Method, device, equipment and storage medium for determining remaining cruising range of new energy vehicles

Abstract

The present invention discloses a method, device, equipment and storage medium for determining the remaining cruising range of a new energy vehicle, wherein the method for determining the remaining cruising range of a new energy vehicle comprises the following steps: obtaining monitoring data of a new energy vehicle collected by a big data platform, wherein the monitoring data comprises vehicle driving status data and vehicle battery basic data; calculating the battery health SOH, actual driving mileage within the state of charge SOC interval and power consumption within the SOC interval of the new energy vehicle under various working conditions according to the monitoring data; generating reference working condition data under various working conditions according to the monitoring data, battery SOH, actual driving mileage within the SOC interval and power consumption within the SOC interval; matching the corresponding reference working condition data according to the real-time working condition data of the new energy vehicle to be tested, so as to determine the remaining cruising range of the new energy vehicle to be tested. The present invention can improve the prediction accuracy of the remaining cruising range of the new energy vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a method, device, equipment and storage medium for determining the remaining cruising range of a new energy vehicle. Background Art

[0002] In recent years, the scale of the new energy vehicle market has continued to expand, and more and more users have chosen new energy vehicles as their means of transportation. As a result, more and more users have complained that the remaining range of new energy vehicles is inaccurately displayed, further increasing range anxiety.

[0003] In order to solve the above problems, some technical solutions in the prior art provide suggestions for determining the remaining range of new energy vehicles based on the current capacity, current power consumption and SOH (state of health) of the vehicle battery.

[0004] Among them, one solution is: when the battery status and discharge voltage are relatively stable, the real-time remaining capacity is calculated through SOH, battery rated capacity and real-time battery current, and then the capacity consumption is calculated by integrating the real-time battery current and vehicle speed, and then the cruising range of the electric vehicle is calculated.

[0005] However, the above-mentioned remaining mileage estimation method of electric vehicles based on capacity consumption is based on the premise that the battery state and discharge voltage are relatively stable. But in actual situations, batteries of different manufacturers, different models, different years of use, and different remaining power levels cannot be continuously guaranteed to be stable in real time under different environmental (weather, road, etc.) conditions.

[0006] Another solution is: by obtaining the remaining battery power of the current vehicle's power battery and determining the current power consumption information of the vehicle; estimating the first drivable time of the current vehicle based on the remaining battery power and the current power consumption information; determining the remaining mileage of the current vehicle based on the first drivable time and providing a prompt.

[0007] However, the premise of the above-mentioned method for determining the remaining mileage of new energy vehicles is that the remaining power value of the current vehicle power battery is accurate. But in actual situations, the remaining battery power is often inflated, and there may be sudden power outages. In this case, the remaining cruising range output by the above method will also suddenly decrease, causing mileage anxiety for users. Summary of the invention

[0008] In view of the defects existing in the prior art, a first aspect of the present invention provides a method for determining the remaining cruising range of a new energy vehicle, which can improve the prediction accuracy of the remaining cruising range of the new energy vehicle.

[0009] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0010] A method for determining the remaining cruising range of a new energy vehicle, the method comprising the following steps:

[0011] Acquire monitoring data of new energy vehicles collected by the big data platform, wherein the monitoring data includes vehicle driving status data and vehicle battery basic data;

[0012] Calculate the battery health SOH, actual driving mileage within the state of charge SOC range, and power consumption within the SOC range of the new energy vehicle under various working conditions according to the monitoring data;

[0013] Generate benchmark operating condition data under various operating conditions based on the monitoring data, battery SOH, actual mileage within the SOC range, and power consumption within the SOC range;

[0014] According to the real-time operating condition data of the new energy vehicle to be tested, the corresponding benchmark operating condition data is matched to determine the remaining cruising range of the new energy vehicle to be tested.

[0015] In some embodiments, the calculation of the battery health SOH, the actual mileage within the state of charge SOC interval, and the power consumption within the SOC interval of the new energy vehicle under various working conditions according to the monitoring data includes:

[0016] Divide the charging trip and the driving trip according to the vehicle driving status data in the monitoring data, wherein the charging trip is the process from when the vehicle starts charging to when the vehicle stops charging, and the driving trip is the process from when the vehicle starts ignition to when the vehicle stops ignition;

[0017] Calculate the battery SOH under various conditions based on a charging trip time;

[0018] In the corresponding driving trip, the actual driving mileage within the SOC interval is determined according to the total mileage difference in the vehicle driving state data corresponding to two consecutive SOC jumps;

[0019] The power consumption within the SOC interval is determined based on the ratio of the total charging capacity of the charging trip to the actual mileage within the SOC interval.

[0020] In some embodiments, the calculation of the battery SOH under various operating conditions based on a charging trip time includes:

[0021] According to the formula Calculate the total charging capacity Q for each charging trip, where t is the charging trip time, U is the total voltage, and I is the total current;

[0022] Calculate the difference between the end-of-charge SOC and the start-of-charge SOC according to the formula: Q 当前 =Q / (SOC 结束 –SOC 起始)*100, calculate the full charge capacity Q of the battery 当前 ;

[0023] According to the formula: SOH = Q 当前 / Q 额定 *100%, calculate battery SOH.

[0024] In some embodiments, the base operating condition data under various operating conditions is generated according to the monitoring data, the battery SOH, the actual mileage within the SOC interval, and the power consumption within the SOC interval, including:

[0025] The driving distance data is associated with the basic battery data of the vehicle through the vehicle VIN number;

[0026] According to the longitude and latitude in the driving itinerary data, the region corresponding to the longitude and latitude and the corresponding driving road grade are obtained;

[0027] The benchmark operating condition data is generated based on the driving distance data, vehicle battery basic data, region, road grade, battery SOH, SOC range, actual mileage within the SOC range and power consumption within the SOC range.

[0028] In some embodiments, it also includes:

[0029] According to the charging journey and driving journey time, the most recent charging type before the start of each driving journey is determined;

[0030] The benchmark operating condition data is generated based on the driving trip data, vehicle battery basic data, region, road grade, battery SOH, the type of last charge, SOC range, actual mileage within the SOC range, and power consumption within the SOC range.

[0031] In some embodiments, the matching of the corresponding benchmark operating condition data according to the real-time operating condition data of the new energy vehicle to be tested to determine the remaining cruising range of the new energy vehicle to be tested includes:

[0032] Perform matching search based on the driving distance data, vehicle battery basic data, region, road grade, battery SOH, and the last charging type of the new energy vehicle to be tested;

[0033] Determine the included SOC range according to the remaining SOC of the new energy vehicle to be tested;

[0034] According to the search results and the real-time power consumption of the new energy vehicle to be tested, determine the benchmark operating condition data closest to the real-time power consumption in each SOC interval;

[0035] The actual mileage within the SOC range in all benchmark operating condition data is added together to obtain the remaining cruising range of the new energy vehicle to be tested.

[0036] In some embodiments, dividing the charging trip and the driving trip according to the vehicle driving status data in the monitoring data includes:

[0037] Set a charging trip message, collect and mark the charging status at a preset frequency, and select the message marked as the charging status as the charging trip;

[0038] Set the driving itinerary message, collect and mark the vehicle status at the preset frequency, and select the one marked as the driving status as the driving itinerary.

[0039] A second aspect of the present invention provides a device for determining the remaining cruising range of a new energy vehicle, which can improve the prediction accuracy of the remaining cruising range of the new energy vehicle.

[0040] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0041] A device for determining the remaining cruising range of a new energy vehicle, comprising:

[0042] A data acquisition module, which is used to acquire monitoring data of new energy vehicles collected by the big data platform, wherein the monitoring data includes vehicle driving status data and vehicle battery basic data;

[0043] A calculation module, which calculates the actual driving mileage within the battery health SOH and state of charge SOC range of the new energy vehicle under various working conditions according to the monitoring data;

[0044] A working condition generation module, which generates reference working condition data under various working conditions according to the monitoring data, the battery SOH and the actual driving mileage within the state of charge SOC interval;

[0045] The operating condition matching module matches the corresponding benchmark operating condition data according to the real-time operating condition data of the new energy vehicle to be tested, so as to determine the remaining cruising range of the new energy vehicle to be tested.

[0046] A third aspect of the present invention provides a device that can improve the prediction accuracy of the remaining cruising range of a new energy vehicle.

[0047] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0048] A device comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned method for determining the remaining cruising range of a new energy vehicle are implemented.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium that can improve the prediction accuracy of the remaining cruising range of a new energy vehicle.

[0050] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0051] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for determining the remaining cruising range of a new energy vehicle are implemented.

[0052] Compared with the prior art, the advantages of the present invention are:

[0053] The method for determining the remaining cruising range of a new energy vehicle in the present invention predicts the remaining cruising range of the vehicle under similar operating conditions based on the actual remaining cruising range of the actual vehicle under different operating conditions, thereby avoiding the limitation of relying on a stable battery state and discharge voltage, and at the same time avoiding the influence of an inflated battery charge on the prediction of the remaining cruising range. The method for determining the remaining cruising range of a new energy vehicle provided by the method in the present invention can improve the prediction accuracy of the remaining cruising range of the new energy vehicle, effectively alleviate the mileage anxiety of users caused by sudden power failure, and improve the user experience. At the same time, the method in the present invention can continuously improve the prediction accuracy by repeatedly iterating and optimizing through continuously enriching the input data of the operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of a method for determining the remaining cruising range of a new energy vehicle in an embodiment of the present invention;

[0055] Figure 2 is a flow chart of step S2 in an embodiment of the present invention;

[0056] Figure 3 is a flow chart of step S4 in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0058] See also Figure 1 As shown, an embodiment of the present invention discloses a method for determining the remaining cruising range of a new energy vehicle, the method comprising the following steps:

[0059] S1. Acquire monitoring data of new energy vehicles collected by a big data platform, wherein the monitoring data includes vehicle driving status data and vehicle battery basic data.

[0060] In this embodiment, the new energy vehicle monitoring data uploaded by T-BOX is accessed through the big data platform, wherein the new energy vehicle monitoring data has undergone data cleaning processes such as analysis, standardization, and outlier processing.

[0061] The monitoring data mainly includes vehicle driving status data and vehicle battery basic data, among which the vehicle driving status data includes but is not limited to total mileage, air conditioning status, ambient temperature, vehicle speed, charging status, charging type (fast charging / slow charging), vehicle status, latitude and longitude, SOC, total current, total voltage, message collection time, etc.

[0062] The basic data of vehicle batteries include but are not limited to vehicle model, battery model, battery rated capacity, and battery service life. The data coverage should include data on the complete battery life cycle of various vehicle models and battery models.

[0063] S2. Calculate the battery health state SOH, actual mileage within the state of charge SOC range, and power consumption within the SOC range of the new energy vehicle under various working conditions based on the monitoring data.

[0064] In this embodiment, see Figure 2 As shown, step S2 specifically includes:

[0065] S21. Divide the charging trip and the driving trip according to the vehicle driving status data in the monitoring data, wherein the charging trip is the process from when the vehicle starts charging to when the vehicle stops charging, and the driving trip is the process from when the vehicle is ignited and started to when the vehicle is turned off and stopped.

[0066] It is worth noting that the driving trip refers to the process from the vehicle ignition start to the vehicle shutdown and end. In this embodiment, the driving trip message is set to collect and identify the vehicle status at a preset frequency. The message collection frequency can be set to no less than 10 seconds. The vehicle status can be represented by 0 and 1. When the vehicle status is 1, it means that it is in the driving state. Then the vehicle status of 1 can be selected as the driving trip. In addition, when the time interval between two received messages with the vehicle status of 1 exceeds the set time, it is classified as a new driving trip.

[0067] It is understandable that when the vehicle is turned off, the vehicle state is 0, indicating that a journey has ended. When the vehicle is waiting for a red light or temporarily stopped, the vehicle state is in a non-0 or 1 state when the vehicle is not turned off. If this state lasts too long, such as 15 minutes, it is also classified as a new travel journey.

[0068] The charging trip refers to the process from plugging in the vehicle to start charging to unplugging the vehicle to stop charging. In this embodiment, a charging trip message is set to collect and mark the charging status at a preset frequency. The message collection frequency can be set to no less than 10 seconds. The charging status can be represented by 0 and 1. When the charging status is 1, it means that it is charging. Then the charging status of 1 can be selected as the driving trip. In addition, when the time interval between two messages with the charging status of 1 exceeds the set time, it is divided into a new charging trip.

[0069] It is understandable that when the gun is unplugged and charging stops, the charging state is 0, indicating that one charging is completed. When disturbed or affected by other factors, the charging state is in a state other than 0 or 1. If this state lasts for too long, such as 15 minutes, it is also classified as a new charging trip.

[0070] S22. Calculate the battery SOH under various operating conditions based on a charging trip time.

[0071] In this embodiment, firstly according to the formula Calculate the total charge capacity Q for each charging trip, where t is the charging trip time, U is the total voltage, and I is the total current.

[0072] Then calculate the difference between the SOC at the end of charging and the SOC at the start of charging, according to the formula: Q 当前 =Q / (SOC 结束 –SOC 起始 )*100, calculate the full charge capacity Q of the battery 当前 .

[0073] It is worth noting that you can get the SOC of the past month. 结束 –SOC 起始 Calculate Q for the case with the largest span 当前 , where in SOC 结束 When the value is 99%, 99 is used for calculation.

[0074] Finally, according to the formula: SOH = Q 当前 / Q 额定 *100%, calculate battery SOH.

[0075] S23. In the corresponding driving journey, the actual driving mileage within the SOC interval is determined according to the total mileage difference in the vehicle driving state data corresponding to two consecutive SOC jumps.

[0076] It is worth noting that, in this embodiment, the SOC interval refers to the battery power interval, such as 49% to 50%, and the SOC jump refers to a jump from 50% to 49%. In this way, two consecutive SOC jumps refer to a jump from 50% to 49%, and then from 49% to 48%.

[0077] Therefore, the actual mileage within the SOC range can be expressed as:

[0078] ΔSOC Actual mileage = Total mileage 第一次SOC跳变 - Total mileage 第二次SOC跳变 .

[0079] S24. Determine the power consumption within the SOC interval according to the ratio of the total charging capacity of the charging trip to the actual driving mileage within the SOC interval.

[0080] It can be understood that the actual mileage within the SOC range is also an increment of the total mileage, that is, ΔSOC actual mileage = Δtotal mileage.

[0081] Therefore, the power consumption in the SOC range can be expressed as:

[0082]

[0083] S3. Generate benchmark operating condition data under various operating conditions based on the monitoring data, battery SOH, actual mileage within the SOC range, and power consumption within the SOC range.

[0084] Specifically, the driving trip data is associated with the vehicle battery basic data through the vehicle VIN number in the monitoring data.

[0085] In this embodiment, the driving trip data is part of the vehicle driving status data, which mainly includes total mileage, air conditioning status, ambient temperature, vehicle speed, longitude and latitude, and charging type (fast charging / slow charging) data.

[0086] According to the longitude and latitude in the driving itinerary data, the region corresponding to the longitude and latitude and the corresponding driving road grade are obtained.

[0087] The benchmark operating condition data is generated based on the driving distance data, vehicle battery basic data, region, road grade, battery SOH, SOC range, actual mileage within the SOC range and power consumption within the SOC range.

[0088] In some embodiments, in order to more accurately estimate the remaining battery life, the charging type during the charging trip is also taken into account, that is, whether fast charging or slow charging is used.

[0089] Therefore, the most recent charging type before the start of each driving journey is also determined through the charging journey and driving journey time.

[0090] The benchmark operating condition data is generated based on the driving trip data, vehicle battery basic data, region, road grade, battery SOH, the type of last charge, SOC range, actual mileage within the SOC range, and power consumption within the SOC range.

[0091] It can be understood that based on the above description, the basic data of vehicle batteries mainly include vehicle model, battery model, battery rated capacity, and battery service life; then the total mileage, air conditioning status, ambient temperature, vehicle speed, and charging type (fast charging / slow charging) data in the vehicle driving status data are selected; combined with the corresponding region determined by longitude and latitude and the corresponding road grade; based on the calculated battery SOH, SOC interval, actual mileage within the SOC interval, and power consumption within the SOC interval (actual power consumption, measured per 100km), the following benchmark operating condition data table 1 is generated:

[0092] Table 1: Baseline operating data

[0093]

[0094] It can be understood that the prediction accuracy of the data in Table 1 can be continuously improved by continuously enriching the working condition input data and repeatedly iterating and optimizing.

[0095] S4. According to the real-time operating condition data of the new energy vehicle to be tested, the corresponding benchmark operating condition data is matched to determine the remaining cruising range of the new energy vehicle to be tested.

[0096] In this embodiment, see Figure 3 As shown, step S4 includes:

[0097] S41. Perform a matching search based on the driving distance data of the new energy vehicle to be tested, the basic data of the vehicle battery, the region, the road grade, the battery SOH, and the most recent charging type.

[0098] S42. Determine the included SOC range according to the remaining SOC of the new energy vehicle to be tested.

[0099] S43. According to the search results and the real-time power consumption of the new energy vehicle to be tested, determine the benchmark operating condition data closest to the real-time power consumption in each SOC interval.

[0100] S44. Add up the actual mileage within the SOC range in all benchmark operating condition data to obtain the remaining cruising range of the new energy vehicle to be tested.

[0101] Specifically, for vehicles that need to predict the remaining mileage, obtain the basic data of the vehicle: VIN number, vehicle model, battery model, battery rated capacity, battery age, and vehicle driving status data of the vehicle: total mileage, longitude and latitude, speed, SOC, total current, total voltage, and refer to the above content to calculate the real-time location (region), road grade, the last charging type, SOH, real-time power consumption, and obtain the real-time working condition data, as shown in Table 2:

[0102] Table 2: Real-time operating data

[0103]

[0104] By comparing the real-time working condition data with the benchmark working condition data in the big data platform, the matching rules can be specified as follows in the specific implementation:

[0105] ① The same vehicle model, battery model, battery rated capacity, region, road grade, charging type, and air-conditioning status are grouped together; ② The battery life is grouped according to 1 month (first quarter) as a range; ③ The total mileage is grouped according to 10,000 kilometers as a range; ④ The average speed is grouped according to 5Km / h as a range; ⑤ SOH is grouped according to 1% as a range; ⑥ The ambient temperature is grouped according to 10 degrees Celsius as a range. All benchmark data under the same group are obtained; ⑦ SOC is grouped according to 1% as a range.

[0106] From all the benchmark data in the same group, select the benchmark data with the closest real-time power consumption in the corresponding SOC interval (for example, 49% to 50%, 48% to 49%, and so on, all the way to 0% to 1%), and sum the ΔSOC actual mileage in each benchmark data to get the remaining cruising range of the vehicle. If the SOC interval cannot be matched, calculate according to the closest SOC interval.

[0107] To sum up, the method for determining the remaining cruising range of a new energy vehicle in the present invention predicts the remaining cruising range of the vehicle under similar operating conditions based on the actual remaining cruising range of the actual vehicle under different operating conditions, avoiding the limitations of relying on a stable battery state and discharge voltage, and avoiding the influence of an inflated battery charge on the prediction of the remaining cruising range. The method for determining the remaining cruising range of a new energy vehicle provided by the method in the present invention can improve the accuracy of prediction of the remaining cruising range of the new energy vehicle, effectively alleviate the user's mileage anxiety caused by sudden power failure, and improve the user experience. At the same time, the method in the present invention can continuously improve the prediction accuracy by continuously enriching the operating condition input data and repeatedly iterating and optimizing.

[0108] An embodiment of the present invention also provides a device for determining the remaining cruising range of a new energy vehicle, which includes a data acquisition module, a calculation module, a working condition generation module and a working condition matching module.

[0109] Among them, the data acquisition module is used to obtain the monitoring data of new energy vehicles collected by the big data platform, and the monitoring data includes vehicle driving status data and vehicle battery basic data. The calculation module calculates the battery health SOH and the actual mileage within the state of charge SOC range of the new energy vehicle under various working conditions based on the monitoring data. The working condition generation module generates benchmark working condition data under various working conditions based on the monitoring data, battery SOH and the actual mileage within the state of charge SOC range. The working condition matching module matches the corresponding benchmark working condition data according to the real-time working condition data of the new energy vehicle to be tested to determine the remaining cruising range of the new energy vehicle to be tested.

[0110] In some embodiments, the calculation module calculates the battery health SOH, the actual mileage within the state of charge SOC interval, and the power consumption within the SOC interval of the new energy vehicle under various working conditions according to the monitoring data, including:

[0111] Divide the charging trip and the driving trip according to the vehicle driving status data in the monitoring data, wherein the charging trip is the process from when the vehicle starts charging to when the vehicle stops charging, and the driving trip is the process from when the vehicle starts ignition to when the vehicle stops ignition;

[0112] Calculate the battery SOH under various conditions based on a charging trip time;

[0113] In the corresponding driving trip, the actual driving mileage within the SOC interval is determined according to the total mileage difference in the vehicle driving state data corresponding to two consecutive SOC jumps;

[0114] The power consumption within the SOC interval is determined based on the ratio of the total charging capacity of the charging trip to the actual mileage within the SOC interval.

[0115] In some embodiments, the calculation module calculates the battery SOH under various working conditions based on a charging trip time, including:

[0116] According to the formula Calculate the total charging capacity Q for each charging trip, where t is the charging trip time, U is the total voltage, and I is the total current;

[0117] Calculate the difference between the end-of-charge SOC and the start-of-charge SOC according to the formula: Q 当前 =Q / (SOC 结束 –SOC 起始 )*100, calculate the full charge capacity Q of the battery 当前 ;

[0118] According to the formula: SOH = Q 当前 / Q 额定 *100%, calculate battery SOH.

[0119] In some embodiments, the operating condition generation module generates reference operating condition data under various operating conditions according to the monitoring data, battery SOH, actual mileage within the SOC interval, and power consumption within the SOC interval, including:

[0120] The driving distance data is associated with the basic battery data of the vehicle through the vehicle VIN number;

[0121] According to the longitude and latitude in the driving itinerary data, the region corresponding to the longitude and latitude and the corresponding driving road grade are obtained;

[0122] The benchmark operating condition data is generated based on the driving distance data, vehicle battery basic data, region, road grade, battery SOH, SOC range, actual mileage within the SOC range and power consumption within the SOC range.

[0123] In some embodiments, the operating condition generation module is further used to:

[0124] According to the charging journey and driving journey time, the most recent charging type before the start of each driving journey is determined;

[0125] The benchmark operating condition data is generated based on the driving trip data, vehicle battery basic data, region, road grade, battery SOH, the type of last charge, SOC range, actual mileage within the SOC range, and power consumption within the SOC range.

[0126] In some embodiments, the operating condition matching module matches the corresponding benchmark operating condition data according to the real-time operating condition data of the new energy vehicle to be tested, so as to determine the remaining cruising range of the new energy vehicle to be tested, including:

[0127] Perform matching search based on the driving distance data, vehicle battery basic data, region, road grade, battery SOH, and the last charging type of the new energy vehicle to be tested;

[0128] Determine the included SOC range according to the remaining SOC of the new energy vehicle to be tested;

[0129] According to the search results and the real-time power consumption of the new energy vehicle to be tested, determine the benchmark operating condition data closest to the real-time power consumption in each SOC interval;

[0130] The actual mileage within the SOC range in all benchmark operating condition data is added together to obtain the remaining cruising range of the new energy vehicle to be tested.

[0131] In some embodiments, the calculation module divides the charging trip and the driving trip according to the vehicle driving status data in the monitoring data, including:

[0132] Set a charging trip message, collect and mark the charging status at a preset frequency, and select the message marked as the charging status as the charging trip;

[0133] Set the driving itinerary message, collect and mark the vehicle status at the preset frequency, and select the one marked as the driving status as the driving itinerary.

[0134] An embodiment of the present invention also provides a device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method for determining the remaining cruising range of the new energy vehicle described above are implemented.

[0135] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0136] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for determining the remaining cruising range of a new energy vehicle are implemented.

[0137] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer-readable storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).

[0138] As is known to those of ordinary skill in the art, the term computer-readable storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.

[0139] Exemplarily, the computer-readable storage medium may be an internal storage unit of the electronic device of the aforementioned embodiment, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device.

[0140] The above are only specific implementations of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the embodiments of the present invention, and these modifications or replacements should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for determining the remaining cruising range of a new energy vehicle, characterized in that: The method comprises the following steps: Acquire monitoring data of new energy vehicles collected by the big data platform, wherein the monitoring data includes vehicle driving status data and vehicle battery basic data; Calculate the battery health SOH, actual driving mileage within the state of charge SOC range, and power consumption within the SOC range of the new energy vehicle under various working conditions according to the monitoring data; Generate benchmark operating condition data under various operating conditions based on the monitoring data, battery SOH, actual mileage within the SOC range, and power consumption within the SOC range; According to the real-time operating condition data of the new energy vehicle to be tested, the corresponding benchmark operating condition data is matched to determine the remaining cruising range of the new energy vehicle to be tested; The calculation of the battery health SOH, the actual driving mileage within the state of charge SOC range and the power consumption within the SOC range of the new energy vehicle under various working conditions according to the monitoring data includes: Divide the charging trip and the driving trip according to the vehicle driving status data in the monitoring data, wherein the charging trip is the process from when the vehicle starts charging to when the vehicle stops charging, and the driving trip is the process from when the vehicle starts ignition to when the vehicle stops ignition; Calculate the battery SOH under various conditions based on a charging trip time; In the corresponding driving trip, the actual driving mileage within the SOC interval is determined according to the total mileage difference in the vehicle driving state data corresponding to two consecutive SOC jumps; Determine the power consumption within the SOC interval according to the ratio of the total charging capacity of the charging trip to the actual driving mileage within the SOC interval; The calculation of the battery SOH under various working conditions based on a charging trip time includes: According to the formula Q= , calculate the total charging capacity Q of each charging trip, where t is the charging trip time, U is the total voltage, and I is the total current; Calculate the difference between the end-of-charge SOC and the start-of-charge SOC according to the formula: Q 当前 = Q / (SOC 结束 –SOC 起始 ) * 100, calculate the full charge capacity Q of the battery 当前 ; According to the formula: SOH =Q 当前 / Q 额定 * 100%, calculate battery SOH; The base operating condition data under various operating conditions are generated according to the monitoring data, battery SOH, actual mileage within the SOC interval, and power consumption within the SOC interval, including: The driving distance data is associated with the basic battery data of the vehicle through the vehicle VIN number; According to the longitude and latitude in the driving itinerary data, the region corresponding to the longitude and latitude and the corresponding driving road grade are obtained; Generate benchmark operating condition data based on driving trip data, vehicle battery basic data, region, road grade, battery SOH, SOC range, actual driving mileage within the SOC range, and power consumption within the SOC range; According to the charging journey and driving journey time, the most recent charging type before the start of each driving journey is determined; Generate benchmark operating condition data based on driving trip data, vehicle battery basic data, region, road grade, battery SOH, last charge type, SOC range, actual driving mileage within the SOC range, and power consumption within the SOC range; The method of matching the corresponding benchmark operating condition data according to the real-time operating condition data of the new energy vehicle to be tested to determine the remaining cruising range of the new energy vehicle to be tested includes: Perform matching search based on the driving distance data, vehicle battery basic data, region, road grade, battery SOH, and the last charging type of the new energy vehicle to be tested; Determine the included SOC range according to the remaining SOC of the new energy vehicle to be tested; According to the search results and the real-time power consumption of the new energy vehicle to be tested, determine the benchmark operating condition data closest to the real-time power consumption in each SOC interval; The actual mileage within the SOC range in all benchmark operating condition data is added together to obtain the remaining cruising range of the new energy vehicle to be tested.

2. A method for determining the remaining cruising range of a new energy vehicle according to claim 1, characterized in that: The dividing of the charging journey and the driving journey according to the vehicle driving status data in the monitoring data includes: Set a charging trip message, collect and mark the charging status at a preset frequency, and select the message marked as the charging status as the charging trip; Set the driving itinerary message, collect and mark the vehicle status at the preset frequency, and select the one marked as the driving status as the driving itinerary.

3. A device for determining the remaining cruising range of a new energy vehicle, characterized in that: The steps of implementing a method for determining the remaining cruising range of a new energy vehicle as claimed in any one of claims 1 to 2 include: A data acquisition module, which is used to acquire monitoring data of new energy vehicles collected by the big data platform, wherein the monitoring data includes vehicle driving status data and vehicle battery basic data; A calculation module, which calculates the actual driving mileage within the battery health SOH and state of charge SOC range of the new energy vehicle under various working conditions according to the monitoring data; A working condition generation module, which generates reference working condition data under various working conditions according to the monitoring data, the battery SOH and the actual driving mileage within the state of charge SOC interval; The operating condition matching module matches the corresponding benchmark operating condition data according to the real-time operating condition data of the new energy vehicle to be tested, so as to determine the remaining cruising range of the new energy vehicle to be tested.

4. A device for determining the remaining cruising range of a new energy vehicle, characterized in that: The device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a method for determining the remaining cruising range of a new energy vehicle as described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of a method for determining the remaining cruising range of a new energy vehicle as described in any one of claims 1 to 2 are implemented.

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