Method for determining remaining cruising range, electronic device, and vehicle

Abstract

The present application provides a method for determining the remaining cruising range, an electronic device, and a vehicle. The method can collect vehicle operating parameters according to a preset first sampling interval, and determine the operating condition ratio of each driving condition based on the vehicle operating parameters and a preset power consumption statistics table; collect vehicle power consumption parameters according to a preset second sampling interval, and determine the average energy consumption within the historical mileage based on the operating condition ratio and the vehicle power consumption parameters; wherein the first sampling interval is greater than the second sampling interval; and determine and display the remaining cruising range based on the current battery remaining power and the average energy consumption. By introducing a probabilistic statistical method into the calculation process of average energy consumption by determining each driving condition, the user's vehicle usage habits can be better determined, and the calculation of average energy consumption can be optimized, thereby improving the accuracy of average energy consumption, thereby improving the accuracy of the remaining cruising range, reducing the user's mileage anxiety, and improving the user's vehicle experience.

Description

Technical Field

[0001] The present application relates to the technical field, and in particular to a method for determining remaining cruising range, an electronic device, and a vehicle. Background Art

[0002] The accuracy of the remaining range of pure electric new energy vehicles is often a focus of great concern to users. If the remaining range is not accurate enough, it will cause mileage anxiety for users, especially when the battery is low. Users often struggle with whether to use the car normally or find a charging station to charge it as soon as possible, which brings a bad driving experience to users. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method for determining the remaining cruising range, an electronic device and a vehicle, so as to improve the accuracy of the remaining cruising range.

[0004] Based on the above objectives, this application provides a method for determining the remaining cruising range, including:

[0005] Collecting vehicle operating parameters according to a preset first sampling interval, and determining a working condition proportion of each driving condition based on the vehicle operating parameters and a preset power consumption statistical table;

[0006] collecting vehicle power consumption parameters according to a preset second sampling interval, and determining average energy consumption within historical mileage based on the operating condition proportion and the vehicle power consumption parameters; wherein the first sampling interval is greater than the second sampling interval;

[0007] The remaining cruising range is determined and displayed based on the current remaining battery power and the average energy consumption.

[0008] Based on the same inventive concept, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the above-mentioned method when executing the computer program.

[0009] Optionally, determining the operating condition proportion of each driving condition according to the vehicle operating parameters and a preset power consumption statistics table includes:

[0010] Determining a target statistical parameter corresponding to the vehicle operating parameter in the power consumption statistical table, and performing a cumulative counting on the target statistical parameter;

[0011] Determining statistical parameters for each driving condition in the power consumption statistical table, and determining the sum of all statistical parameters as a statistical total;

[0012] The ratio of each statistical parameter to the statistical total is respectively determined as the operating condition proportion of the corresponding driving condition.

[0013] By introducing statistics into the calculation process of average energy consumption and determining the proportion of working conditions, the user's driving habits can be transformed into a statistical probability statistics method. As the driving mileage increases, the statistical results will become closer and closer to the user's driving habits, thereby improving the accuracy of the calculation of average energy consumption.

[0014] Optionally, the vehicle operating parameters include monitored vehicle speed, monitored torque, and battery power consumption; and determining target statistical parameters corresponding to the vehicle operating parameters in the power consumption statistical table includes:

[0015] determining, in the power consumption statistical table, a target vehicle speed interval item corresponding to the monitored vehicle speed and a target torque interval item corresponding to the monitored torque;

[0016] In the power consumption statistics table, determining a parameter selection range according to the target torque range item and the target vehicle speed range item, and determining a target power range item corresponding to the battery consumption power within the parameter selection range;

[0017] The statistical parameter corresponding to the target power interval item in the parameter selection range is determined as the target statistical parameter.

[0018] The discrete values ​​within a certain range are uniformly divided through the parameter selection range to reduce the proportion of transient values ​​and amplify the ratio of sampling points in the steady-state part to achieve the purpose of improving calculation accuracy.

[0019] Optionally, the battery power consumption includes low-voltage power consumption and high-voltage power consumption; and determining a target power interval item corresponding to the battery power consumption within the parameter selection range includes:

[0020] Determining a target low-voltage interval item corresponding to the low-voltage power consumption and a target high-voltage interval item corresponding to the high-voltage power consumption within the parameter selection range;

[0021] The search item corresponding to the target high-voltage interval item and the target low-voltage interval item in the parameter selection range is determined as the target power interval item.

[0022] By separating the power consumption of the low-voltage system and the high-voltage system, the accuracy of the statistical data is further improved, which in turn improves the accuracy of the calculation of average energy consumption and achieves accurate calculation of remaining range. The more content the statistical table is divided into, the more accurate the calculation results will be.

[0023] Optionally, determining the average energy consumption within the historical mileage according to the operating condition proportion and the vehicle power consumption parameter includes:

[0024] Determining the total energy consumption within the historical mileage according to the vehicle power consumption parameters and the operating condition ratio;

[0025] The ratio of the total energy consumption to the historical mileage is determined as the average energy consumption.

[0026] Using historical mileage for calculation has more data volume, which can improve the accuracy of the corresponding average energy consumption calculation.

[0027] Optionally, the vehicle power consumption parameters include battery current and battery voltage; and determining the total energy consumption within the historical mileage based on the vehicle power consumption parameters and the operating condition proportion includes:

[0028] Performing a time-based integral calculation on the product of the battery current and the battery voltage to obtain a battery calculated energy consumption;

[0029] Determining a target operating condition ratio corresponding to the battery calculated energy consumption, and performing a weighted average calculation on the battery calculated energy consumption and the corresponding target operating condition ratio to obtain a weighted average energy consumption;

[0030] Determine the number of collected samples according to the historical mileage and the second sampling interval;

[0031] The product of the weighted average energy consumption and the number of collected samples is determined as the total energy consumption.

[0032] The weighted average calculation is used to eliminate the influence of interference data and improve the accuracy of average energy consumption and total energy consumption.

[0033] Optionally, determining the target operating condition ratio corresponding to the calculated battery energy consumption includes:

[0034] Determining a target proportional power interval including the battery calculated energy consumption from a plurality of preset proportional power intervals;

[0035] The operating condition proportion corresponding to the target proportion power interval is determined as the target operating condition proportion.

[0036] While ensuring the accuracy of calculations, it is necessary to ensure the efficiency of the calculation process. By reducing the amount of data calculations and improving the calculation efficiency, the power interval can be used.

[0037] Optionally, determining the number of collected samples according to the historical mileage and the second sampling interval includes:

[0038] Determine the driving duration of the historical mileage;

[0039] The ratio of the driving duration to the second sampling interval is determined as the number of collected samples.

[0040] When collecting data, some data that exceeds the boundary may need to be eliminated. The elimination operation may cause the number of collected samples to change. The number of collected samples calculated based on the driving time and the second sampling interval will not be affected by data elimination, thereby improving the accuracy of the calculation of total energy consumption.

[0041] Based on the same inventive concept, the present disclosure also provides a vehicle, comprising the electronic device as described above.

[0042] As can be seen from the above, the method for determining the remaining cruising range, the electronic device, and the vehicle provided in this application can collect vehicle operating parameters according to a preset first sampling interval, and determine the proportion of each driving condition based on the vehicle operating parameters and a preset power consumption statistics table; collect vehicle power consumption parameters according to a preset second sampling interval, and determine the average energy consumption within the historical mileage based on the proportion of the operating conditions and the vehicle power consumption parameters; wherein the first sampling interval is greater than the second sampling interval; and determine and display the remaining cruising range based on the current remaining battery power and the average energy consumption. By introducing a probabilistic statistical method into the calculation process of average energy consumption by determining each driving condition, the user's car usage habits can be better determined, the calculation of average energy consumption can be optimized, the accuracy of average energy consumption can be improved, and the accuracy of the remaining cruising range can be improved, thereby reducing the user's mileage anxiety and improving the user's car experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 This is a flowchart of a method for determining the remaining cruising range according to an embodiment of the present application;

[0045] Figure 2 A flowchart for determining the operating condition ratio of each driving condition in an embodiment of the present application;

[0046] Figure 3 A flowchart of determining a target power interval item corresponding to battery consumption power according to an embodiment of the present application;

[0047] Figure 4 Flowchart for determining average energy consumption within historical mileage for an embodiment of the present application;

[0048] Figure 5 Flowchart for determining total energy consumption within historical mileage for an embodiment of the present application;

[0049] Figure 6This is a schematic diagram of the structure of a device for determining the remaining cruising range according to an embodiment of the present application;

[0050] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0052] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0053] It should be understood herein that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0054] Based on the description of the above background technology, the following situations also exist in the related art:

[0055] In related technologies, the following formula is generally used to calculate the remaining cruising range:

[0056] Remaining cruising range (in km) = current remaining battery power (in kWh) / average energy consumption (in kWh / km);

[0057] It can be seen from the calculation formula of the remaining cruising range that in order to improve the accuracy of the remaining cruising range, we can start from two aspects. On the one hand, we can improve the accuracy of the current remaining battery power, and on the other hand, we can improve the accuracy of the average energy consumption. The current remaining battery power is detected and determined by the battery management system, and the development of battery-related technologies is already quite mature. The current detection and correction technology of the remaining battery power is relatively complete, so the determination of the current remaining battery power is usually more accurate. However, there is no unified method for calculating the average energy consumption. Different calculation methods will lead to large differences in the accuracy of the average energy consumption.

[0058] The common method for calculating average energy consumption is to calculate the average energy consumption per unit of historical mileage (usually the unit of historical mileage is relatively small, such as 10 km), and assume that the driver will also operate the vehicle according to this average energy consumption when driving in the future. The calculation process of average energy consumption is as follows:

[0059] Average energy consumption = (accumulated battery power consumption per unit historical mileage) / unit historical mileage

[0060] = (integral of battery power per unit historical mileage) / unit historical mileage

[0061] = (integral of battery current and voltage per unit historical mileage) / unit historical mileage;

[0062] The unit historical mileage in the related art is a relatively small mileage, for example, the unit historical mileage is set to 10km, or 20km, etc.;

[0063] However, the disadvantage of this method is that it arbitrarily assumes that the average energy consumption per unit historical mileage represents the average energy consumption of future users when driving the vehicle. It ignores the fact that the unit historical mileage cannot fully reflect the user's driving habits, thus leading to inaccurate calculation of average energy consumption.

[0064] The remaining cruising range determination method, electronic device, and vehicle provided in the embodiments of the present application can collect vehicle operating parameters according to a preset first sampling interval, and determine the operating condition ratio of each driving condition based on the vehicle operating parameters and a preset power consumption statistics table; collect vehicle power consumption parameters according to a preset second sampling interval, and determine the average energy consumption within the historical mileage based on the operating condition ratio and the vehicle power consumption parameters; wherein the first sampling interval is greater than the second sampling interval; and determine and display the remaining cruising range based on the current battery remaining power and the average energy consumption. By introducing a probabilistic statistical method into the calculation process of average energy consumption by determining each driving condition, the user's vehicle usage habits can be better determined, and the calculation of average energy consumption can be optimized, thereby improving the accuracy of average energy consumption, thereby improving the accuracy of the remaining cruising range, reducing the user's mileage anxiety, and improving the user's vehicle experience.

[0065] The following describes in detail the method for determining the remaining cruising range provided by the embodiments of the present application with reference to the accompanying drawings.

[0066] In some embodiments, as Figure 1 As shown, a method for determining the remaining cruising range includes:

[0067] Step 101: Collect vehicle operating parameters according to a preset first sampling interval, and determine the operating condition proportion of each driving condition according to the vehicle operating parameters and a preset power consumption statistics table.

[0068] In specific implementation, the regular sampling interval of vehicle operation data is generally at the millisecond level. The vehicle operation parameters collected based on the millisecond sampling interval are transient data. Transient data may deviate from the current scenario due to various operating conditions during vehicle driving (such as bad road conditions, slope conditions, etc.), and manifest as sudden increase or decrease in fluctuating data. These vehicle operation parameters that deviate from the current scenario will affect the accuracy of the calculation of average energy consumption and are noise data. Therefore, the influence of these noise data needs to be reduced or even eliminated when calculating the average energy consumption.

[0069] The embodiment of the present application chooses to reduce or even eliminate the influence of noise data by changing the sampling interval of vehicle operating parameters. By setting a larger first sampling interval, the transient vehicle operating parameters are converted into steady-state vehicle operating parameters. For example, taking the first sampling interval as 1 second, the corresponding sampling frequency is 1 second / time, and the first sampling interval is in the second level. The regular collection of vehicle operating parameters is 10 milliseconds, and the corresponding sampling frequency is 10 milliseconds / time, which is in the millisecond level. Then, if the vehicle operating parameters are collected once at the first sampling interval, the regular collection will be performed 100 times. If there is one noise data in the 100 collections, unless the noise appears in the 100th collection, it will not cause any problems with the accuracy of the vehicle operating parameters collected according to the first sampling interval.

[0070] If there is one piece of noise data in 100 acquisitions, there will be an error of approximately 1% when calculating based on millisecond-level data collection. If the vehicle operating parameters are collected at a first sampling interval of seconds, and the probability of noise data appearing in the 100th acquisition is 1%, then collecting the vehicle operating parameters at the first sampling interval of seconds will have an error of 1% × 1% = 0.01%, greatly reducing the probability of error and thereby improving the accuracy of the collected data. By changing the sampling interval of the vehicle operating parameters, the vehicle operating parameters are transformed from millisecond-level transient data to second-level steady-state data, improving the accuracy of the vehicle operating parameters themselves. This is because compared to transient values, steady-state values ​​can more accurately reflect the user's driving habits and behavior. Using steady-state values ​​for calculations can improve the accuracy of average energy consumption and achieve the accuracy of optimizing the remaining range from the data collection process.

[0071] The purpose of collecting vehicle operating parameters is to determine the user's vehicle usage habits. Usage habits can be expressed as a percentage. A higher percentage of a driving condition indicates that the user spends more time driving in that driving condition, indicating that the user is accustomed to driving in that driving condition. Vehicle operating parameters include monitored vehicle speed, monitored torque, and battery power consumption. Monitored vehicle speed represents the vehicle speed value collected during data collection at the first sampling interval. Monitored torque represents the motor output torque value collected during data collection at the first sampling interval. Battery power consumption represents the battery power consumption value sent by the battery management system during data collection at the first sampling interval.

[0072] The power consumption statistics table is a distribution relationship table showing power consumption under different driving conditions. For example, the power consumption statistics table is shown in Table 1:

[0073] Table 1 Power consumption statistics

[0074]

[0075] Among them, when the vehicle is in motion (this can be determined by whether the vehicle speed is greater than 1 kph; when the speed is greater than 1, the vehicle is considered in motion; when the speed is less than or equal to 1, the vehicle is considered stationary. The vehicle's driving data is not included in the statistics), the components that cause the battery's remaining power consumption to decrease can be roughly divided into three parts: the motor system, the low-voltage system (such as the instrument panel, lights, fans, etc.), and the high-voltage system (such as the air conditioning system). Therefore, the user's vehicle usage habits can be combined with the power consumption of these three systems to statistically divide them. The power consumption of the low-voltage and high-voltage systems can be combined into battery power consumption, which can reduce the corresponding data volume and improve calculation efficiency. The resulting power consumption statistics are shown in Table 1.

[0076] By using the grid division method shown in Table 1, statistics are collected during the first sampling interval T1 (e.g., 1 second) of vehicle operation, and the vehicle speed / torque / low-voltage system / high-voltage system power consumption is divided into grids according to ranges;

[0077] The grid-type division of vehicle speed and torque can be understood as mainly dividing the power consumption of the motor system by vehicle speed and torque;

[0078] To further improve accuracy, the power consumption of the low-voltage system and the high-voltage system can be divided separately to obtain the power consumption statistics shown in Table 2:

[0079] Table 2 Power consumption statistics after high and low voltage division

[0080]

[0081] PL1 / PL2 / PL3 are the power division thresholds for the low-voltage system and are all backward compatible. That is, when the low-voltage power is equal to PL2, the corresponding low-voltage interval is determined to be PL1-PL2. PH1 / PH2 / PH3 are the power division thresholds for the high-voltage system and are all backward compatible. That is, when the high-voltage power is equal to PH2, the corresponding low-voltage interval is determined to be PH1-PH2. N1-N32 represent the statistical parameters of the corresponding driving conditions. The values ​​of the statistical parameters are the statistical counts of the corresponding driving conditions. Tables 1 and 2 are examples. The grid can be made finer by dividing the range into more areas to improve statistical accuracy.

[0082] Taking the power consumption statistics table in Table 1 as an example, after collecting vehicle operating parameters, a counter is used to calculate these parameters to determine the user's driving habits. Specifically, while the user is driving, at each first sampling interval T1, the VCU determines which region of Table 1 the driving condition during the past T1 period falls within based on the vehicle operating parameters. The corresponding statistical parameter values ​​within that region are then accumulated. For example, if the monitored speed in the vehicle operating parameters is 15 kph, the monitored torque is 80 NM, and the battery power consumption is between P1 and P2, then the driving condition corresponds to the region N22 in the power consumption statistics table, and the corresponding statistical parameter is N22. Therefore, N22 needs to be accumulated, i.e., N22 = N22 + 1. If the initial value of N22 is N22 = 5, and after the vehicle has traveled for T1, the driving condition still corresponds to N22, then N22 will be 6 after one accumulation.

[0083] After obtaining the values ​​of each statistical parameter in the power consumption statistics table, continue to calculate the probability of each statistical parameter appearing in the entire power consumption statistics table: Taking the power consumption statistics table shown in Table 1 as an example, let Nsum = N1 + N2 + ... + N24, where Nsum is the total number of statistics, representing the total amount of statistical data. Then the proportion of each statistical parameter is N1 / Nsum, N2 / Nsum, N3 / Nsum, ..., N24 / Nsum, which is the proportion of the corresponding driving condition. For example, N22 / Nsum is the proportion of driving conditions with a monitored vehicle speed of 15kph, a monitored torque of 80NM, and a battery power consumption between P1 and P2.

[0084] By determining the proportion of operating conditions, a user's driving habits can be transformed into a statistically probabilistic method. As driving mileage increases, the statistical results become increasingly closer to the user's driving habits, thereby improving the accuracy of the average energy consumption calculation. The idea behind this embodiment of the application is to improve calculation accuracy by adjusting the proportion (weighted ratio) of transient values ​​and amplifying the ratio of the steady-state sampling points.

[0085] Step 102: Collect vehicle power consumption parameters according to a preset second sampling interval, and determine the average energy consumption within the historical mileage based on the operating condition ratio and the vehicle power consumption parameters. The first sampling interval is greater than the second sampling interval.

[0086] In specific implementation, the calculation process of average energy consumption is as follows:

[0087] Average energy consumption = (battery power in historical mileage × integral of corresponding operating condition ratio) × Nsum / historical mileage = (battery current in historical mileage × battery voltage × integral of corresponding operating condition ratio) × Nsum / historical mileage;

[0088] First, the unit historical mileage is changed to historical mileage to avoid the problem that the unit historical mileage cannot fully reflect the user's driving habits, thereby leading to inaccurate calculation of average energy consumption, and ensure the accuracy of the average energy consumption calculation.

[0089] Then, for the user's commonly used driving conditions, the proportion of the condition ratio is magnified by introducing a statistical method of the condition ratio, while the proportion of the less commonly used driving conditions in the overall energy consumption calculation is reduced by introducing a statistical method of the condition ratio. The average energy consumption is calculated using a weighted average method rather than an absolute average method; this can more accurately reflect each user's driving habits when driving the vehicle, and link the driving habits with the calculation of the average energy consumption. It should be noted that when calculating the condition ratio, the first sampling interval T1 needs to be much larger than the second sampling interval T2 of the vehicle's power consumption parameters, usually a comparison of s level (first sampling interval T1) VS ms level (second sampling interval T2).

[0090] The weighted average method is used to adjust the proportion of transient values ​​and amplify the ratio of the steady-state sampling points to improve the calculation accuracy, so that the calculated average energy consumption is closer to the user's driving habits, thereby improving the accuracy of the remaining cruising range and improving the user's driving experience.

[0091] Step 103: Determine and display the remaining cruising range based on the current remaining battery power and average energy consumption.

[0092] In specific implementation, the calculation formula for the remaining cruising range is as follows:

[0093] Remaining cruising range (in km) = current remaining battery power (in kWh) / average energy consumption (in kWh / km);

[0094] Among them, since the current battery remaining power detection technology is already quite mature, the current battery remaining power value is generally more accurate, and the average energy consumption has been statistically optimized through weighted average, which has greatly reduced the error. The accuracy of the remaining cruising range determined based on the current battery remaining power and the average energy consumption obtained after optimized calculation is greatly improved, reducing the user's mileage anxiety and improving the user's car experience.

[0095] In summary, the method for determining the remaining cruising range provided in the embodiment of the present application can collect vehicle operating parameters according to a preset first sampling interval, and determine the operating condition ratio of each driving condition based on the vehicle operating parameters and a preset power consumption statistics table; collect vehicle power consumption parameters according to a preset second sampling interval, and determine the average energy consumption within the historical mileage based on the operating condition ratio and the vehicle power consumption parameters; and determine and display the remaining cruising range based on the current battery remaining power and the average energy consumption. By introducing a probabilistic statistical method into the calculation process of average energy consumption by determining each driving condition, the user's driving habits can be better determined, and the calculation of average energy consumption can be optimized, thereby improving the accuracy of average energy consumption, thereby improving the accuracy of the remaining cruising range, reducing the user's mileage anxiety, and improving the user's driving experience.

[0096] In some embodiments, as Figure 2 As shown, the proportion of each driving condition is determined based on the vehicle operating parameters and the preset power consumption statistics table, including:

[0097] Step 201: determining a target statistical parameter corresponding to a vehicle operating parameter in a power consumption statistical table, and performing a cumulative counting on the target statistical parameter.

[0098] In specific implementation, the vehicle operating parameters include monitoring vehicle speed, monitoring torque, and battery power consumption; the target statistical parameters corresponding to the vehicle operating parameters are determined in the power consumption statistics table, including:

[0099] Step 2011: Determine a target vehicle speed interval item corresponding to the monitored vehicle speed and a target torque interval item corresponding to the monitored torque in the power consumption statistics table.

[0100] In specific implementation, taking the power consumption statistics table shown in Table 1 as an example, the torque intervals include [-100, -50], (-50, 0], (0, 50], (50, 100], and the speed intervals include [1, 10], (10, 20]. If the monitored speed in the vehicle operating parameters is 15 kph, 15 kph is less than the upper boundary speed of 20 kph in the speed interval (10, 20], and is greater than the lower boundary speed of 10 kph in the speed interval (10, 20], then the corresponding target speed interval is (10, 20], and the target speed interval item is 10~20. If the monitored torque in the vehicle operating parameters is 80 NM, 80 NM is less than the upper boundary torque of 100 NM in the torque interval (50, 100], and is greater than the lower boundary torque of 50 NM in the torque interval (50, 100], then the corresponding target torque interval is (50, 100], and the target torque interval item is 50~100.

[0101] Step 2012: In the power consumption statistics table, a parameter selection range is determined according to the target torque range item and the target vehicle speed range item, and a target power range item corresponding to the battery consumption power is determined in the parameter selection range.

[0102] In specific implementation, when the target torque interval item is determined to be 50~100 and the target vehicle speed interval item is 10~20, the corresponding parameter selection range is N22-N24 in Table 1, so it is necessary to determine the final target statistical parameters within the parameter selection range, and N22, N23 and N24 correspond to a power interval respectively, among which the statistical parameter N22 corresponds to the power interval [P1, P2], the statistical parameter N23 corresponds to the power interval (P2, P3], and the statistical parameter N24 corresponds to the power interval (P3, P4). If the battery power consumption is within the power interval [P1, P2], then the target power interval item corresponding to the battery power consumption is P1~P2.

[0103] Step 2013: Determine the statistical parameter corresponding to the target power interval item in the parameter selection range as the target statistical parameter.

[0104] In specific implementation, if the target power interval item is P1~P2, then the driving condition at this time corresponds to the area where N22 is located in the power consumption statistics table, and the corresponding statistical parameter is N22. The statistical parameter corresponding to the target power interval item in the parameter selection range is N22, and N22 is determined as the target statistical parameter.

[0105] When the target statistical parameter is determined to be N22, it is necessary to perform a count accumulation on the target statistical parameter N22, i.e., set N22 = N22 + 1. If the initial value of N22 is N22 = 5, after the vehicle has traveled for time T1, the area corresponding to the vehicle's driving condition is still N22. After a count accumulation, N22 = 6.

[0106] Step 202: Determine the statistical parameters of each driving condition in the power consumption statistical table, and determine the sum of all statistical parameters as the statistical total.

[0107] In specific implementation, when calculating the proportion of working conditions, the numerator of the calculation is the value of the target statistical parameter N22. At this time, it is necessary to further determine the denominator of the proportion calculation. The denominator is the statistical total of the power consumption statistics table. The statistical total is the sum of all statistical parameters in the power consumption statistics table, that is, the statistical total Nsum=N1+N2+~+N24, Nsum is the statistical total, which represents the total amount of statistical data.

[0108] Step 203: Determine the ratio of each statistical parameter to the statistical total as the operating condition ratio of the corresponding driving condition.

[0109] In specific implementations, the proportions of the various statistical parameters are N1 / Nsum, N2 / Nsum, N3 / Nsum, ..., N24 / Nsum, which represent the proportions of the corresponding driving conditions. N22 / Nsum represents the proportion of the driving condition where the monitored speed is 15 kph, the monitored torque is 80 NM, and the battery power consumption is between P1 and P2.

[0110] By determining the proportion of operating conditions, a user's driving habits can be transformed into a statistically probabilistic method. As driving mileage increases, the statistical results become increasingly closer to the user's driving habits, thereby improving the accuracy of the average energy consumption calculation. The idea behind this embodiment of the application is to improve calculation accuracy by adjusting the proportion (weighted ratio) of transient values ​​and amplifying the ratio of the steady-state sampling points.

[0111] In some embodiments, the battery power consumption includes low voltage power consumption and high voltage power consumption; Figure 3 As shown, the target power range item corresponding to the battery consumption power is determined in the parameter selection range, including:

[0112] Step 301: Determine a target low-voltage interval item corresponding to low-voltage power consumption and a target high-voltage interval item corresponding to high-voltage power consumption in the parameter selection range.

[0113] In specific implementations, to further improve accuracy, the power consumption of the low-voltage and high-voltage systems can be divided separately, resulting in a power consumption statistics table as shown in Table 2: PL1 / PL2 / PL3 are the power division thresholds for the low-voltage system, and are all backward compatible. That is, when the low-voltage power is equal to PL2, the corresponding low-voltage interval is determined to be PL1-PL2. PH1 / PH2 / PH3 are the power division thresholds for the high-voltage system, and are all backward compatible. That is, when the high-voltage power is equal to PH2, the corresponding low-voltage interval is determined to be PH1-PH2.

[0114] If the low-voltage power consumption is between PL1 and PL2, PL1 and PL2 are determined as the target low-voltage interval items; if the high-voltage power consumption is between PH1 and PH2, PH1 and PH2 are determined as the target high-voltage interval items.

[0115] Step 302: Determine the search items corresponding to the target high voltage interval item and the target low voltage interval item in the parameter selection range as target power interval items.

[0116] In specific implementation, according to Table 2, when the target low pressure interval item is PL1~PL2 and the target high pressure interval item is PH1~PH2, the corresponding search item is N29.

[0117] By separating the power consumption of the low-voltage system and the high-voltage system, the accuracy of the statistical data is further improved, which in turn improves the accuracy of the calculation of average energy consumption and achieves accurate calculation of remaining range. The more content the statistical table is divided into, the more accurate the calculation results will be.

[0118] In some embodiments, as Figure 4 As shown, the average energy consumption within the historical mileage is determined based on the operating condition ratio and vehicle power parameters, including:

[0119] Step 401: Determine the total energy consumption within the historical mileage based on the vehicle's power consumption parameters and operating condition ratio.

[0120] In specific implementation, the battery current and voltage of each collected vehicle power parameter must first be determined, followed by the power at each collection moment. The operating mode percentage of power at different moments is then determined, and the product of power and operating mode percentage is integrated over time to obtain the weighted average energy consumption over the historical mileage. Finally, the weighted average energy consumption is multiplied by Nsum to determine the total energy consumption: total energy consumption = (battery power over historical mileage × integral of corresponding operating mode percentage) × Nsum. The historical mileage is the total mileage from the time of the vehicle's initial drive to the current moment. Using historical mileage for calculations provides more data, which can improve the accuracy of the corresponding average energy consumption calculation.

[0121] While ensuring accuracy, it is also necessary to ensure the efficiency of the calculation process. Therefore, certain restrictions need to be placed on historical mileage. If the historical mileage is less than or equal to a preset mileage threshold (e.g., 500km), the historical mileage is used to calculate the average energy consumption. If the historical mileage is less than or equal to a preset mileage threshold (e.g., 500KM), the mileage threshold is used to replace the historical mileage to calculate the average energy consumption. When using the mileage threshold to calculate the average energy consumption, the mileage at the current moment is used as the starting point for mileage selection, and the mileage of 500km is selected as the historical mileage. For example, if the historical mileage is 600km up to the current moment, the mileage of the last 500km is selected as the mileage for calculation, because the data closer to the current moment can better reflect the user's car usage habits. By limiting the selection of historical mileage, the accuracy of the average energy consumption calculation is guaranteed while reducing the amount of calculation and improving the calculation efficiency.

[0122] Step 402: Determine the ratio of the total energy consumption to the historical mileage as the average energy consumption.

[0123] In specific implementation, average energy consumption = total energy consumption / historical mileage = (battery power in historical mileage × integral of corresponding operating condition proportion) × Nsum / historical mileage = (battery current in historical mileage × battery voltage × integral of corresponding operating condition proportion) × Nsum / historical mileage.

[0124] In some embodiments, the vehicle power parameters include battery current and battery voltage; Figure 5 As shown, the total energy consumption within the historical mileage is determined based on the vehicle's power consumption parameters and operating conditions, including:

[0125] Step 501: Perform time-based integration calculation on the product of the battery current and the battery voltage to obtain the battery calculated energy consumption.

[0126] In specific implementation, the corresponding energy consumption value is obtained by integrating the battery power within the historical mileage. The battery power is the product of the battery current and the battery voltage. Therefore, by performing a time-based integral calculation on the product of the battery current and the battery voltage, the calculated battery energy consumption at each data collection moment can be obtained.

[0127] Step 502: Determine the target operating condition ratio corresponding to the battery calculated energy consumption, and perform weighted average calculation on the battery calculated energy consumption and the corresponding target operating condition ratio to obtain a weighted average energy consumption.

[0128] During specific implementation, determine the target operating condition ratio corresponding to the battery calculation energy consumption, including:

[0129] Step 5021: Determine a target proportional power interval including battery calculation energy consumption from a plurality of preset proportional power intervals.

[0130] In specific implementation, take the case where only N1, N2 and N3 are present as an example. If the operating condition corresponding to N1 accounts for 10%, the operating condition corresponding to N2 accounts for 70%, and the operating condition corresponding to N3 accounts for 20%, and the power interval corresponding to N1 is [P1, P2]; the power interval corresponding to N2 is (P2, P3]; and the power interval corresponding to N3 is (P3, P4]. If the battery calculated energy consumption X1 is within the power interval [P1, P2], [P1, P2] is used as the target power interval; if the battery calculated energy consumption X2 is within the power interval (P2, P3], (P2, P3] is used as the target power interval; if the battery calculated energy consumption X3 is within the power interval (P3, P4], (P3, P4] is used as the target power interval.

[0131] Step 5022: Determine the operating condition proportion corresponding to the target proportion power interval as the target operating condition proportion.

[0132] In specific implementation, if the battery calculated energy consumption X1 is within the power interval [P1, P2], 10% corresponding to the target power interval [P1, P2] is used as the target operating condition proportion; if the battery calculated energy consumption X2 is within the power interval (P2, P3], 70% corresponding to the target power interval (P2, P3] is used as the target operating condition proportion; if the battery calculated energy consumption X3 is within the power interval (P3, P4], 20% corresponding to the target power interval (P3, P4] is used as the target operating condition proportion.

[0133] The weighted average energy consumption = X1×10%+X2×70%+X3×20%. In actual use, the historical mileage of 500km may include a large amount of battery calculated energy consumption. The weighted average energy consumption can be calculated in the same way. The weighted average energy consumption = battery current in historical mileage × battery voltage × the integral of the corresponding operating condition ratio.

[0134] Step 503: Determine the number of collected samples based on the historical mileage and the second sampling interval.

[0135] In specific implementation, step 503 includes:

[0136] Step 5031: Determine the driving duration of the historical mileage.

[0137] In specific implementation, because the second sampling interval of the vehicle power consumption parameters is 10ms, if the historical mileage is 500km, it is necessary to determine the driving time consumed by the vehicle to travel 500km, so as to determine the number of samples of the vehicle power consumption parameters collected during the historical mileage.

[0138] Step 5032: Determine the ratio of the driving duration to the second sampling interval as the number of collected samples.

[0139] In specific implementation, the second sampling interval is the frequency of collecting vehicle power parameters, and the ratio of the driving time to the second sampling interval is the number of collected samples; if the driving time is 6 hours, the number of collected samples = 6×3600000 / 10=2160000.

[0140] Among them, when collecting data, there may be some data that exceeds the boundary and needs to be eliminated. The elimination operation may cause the number of collected samples to change. Because the statistical range of the operating condition ratio is P1~P4, if the battery calculated energy consumption corresponding to a certain vehicle power parameter exceeds the range of P1~P4, the battery calculated energy consumption will not be matched to the corresponding operating condition ratio. In this case, the battery calculated energy consumption can be directly eliminated, and the number of collected samples can be reduced by 1. If there are 50 out-of-range elimination data among the 2160000 vehicle power parameters, the final number of collected samples = 2160000-50=2159950. Data elimination can further improve the accuracy of the calculation of total energy consumption, thereby improving the accuracy of the calculation of the remaining cruising range.

[0141] Step 504: The product of the weighted average energy consumption and the number of collected samples is determined as the total energy consumption.

[0142] In specific implementation, the weighted average represents the energy consumption of a single collected data, and the product of the weighted average energy consumption and the number of collected samples is the total energy consumption consumed by the historical mileage.

[0143] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0144] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0145] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a device for determining the remaining cruising range.

[0146] refer to Figure 6 The remaining cruising range determining device includes:

[0147] The operating condition ratio determination module 10 is configured to: collect vehicle operating parameters according to a preset first sampling interval, and determine the operating condition ratio of each driving condition based on the vehicle operating parameters and a preset power consumption statistics table;

[0148] The average energy consumption determination module 20 is configured to: collect vehicle power consumption parameters according to a preset second sampling interval, and determine the average energy consumption within the historical mileage based on the operating condition ratio and the vehicle power consumption parameters; wherein the first sampling interval is greater than the second sampling interval;

[0149] The cruising range determination module 30 is configured to determine and display the remaining cruising range based on the current remaining battery power and average energy consumption.

[0150] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0151] The device of the above embodiment is used to implement the corresponding method for determining the remaining cruising range in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0152] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for determining the remaining cruising range described in any of the above embodiments is implemented.

[0153] Figure 7 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0154] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0155] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0156] The input / output interface 1030 is used to connect to input / output modules to enable information input and output. The input / output modules can be configured as components within the device (not shown) or externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, and various sensors. Output devices may include a display, speaker, vibrator, indicator light, and the like.

[0157] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.).

[0158] The bus 1050 comprises a pathway for transmitting information between various components of the device, such as the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 .

[0159] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0160] The electronic device of the above embodiment is used to implement the corresponding remaining cruising range determination method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0161] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the method for determining the remaining cruising range as described in any of the above embodiments.

[0162] The computer-readable media of this embodiment includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0163] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for determining the remaining cruising range as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0164] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a vehicle, including the electronic device or the device for determining the remaining cruising range of the above-mentioned embodiments, and executing the method for determining the remaining cruising range as described in any of the above embodiments through the electronic device or the device for determining the remaining cruising range of the above-mentioned embodiments, and having the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0165] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0166] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the disclosed technical solution based on the prompt message.

[0167] As an optional but non-limiting implementation, in response to a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0168] It is understandable that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0169] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0170] In addition, to simplify the description and discussion, and to avoid obscuring the understanding of the embodiments of the present application, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. Furthermore, devices may be shown in block diagram form to avoid obscuring the understanding of the embodiments of the present application, and this also takes into account the fact that the implementation details of these block diagram devices are highly dependent on the platform on which the embodiments of the present application will be implemented (i.e., these details should be fully understood by those skilled in the art). Where specific details (e.g., circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations therefrom. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0171] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the discussed embodiments.

[0172] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for determining remaining cruising range, characterized in that: include: Collecting vehicle operating parameters according to a preset first sampling interval, and determining a working condition proportion of each driving condition based on the vehicle operating parameters and a preset power consumption statistical table; collecting vehicle power consumption parameters according to a preset second sampling interval, and determining average energy consumption within historical mileage based on the operating condition proportion and the vehicle power consumption parameters; wherein the first sampling interval is greater than the second sampling interval; Determine and display the remaining cruising range based on the current remaining battery power and the average energy consumption; The determining of the proportion of each driving condition according to the vehicle operating parameters and a preset power consumption statistics table includes: Determining a target statistical parameter corresponding to the vehicle operating parameter in the power consumption statistical table, and performing a cumulative counting on the target statistical parameter; Determining statistical parameters for each driving condition in the power consumption statistical table, and determining the sum of all statistical parameters as a statistical total; Determining the ratio of each statistical parameter to the statistical total as the operating condition ratio of the corresponding driving condition; The vehicle operating parameters include monitoring vehicle speed, monitoring torque, and battery power consumption; and determining target statistical parameters corresponding to the vehicle operating parameters in the power consumption statistical table includes: determining, in the power consumption statistical table, a target vehicle speed interval item corresponding to the monitored vehicle speed and a target torque interval item corresponding to the monitored torque; In the power consumption statistics table, determining a parameter selection range according to the target torque range item and the target vehicle speed range item, and determining a target power range item corresponding to the battery consumption power within the parameter selection range; The statistical parameter corresponding to the target power interval item in the parameter selection range is determined as the target statistical parameter.

2. The method for determining the remaining cruising range according to claim 1, wherein: The battery power consumption includes low-voltage power consumption and high-voltage power consumption; and determining a target power interval item corresponding to the battery power consumption within the parameter selection range includes: Determining a target low-voltage interval item corresponding to the low-voltage power consumption and a target high-voltage interval item corresponding to the high-voltage power consumption within the parameter selection range; The search item corresponding to the target high-voltage interval item and the target low-voltage interval item in the parameter selection range is determined as the target power interval item.

3. The method for determining the remaining cruising range according to claim 1, wherein: The determining of the average energy consumption within the historical mileage according to the operating condition proportion and the vehicle power consumption parameter includes: Determining the total energy consumption within the historical mileage according to the vehicle power consumption parameters and the operating condition ratio; The ratio of the total energy consumption to the historical mileage is determined as the average energy consumption.

4. The method for determining the remaining cruising range according to claim 3, wherein: The vehicle power parameters include battery current and battery voltage; and determining the total energy consumption within the historical mileage based on the vehicle power parameters and the operating condition ratio includes: Performing a time-based integral calculation on the product of the battery current and the battery voltage to obtain a battery calculated energy consumption; Determining a target operating condition ratio corresponding to the battery calculated energy consumption, and performing a weighted average calculation on the battery calculated energy consumption and the corresponding target operating condition ratio to obtain a weighted average energy consumption; Determining the number of collected samples according to the historical mileage and the second sampling interval; The product of the weighted average energy consumption and the number of collected samples is determined as the total energy consumption.

5. The method for determining the remaining cruising range according to claim 4, characterized in that: The determining of the target operating condition ratio corresponding to the calculated energy consumption of the battery includes: Determining a target proportional power interval including the battery calculated energy consumption from a plurality of preset proportional power intervals; The operating condition proportion corresponding to the target proportion power interval is determined as the target operating condition proportion.

6. The method for determining the remaining cruising range according to claim 4, wherein: The determining the number of collected samples according to the historical mileage and the second sampling interval includes: Determine the driving duration of the historical mileage; The ratio of the driving duration to the second sampling interval is determined as the number of collected samples.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

8. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 7.

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