperature value range in Table 1 and Table 2, as well as the battery suitable charging temperature are calibrated according to the specific battery type used.
表1不同电池电量值段和充电电流值段对应的第一预估充电时间Ta Table 1 The first estimated charging time T a corresponding to different battery power value segments and charging current value segments
表2不同电池电量值段和电池温度值段对应的第二预估充电时间Tb Table 2 The second estimated charging time T b corresponding to different battery power value segments and battery temperature value segments
对步骤S101获得的能耗历史数据，将每个能耗历史数据的环境温度、电池电量值、电池温度值、空调开启情况和路况输入训练好的BP神经网络中，得到与每个能耗历史数据对应的模拟续驶里程S1；本发明实施例采用的BP网络模型示意图如图5所示。For the energy consumption history data obtained in step S101, the ambient temperature, battery power value, battery temperature value, air conditioning start status and road condition of each energy consumption history data are input into the trained BP neural network to obtain the simulated driving range S 1 corresponding to each energy consumption history data; the schematic diagram of the BP network model used in the embodiment of the present invention is shown in FIG5 .
S103：对步骤S101中N辆电动汽车，将每辆电动汽车的每个充电历史数据与对应的第一预估时间Ta和第二预估时间Tb进行比较，分别计算每辆电动汽车的第一预估充电时间准确率和第二预估充电时间准确率；计算每辆电动汽车的第一预估充电时间准确率和第二预估充电时间准确率的具体方法为：S103: For the N electric vehicles in step S101, each charging history data of each electric vehicle is compared with the corresponding first estimated time Ta and second estimated time Tb , and the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle are calculated respectively; the specific method for calculating the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle is:
在一个运营周期内，对单量电动汽车有X个充电历史数据Tr，求出每个充电历史数据对应的第一预估时间Ta和第二预估时间Tb，并根据下列公式计算单量车的单个充电历史数据的准确率：In an operation cycle, there are X charging history data Tr for a single electric vehicle. The first estimated time Ta and the second estimated time Tb corresponding to each charging history data are calculated, and the accuracy of a single charging history data of a single vehicle is calculated according to the following formula:
a1＝1-|Ta-Tr|/Tr a1 ＝1-| Ta - Tr |/ Tr
b1＝1-|Tb-Tr|/Tr b1 ＝1-|Tb -Tr | / Tr
其中，a1为单量车的单个充电历史数据的第一预估充电时间准确率，b1为单量车的单个充电历史数据的第二预估充电时间准确率。Among them, a1 is the first estimated charging time accuracy of the single charging history data of a single vehicle, and b1 is the second estimated charging time accuracy of the single charging history data of a single vehicle.
分别对单量电动汽车X个充电历史数据的第一预估充电时间准确率和第二预估充电时间准确率求平均值，作为每辆电动汽车的第一预估充电时间准确率和第二预估充电时间准确率。对N辆电动汽车的第一预估充电时间准确率求取平均值，得到第一平均准确率a；对N辆电动汽车的第二预估充电时间准确率求取平均值，得到第二平均准确率b。The first estimated charging time accuracy and the second estimated charging time accuracy of the X charging history data of a single electric vehicle are averaged to obtain the first estimated charging time accuracy and the second estimated charging time accuracy of each electric vehicle. The first estimated charging time accuracy of N electric vehicles is averaged to obtain the first average accuracy a; the second estimated charging time accuracy of N electric vehicles is averaged to obtain the second average accuracy b.
将每辆电动汽车的每个能耗历史数据与对应的模拟续驶里程进行比较，分别计算每辆电动汽车的预估续驶里程准确率，计算每辆电动汽车的预估续驶里程准确率的具体方法为：Each energy consumption historical data of each electric vehicle is compared with the corresponding simulated driving range, and the accuracy of the estimated driving range of each electric vehicle is calculated respectively. The specific method for calculating the accuracy of the estimated driving range of each electric vehicle is:
在一个运营周期内，对单量电动汽车有Y个能耗历史数据Sr，求出每个能耗历史数据对应模拟续驶里程S1，并根据下列公式计算单量车的能耗历史数据的准确率：In an operation cycle, there are Y energy consumption historical data S r for a single electric vehicle. The simulated driving range S 1 corresponding to each energy consumption historical data is calculated, and the accuracy of the energy consumption historical data of a single vehicle is calculated according to the following formula:
c1＝1-|S1-Sr|/Sr c 1 =1-|S 1 -S r |/S r
其中，c1为单量车的单个能耗历史数据的准确率。Among them, c 1 is the accuracy of the historical energy consumption data of a single vehicle.
分别对单量电动汽车Y个充电历史数据的的准确率求平均值，作为每辆电动汽车的预估续驶里程准确率。对N辆电动汽车的预估续驶里程准确率求取平均值，得到第三平均准确率c。The accuracy of Y charging history data of a single electric vehicle is averaged to obtain the estimated driving range accuracy of each electric vehicle. The estimated driving range accuracy of N electric vehicles is averaged to obtain a third average accuracy c.
在本发明实施例中，计算得到第一平均准确率a为90％，第二平均准确率b为80％，第三平均准确率c为95％。In the embodiment of the present invention, it is calculated that the first average accuracy rate a is 90%, the second average accuracy rate b is 80%, and the third average accuracy rate c is 95%.
S104：根据第一平均准确率a和第二平均准确率b对第一预估充电时间Ta、第二预估充电时间Tb进行加权计算，得到预估充电时间T；根据第三平均准确率c对模拟续驶里程S1进行加权计算，得到预估续驶里程S；预估充电时间T和预估续驶里程S的计算公式如下：S104: Perform weighted calculation on the first estimated charging time T a and the second estimated charging time T b according to the first average accuracy a and the second average accuracy b to obtain the estimated charging time T; perform weighted calculation on the simulated driving range S 1 according to the third average accuracy c to obtain the estimated driving range S; the calculation formulas for the estimated charging time T and the estimated driving range S are as follows:
T＝a/(a+b)*T1+b/(a+b)*T2 T＝a/(a+b)*T 1 +b/(a+b)*T 2
S＝S1/cS＝S 1 /c
通过上述步骤，即完成了对电动汽车的剩余充电时间和剩余里程的离线估算，在电动汽车运行过程中，可通过电动汽车上电池管理系统BMS获取电池参数，例如电池电量值、充电电流值和电池温度值，通过车辆监控系统获取车辆运行环境参数，例如环境温度、空调开启情况和路况。如图3和图4所示，当车辆处于充电状态下，根据当前的电池参数可得到对应第一预估充电时间Ta、第二预估充电时间Tb，通过第一平均准确率a和第二平均准确率b对第一预估充电时间Ta和第二预估充电时间Tb进行修正，可实现对预估充电时间T的在线计算和实时更新；当车辆处于行驶状态下，根据当前的电池参数和车辆运行环境参数可得到对应模拟续驶里程S1，通过第三平均准确率c对模拟续驶里程S1进行修正，可实现对预估续驶里程S的在线计算和实时更新。本发明实施例可准确预估剩余充电时间和剩余里程，消除司机里程担忧，保障车辆安全可靠运营。Through the above steps, the offline estimation of the remaining charging time and the remaining mileage of the electric vehicle is completed. During the operation of the electric vehicle, the battery parameters, such as the battery power value, the charging current value and the battery temperature value, can be obtained through the battery management system BMS on the electric vehicle, and the vehicle operating environment parameters, such as the ambient temperature, the air conditioner opening status and the road conditions, can be obtained through the vehicle monitoring system. As shown in Figures 3 and 4, when the vehicle is in a charging state, the corresponding first estimated charging time Ta and the second estimated charging time Tb can be obtained according to the current battery parameters, and the first estimated charging time Ta and the second estimated charging time Tb are corrected by the first average accuracy a and the second average accuracy b , so as to realize the online calculation and real-time update of the estimated charging time T; when the vehicle is in a driving state, the corresponding simulated driving range S1 can be obtained according to the current battery parameters and the vehicle operating environment parameters, and the simulated driving range S1 is corrected by the third average accuracy c, so as to realize the online calculation and real-time update of the estimated driving range S. The embodiment of the present invention can accurately estimate the remaining charging time and the remaining mileage, eliminate the driver's mileage concerns, and ensure the safe and reliable operation of the vehicle.
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
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