Remaining mileage prediction method and device of vehicle, vehicle and storage medium

Abstract

The invention relates to the technical field of vehicles, in particular to a remaining mileage prediction method and device of a vehicle, the vehicle and a storage medium. The method comprises the steps that the current driving working condition, the current battery remaining energy state and historical operation data of the vehicle are obtained, and the basic average energy consumption of the vehicle is matched according to the current driving working condition; the historical average energy consumption of the vehicle is determined according to the historical operation data, the target average energy consumption of the vehicle is calculated according to the basic average energy consumption and the historical average energy consumption, and the current remaining mileage of the vehicle is obtained according to the current battery remaining energy state and the target average energy consumption. Therefore, the problems of low prediction precision, complex calculation and the like of the remaining mileage of the vehicle in the related technology are solved, the basic average consumption corresponding to the traditional working condition and the historical average energy consumption of segmented calculation are combined, and the weight coefficient is introduced, so that the accuracy of calculating the target average energy consumption is improved, and the prediction accuracy of the remaining mileage of the vehicle is improved. And thus, accurate residual mileage prediction is realized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicles, and particularly relates to a method, device, vehicle and storage medium for predicting the remaining mileage of a vehicle. Background Art

[0002] During the vehicle driving process, in order to prevent the user from exhausting the battery energy during driving and being unable to reach the destination due to the deviation of the predicted remaining mileage value, it is necessary to provide the user with the predicted remaining mileage value that can continue to drive under the current battery reserve scenario, so as to prompt the user to replenish energy in time when the battery energy is insufficient and avoid exhausting the energy during driving.

[0003] In the related art, the prediction methods of the remaining mileage mainly include two types. One is to calculate the remaining mileage based on the historical average energy consumption and the remaining battery power, and the other is to accurately match the energy consumption parameters in the path library according to the current running conditions of the vehicle, obtain the future vehicle energy consumption situation, and predict the remaining mileage of the vehicle based on the future energy consumption parameters.

[0004] However, by obtaining the current remaining battery power to calculate the remaining mileage, only one prediction strategy of driving with the historical average energy consumption can be obtained, and the future remaining mileage cannot be accurately obtained. At the same time, in the process of fitting and matching the energy consumption parameters in the path library according to the driving conditions, due to the continuous change of the fitting line, the calculation strategy is relatively complex, reducing the accuracy of the energy consumption parameters, which urgently needs to be solved. Summary of the Invention

[0005] The present application provides a method, device, vehicle and storage medium for predicting the remaining mileage of a vehicle to solve the problems of low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art.

[0006] The first aspect embodiment of the present application provides a method for predicting the remaining mileage of a vehicle, including the following steps:

[0007] Obtain the current driving condition, the current remaining energy state of the battery and the historical operation data of the vehicle;

[0008] Match the basic average energy consumption of the vehicle according to the current driving condition, determine the historical average energy consumption of the vehicle according to the historical operation data, and calculate the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption;

[0009] Obtain the current remaining driving mileage of the vehicle according to the current remaining energy state of the battery and the target average energy consumption.

[0010] According to an embodiment of the present application, the determining the historical average energy consumption of the vehicle according to the historical operation data includes:

[0011] Based on the historical operation data, obtain the energy consumption data within a preset mileage.

[0012] Based on a preset mileage allocation strategy, divide the preset mileage to obtain the energy consumption data of the vehicle for each mileage segment.

[0013] Determine the weight value of each mileage segment, and calculate the historical average energy consumption of the vehicle according to the weight value of each mileage segment and the energy consumption data of each mileage segment.

[0014] According to an embodiment of the present application, the obtaining the current remaining energy state of the vehicle battery includes:

[0015] Obtain the current state of charge of the vehicle battery, the current health state of the battery, the theoretical battery capacity value, and the discharge coefficient value of the cell temperature.

[0016] Perform filtering processing on the current state of charge and the current health state, and obtain the current remaining energy state of the battery based on the filtered current state of charge, the filtered current health state, the theoretical battery capacity value, and the discharge coefficient value of the cell temperature.

[0017] According to an embodiment of the present application, the calculating the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption includes:

[0018] Determine the first weight coefficient of the historical average energy consumption and the second weight coefficient of the basic average energy consumption.

[0019] Calculate the first product of the historical average energy consumption and the first weight coefficient, and calculate the second product of the basic average energy consumption and the second weight coefficient.

[0020] Obtain the target average energy consumption according to the sum of the first product and the second product.

[0021] According to an embodiment of the present application, after obtaining the current remaining driving mileage of the vehicle according to the current remaining energy state of the battery and the target average energy consumption, it further includes:

[0022] Judge whether the vehicle enters the power-off mode.

[0023] If the vehicle enters the power-off mode, store the current remaining energy state of the battery and the current remaining driving mileage, and detect the new remaining energy state of the vehicle battery and the new remaining driving mileage after the vehicle is powered on again.

[0024] If the difference between the remaining energy state of the new battery and the remaining energy state of the current battery is within a first preset range, or the difference between the new remaining driving range and the current remaining driving range is within a second preset range, then use the current remaining driving range;

[0025] If the difference between the remaining energy state of the new battery and the remaining energy state of the current battery is not within the first preset range, and the difference between the new remaining driving range and the current remaining driving range is not within the second preset range, then use the new remaining driving range.

[0026] According to the method for predicting the remaining mileage of a vehicle according to an embodiment of the present application, the current driving condition, the current remaining energy state of the battery, and the historical operation data of the vehicle are obtained, the basic average energy consumption of the vehicle is matched according to the current driving condition, the historical average energy consumption of the vehicle is determined according to the historical operation data, and the target average energy consumption of the vehicle is calculated according to the basic average energy consumption and the historical average energy consumption. The current remaining driving range of the vehicle is obtained according to the current remaining energy state of the battery and the target average energy consumption. Thus, problems such as low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art are solved. By combining the basic average consumption corresponding to the traditional working condition and the historical average energy consumption calculated in segments, and introducing a weight coefficient thereto, the accuracy of calculating the target average energy consumption is improved, and thus accurate prediction of the remaining mileage is realized.

[0027] An embodiment of the second aspect of the present application provides a device for predicting the remaining mileage of a vehicle, including:

[0028] A first acquisition module, configured to acquire the current driving condition, the current remaining energy state of the battery, and the historical operation data of the vehicle;

[0029] A calculation module, configured to match the basic average energy consumption of the vehicle according to the current driving condition, determine the historical average energy consumption of the vehicle according to the historical operation data, and calculate the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption;

[0030] A second acquisition module, configured to obtain the current remaining driving range of the vehicle according to the current remaining energy state of the battery and the target average energy consumption.

[0031] According to an embodiment of the present application, the calculation module includes:

[0032] A first acquisition unit, configured to acquire energy consumption data within a preset mileage based on the historical operation data;

[0033] A division unit, configured to divide the preset mileage based on a preset mileage distribution strategy to obtain the energy consumption data of the vehicle for each mileage segment;

[0034] A first calculation unit, configured to determine a weight value for each mileage segment, and calculate a historical average energy consumption of the vehicle according to the weight value of each mileage segment and the energy consumption data of each mileage segment.

[0035] According to an embodiment of the present application, the first acquisition module includes:

[0036] A second acquisition unit, configured to acquire a current state of charge of the battery of the vehicle, a current state of health of the battery, a theoretical battery charge value, and a discharge coefficient value of the cell temperature;

[0037] A filtering unit, configured to perform filtering processing on the current state of charge and the current state of health, and obtain the current remaining energy state of the battery based on the filtered current state of charge, the filtered current state of health, the theoretical battery charge value, and the discharge coefficient value of the cell temperature.

[0038] According to an embodiment of the present application, the calculation module includes:

[0039] A first determination unit, configured to determine a first weight coefficient of the historical average energy consumption and a second weight coefficient of the basic average energy consumption;

[0040] A second calculation unit, configured to calculate a first product of the historical average energy consumption and the first weight coefficient, and calculate a second product of the basic average energy consumption and the second weight coefficient;

[0041] A second determination unit, configured to obtain the target average energy consumption according to the sum of the first product and the second product.

[0042] According to an embodiment of the present application, after obtaining the current remaining driving mileage of the vehicle according to the current remaining energy state of the battery and the target average energy consumption, the second acquisition module further includes:

[0043] A judgment unit, configured to judge whether the vehicle enters a power-off mode;

[0044] A detection unit, configured to, if the vehicle enters the power-off mode, store the current remaining energy state of the battery and the current remaining driving mileage, and detect a new remaining energy state of the battery and a new remaining driving mileage of the vehicle after the vehicle is powered on again;

[0045] A first selection unit, configured to, if the difference between the new remaining energy state of the battery and the current remaining energy state of the battery is within a first preset range, or the difference between the new remaining driving mileage and the current remaining driving mileage is within a second preset range, use the current remaining driving mileage;

[0046] A second selection unit, configured to use the new remaining driving range if the difference between the new remaining energy state of the battery and the current remaining energy state of the battery does not fall within the first preset range, and the difference between the new remaining driving range and the current remaining driving range does not fall within the second preset range.

[0047] The remaining mileage prediction device of a vehicle according to an embodiment of the present application obtains the current driving condition, the current remaining energy state of the battery, and the historical operation data of the vehicle, matches the basic average energy consumption of the vehicle according to the current driving condition, determines the historical average energy consumption of the vehicle according to the historical operation data, calculates the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption, and obtains the current remaining driving range of the vehicle according to the current remaining energy state of the battery and the target average energy consumption. Thereby, problems such as low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art are solved. By combining the basic average consumption corresponding to the traditional driving condition and the historical average energy consumption calculated in segments, and introducing a weight coefficient thereto, the accuracy of calculating the target average energy consumption is improved, and thus accurate remaining mileage prediction is realized.

[0048] An embodiment of the third aspect of the present application provides a vehicle, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the remaining mileage prediction method of the vehicle as described in the above embodiment.

[0049] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores computer instructions for causing the computer to execute the remaining mileage prediction method of the vehicle as described in the above embodiment.

[0050] An embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and the computer program is executed to implement the remaining mileage prediction method of the vehicle as described in the above embodiment.

[0051] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0052] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, in which:

[0053] Figure 1 It is a flowchart of a remaining mileage prediction method of a vehicle according to an embodiment of the present application;

[0054] Figure 2Schematic diagram of modules involved in remaining mileage calculation according to an embodiment of the present application;

[0055] Figure 3 Flowchart for calculating the relative State of Energy (SOE) according to an embodiment of the present application;

[0056] Figure 4 Block diagram example of a remaining mileage prediction device for a vehicle according to an embodiment of the present application;

[0057] Figure 5 Schematic diagram of the structure of a vehicle according to an embodiment of the present application. Detailed implementation manners

[0058] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0059] The remaining mileage prediction method, device, vehicle, and storage medium of the vehicle according to the embodiments of the present application will be described below with reference to the accompanying drawings. Aiming at the problems of low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art mentioned in the above background art, the present application provides a remaining mileage prediction method for a vehicle. In this method, the current driving condition, the current battery remaining energy state, and the historical operation data of the vehicle are obtained, the basic average energy consumption of the vehicle is matched according to the current driving condition, the historical average energy consumption of the vehicle is determined according to the historical operation data, and the target average energy consumption of the vehicle is calculated according to the basic average energy consumption and the historical average energy consumption. The current remaining driving mileage of the vehicle is obtained according to the current battery remaining energy state and the target average energy consumption. Thus, the problems of low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art are solved. By combining the basic average consumption corresponding to the traditional working condition and the historical average energy consumption calculated in segments, and introducing a weight coefficient thereto, the accuracy of calculating the target average energy consumption is improved, and thus accurate remaining mileage prediction is realized.

[0060] Specifically, Figure 1 Schematic flowchart of a remaining mileage prediction method for a vehicle provided by an embodiment of the present application.

[0061] As Figure 1 shown, the remaining mileage prediction method for the vehicle includes the following steps:

[0062] In step S101, the current driving condition, the current battery remaining energy state, and the historical operation data of the vehicle are obtained.

[0063] According to an embodiment of the present application, obtaining the current remaining energy state of the vehicle's battery includes: obtaining the current state of charge of the vehicle's battery, the current state of health of the battery, the theoretical battery capacity value, and the discharge coefficient value of the cell temperature; performing filtering processing on the current state of charge and the current state of health, and obtaining the current remaining energy state of the battery based on the filtered current state of charge, the filtered current state of health, the theoretical battery capacity value, and the discharge coefficient value of the cell temperature.

[0064] Specifically, during the operation of the vehicle, in order to provide the user with an accurate predicted remaining mileage value so that the user can replenish energy in time and avoid running out of energy during the journey and being unable to reach the destination, the embodiments of the present application need to accurately predict the remaining mileage of the vehicle. To improve the prediction accuracy of the remaining mileage, the remaining mileage prediction method of the embodiments of the present application mainly includes five parts, as Figure 2 shown, which are BMS (Battery Management System) signal preprocessing, mileage display initialization, average energy consumption calculation, driving condition matching, and dynamic mileage prediction calculation. Based on the accurate calculations of the above five parts, the remaining mileage of the vehicle can be obtained and the prediction accuracy can be ensured.

[0065] Specifically, the embodiments of the present application first need to obtain BMS signals, mainly including the vehicle's SOC (State of Charge) (i.e., the current state of charge of the battery), SOH (State of Health) (i.e., the current state of health of the battery), SOE, the theoretical battery capacity value, and the discharge coefficient value of the cell temperature. To avoid the jump of the mileage display caused by the jump of the BMS signal and ensure the stability of the BMS signal, the embodiments of the present application need to perform filtering processing on the BMS signals, that is, the above-mentioned SOC and SOH, to ensure that the original value remains unchanged when a sudden signal jump occurs, and if the input data exceeds the filtering time, it follows the input value.

[0066] Furthermore, as Figure 3 shown, to improve the true discharge capacity of the current battery, the battery capacity of the embodiments of the present application cannot use the separately input SOE, but needs to calculate a relative SOE, that is, the current remaining energy state of the battery, with the filtered SOC, the filtered SOH, the theoretical battery capacity value, and the discharge coefficient value of the cell temperature, so as to calculate the final remaining mileage of the vehicle using the calculated current remaining energy state of the battery. Among them, the specific formula for the relative SOE can be expressed as:

[0067]

[0068] The discharge coefficient value of the cell temperature in the embodiments of the present application is shown in Table 1:

[0069] Table 1

[0070] Cell temperature -20 0 5 20 Coefficient 0.6 0.8 0.9 1

[0071] Therefore, the corresponding discharge coefficient value can be matched according to different cell temperatures, and the relative SOE can be calculated based on the discharge coefficient value based on the cell temperature and in combination with the filtered SOC, the filtered SOH, and the theoretical battery charge value, thereby ensuring the prediction accuracy of the remaining mileage.

[0072] Furthermore, the embodiments of the present application also need to obtain the current driving condition of the vehicle and the historical operation data corresponding to each condition. For example, the current driving condition may include a highway condition, an urban condition, a suburban condition, a mountain condition, etc., and at the same time, obtain the historical operation data corresponding to the highway condition, the urban condition, the suburban condition, and the mountain condition respectively.

[0073] In step S102, the basic average energy consumption of the vehicle is matched according to the current driving condition, the historical average energy consumption of the vehicle is determined according to the historical operation data, and the target average energy consumption of the vehicle is calculated according to the basic average energy consumption and the historical average energy consumption.

[0074] According to an embodiment of the present application, determining the historical average energy consumption of the vehicle according to the historical operation data includes: based on the historical operation data, obtaining the energy consumption data within a preset mileage; based on a preset mileage allocation strategy, dividing the preset mileage to obtain the energy consumption data of the vehicle for each mileage segment; determining the weight value for each mileage segment, and calculating the historical average energy consumption of the vehicle according to the weight value of each mileage segment and the energy consumption data of each mileage segment.

[0075] Among them, both the preset mileage and the preset mileage allocation strategy can be set by those skilled in the art according to actual prediction requirements, and specific limitations are not made here.

[0076] Specifically, the embodiments of the present application set the mileage in units of 10 km, that is, the preset mileage, and based on a preset mileage allocation strategy, for example, dividing it into segments of 500 m within a 10 km mileage, a total of 20 segments can be divided. At this time, based on the historical operation data, the energy consumption data of the vehicle for each mileage segment can be obtained, and a weight value is added to the energy consumption data of each mileage segment, so that the historical average energy consumption of the vehicle can be calculated according to the weight value of each mileage segment and the energy consumption data of each mileage segment to show the real-time nature of the historical average energy consumption. Among them, the historical average energy consumption can be shown by the following formula:

[0077]

[0078] where, a 1 +a 2 +…a 20= 1, 0 < i <= 20, the energy consumption coefficient closer to the current moment is larger, and this coefficient can be adjusted by calibration.

[0079] According to an embodiment of the present application, calculating the target average energy consumption of a vehicle based on the basic average energy consumption and the historical average energy consumption includes: determining a first weight coefficient of the historical average energy consumption and a second weight coefficient of the basic average energy consumption; calculating a first product of the historical average energy consumption and the first weight coefficient, and calculating a second product of the basic average energy consumption and the second weight coefficient; obtaining the target average energy consumption according to the sum of the first product and the second product.

[0080] Specifically, after obtaining the current driving condition of the vehicle in the embodiment of the present application, the basic average energy consumption of the vehicle can be matched according to the current driving condition of the vehicle. For example, the basic average energy consumption of the air conditioner corresponding to the current driving condition, the basic average energy consumption of the multimedia, etc. When it is recognized or recognized through navigation data that the vehicle is in a certain condition at this time, the basic average energy consumption corresponding to this condition can be used as a part of calculating the final target average energy consumption.

[0081] For example, if the vehicle is currently in a high-speed condition, the basic average energy consumption corresponding to the high-speed condition is matched at this time. For example, the basic average energy consumption of the air conditioner corresponding to the high-speed condition. At this time, the basic average energy consumption of the air conditioner needs to be used as a part of calculating the final target average energy consumption.

[0082] It should be noted that for the basic average energy consumption corresponding to different driving conditions, a relatively accurate basic consumption value needs to be given through a large amount of data accumulation, and the size of this value is strongly correlated with the battery power and the battery temperature.

[0083] Furthermore, after obtaining the basic average energy consumption corresponding to the current driving condition in the embodiment of the present application, it is also necessary to allocate weight values to the basic average energy consumption and the historical average energy consumption obtained above, that is, it is necessary to determine the first weight coefficient b of the historical average energy consumption 1 and the second weight coefficient b of the basic average energy consumption 2 , so that the new average energy consumption, that is, the target average energy consumption, can be obtained according to the sum of the first product of the historical average energy consumption and the first weight coefficient b 1 and the second product of the basic average energy consumption and the second weight coefficient b 2 . Among them, the target average energy consumption can be expressed as:

[0084] Target average energy consumption = b 1 × Historical average energy consumption + b 2 × Basic average energy consumption of the matching condition;

[0085] Among them, b 1 + b 2 = 1, and this weight coefficient can be adjusted by calibration.

[0086] In step S103, the current remaining driving range of the vehicle is obtained according to the current remaining energy state of the battery and the target average energy consumption.

[0087] Specifically, after obtaining the target average energy consumption in the embodiment of the present application, based on the current remaining energy state of the battery obtained above, the current remaining driving range of the vehicle can be obtained, which can be expressed as:

[0088]

[0089] Thus, in the embodiment of the present application, the accuracy of the target average energy consumption of the vehicle can be improved by adding a weight coefficient to the basic average energy consumption of the vehicle matched with the target average energy consumption and the current driving condition.

[0090] According to an embodiment of the present application, after obtaining the current remaining driving range of the vehicle according to the current remaining energy state of the battery and the target average energy consumption, it further includes: determining whether the vehicle enters the power-off mode; if the vehicle enters the power-off mode, storing the current remaining energy state of the battery and the current remaining driving range, and detecting the new remaining energy state of the battery and the new remaining driving range of the vehicle after the vehicle is powered on again; if the difference between the new remaining energy state of the battery and the current remaining energy state of the battery is within the first preset range, or the difference between the new remaining driving range and the current remaining driving range is within the second preset range, using the current remaining driving range; if the difference between the new remaining energy state of the battery and the current remaining energy state of the battery is not within the first preset range, and the difference between the new remaining driving range and the current remaining driving range is not within the second preset range, using the new remaining driving range.

[0091] Wherein, both the first preset range and the second preset range can be threshold ranges obtained through multiple computer simulations, or can be threshold ranges set by those skilled in the art according to historical experience, and are not specifically limited herein.

[0092] Specifically, in order to better display the remaining mileage and improve the display accuracy of the remaining mileage, the embodiment of the present application needs to initialize the display of the remaining mileage.

[0093] Specifically, when the vehicle enters the power-off mode, it is necessary to store the current remaining battery energy state and the current remaining driving range. After the vehicle is powered on again, the new remaining battery energy state and the new remaining driving range of the vehicle are detected. If the difference between the new remaining battery energy state and the current remaining battery energy state is within the updated threshold range, that is, within the first preset range, or the difference between the new remaining driving range and the current remaining driving range is within the updated threshold range, that is, within the second preset range, it indicates that the error between the stored current remaining battery energy state and the current remaining driving range and the new remaining battery energy state and the new remaining driving range calculated after the vehicle is powered on again is very small and will not affect the prediction of the remaining mileage. Therefore, the stored current remaining driving range can be directly used for prediction.

[0094] If the difference between the new remaining battery energy state and the current remaining battery energy state is not within the first preset range, and the difference between the new remaining driving range and the current remaining driving range is not within the second preset range, it indicates that the error between the stored current remaining battery energy state and the current remaining driving range and the new remaining battery energy state and the new remaining driving range calculated after the vehicle is powered on again is relatively large and will affect the prediction of the remaining mileage. Therefore, the new remaining driving range needs to be used for prediction to improve the user's driving experience.

[0095] In summary, the present application can obtain the following beneficial effects based on the above embodiments:

[0096] (1) The present application reduces the influence of signal disturbance on mileage display by preprocessing the key signals of the BMS;

[0097] (2) The present application can calculate the consumption of the current energy consumption by using the historical average energy consumption, and can also obtain the target average energy consumption of the future road conditions, and introduces a weight coefficient. Through different combinations of proportions, it can be provided to the user more accurately, which not only improves the accuracy of mileage prediction, but also effectively reminds the user to choose a shorter route;

[0098] (3) The present application initializes the display of the remaining mileage, thereby improving the user's driving experience.

[0099] According to the method for predicting the remaining mileage of a vehicle according to an embodiment of the present application, the current driving condition, the current remaining energy state of the battery, and the historical operation data of the vehicle are obtained. The basic average energy consumption of the vehicle is matched according to the current driving condition, the historical average energy consumption of the vehicle is determined according to the historical operation data, and the target average energy consumption of the vehicle is calculated according to the basic average energy consumption and the historical average energy consumption. The current remaining driving mileage of the vehicle is obtained according to the current remaining energy state of the battery and the target average energy consumption. Thus, the problems of low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art are solved. By combining the basic average consumption corresponding to the traditional working condition and the historical average energy consumption calculated in segments, and introducing a weight coefficient thereto, the accuracy of calculating the target average energy consumption is improved, and thus accurate prediction of the remaining mileage is achieved.

[0100] Next, a device for predicting the remaining mileage of a vehicle according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0101] Figure 4 It is a block diagram of a device for predicting the remaining mileage of a vehicle according to an embodiment of the present application.

[0102] As Figure 4 shown, the device 10 for predicting the remaining mileage of the vehicle includes: a first acquisition module 100, a calculation module 200, and a second acquisition module 300.

[0103] Among them, the first acquisition module 100 is used to acquire the current driving condition, the current remaining energy state of the battery, and the historical operation data of the vehicle;

[0104] The calculation module 200 is used to match the basic average energy consumption of the vehicle according to the current driving condition, determine the historical average energy consumption of the vehicle according to the historical operation data, and calculate the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption;

[0105] The second acquisition module 300 is used to obtain the current remaining driving mileage of the vehicle according to the current remaining energy state of the battery and the target average energy consumption.

[0106] According to an embodiment of the present application, the calculation module 200 includes:

[0107] The first acquisition unit is used to acquire the energy consumption data within a preset mileage based on the historical operation data;

[0108] The division unit is used to divide the preset mileage based on a preset mileage allocation strategy to obtain the energy consumption data of the vehicle for each mileage segment;

[0109] The first calculation unit is used to determine the weight value of each mileage segment, and calculate the historical average energy consumption of the vehicle according to the weight value of each mileage segment and the energy consumption data of each mileage segment.

[0110] According to an embodiment of the present application, the first acquisition module 100 includes:

[0111] A second acquisition unit, configured to acquire the current state of charge of the vehicle's battery, the current health state of the battery, the theoretical power value of the battery, and the discharge coefficient value of the cell temperature;

[0112] A filtering unit, configured to perform filtering processing on the current state of charge and the current health state, and obtain the current remaining energy state of the battery based on the filtered current state of charge, the filtered current health state, the theoretical power value, and the discharge coefficient value of the cell temperature.

[0113] According to an embodiment of the present application, the calculation module 200 includes:

[0114] A first determination unit, configured to determine a first weight coefficient of the historical average energy consumption and a second weight coefficient of the basic average energy consumption;

[0115] A second calculation unit, configured to calculate a first product of the historical average energy consumption and the first weight coefficient, and calculate a second product of the basic average energy consumption and the second weight coefficient;

[0116] A second determination unit, configured to obtain the target average energy consumption according to the sum of the first product and the second product.

[0117] According to an embodiment of the present application, after obtaining the current remaining driving mileage of the vehicle based on the current remaining energy state of the battery and the target average energy consumption, the second acquisition module 300 further includes:

[0118] A judgment unit, configured to judge whether the vehicle enters the power-off mode;

[0119] A detection unit, configured to, if the vehicle enters the power-off mode, store the current remaining energy state of the battery and the current remaining driving mileage, and detect the new remaining energy state of the battery and the new remaining driving mileage of the vehicle after the vehicle is powered on again;

[0120] A first selection unit, configured to, if the difference between the new remaining energy state of the battery and the current remaining energy state of the battery is within a first preset range, or the difference between the new remaining driving mileage and the current remaining driving mileage is within a second preset range, use the current remaining driving mileage;

[0121] A second selection unit, configured to, if the difference between the new remaining energy state of the battery and the current remaining energy state of the battery is not within the first preset range, and the difference between the new remaining driving mileage and the current remaining driving mileage is not within the second preset range, use the new remaining driving mileage.

[0122] The remaining mileage prediction device of a vehicle according to an embodiment of the present application obtains the current driving condition, the current remaining battery energy state, and the historical operation data of the vehicle, matches the basic average energy consumption of the vehicle according to the current driving condition, determines the historical average energy consumption of the vehicle according to the historical operation data, calculates the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption, and obtains the current remaining driving mileage of the vehicle according to the current remaining battery energy state and the target average energy consumption. Thus, the problems of low prediction accuracy and complex calculation of the remaining mileage of the vehicle in the related art are solved. By combining the basic average consumption corresponding to the traditional working condition and the historical average energy consumption calculated in segments, and introducing a weight coefficient thereto, the accuracy of calculating the target average energy consumption is improved, and thus accurate remaining mileage prediction is realized.

[0123] Figure 5 The structure diagram of the vehicle provided by the embodiment of the present application. The vehicle may include:

[0124] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0125] When the processor 502 executes the program, it implements the remaining mileage prediction method of the vehicle provided in the above embodiment.

[0126] Further, the electronic device further includes:

[0127] A communication interface 503 for communication between the memory 501 and the processor 502.

[0128] The memory 501 is used for storing a computer program executable on the processor 502.

[0129] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0130] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 may be interconnected through a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5It is represented only by a thick line, but it does not mean that there is only one bus or one type of bus.

[0131] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0132] The processor 502 may be a central processing unit (CPU for short), or an application specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present application.

[0133] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the remaining mileage prediction method of the vehicle as described above is implemented.

[0134] This embodiment also provides a computer program product, including a computer program, which is executed to implement the remaining mileage prediction method of the vehicle in the above embodiment.

[0135] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0136] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0137] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations in which functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner or in the reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0138] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as an ordered listing of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0139] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0140] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-mentioned embodiment methods can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0141] In addition, in each of the embodiments of the present application, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0142] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for predicting the remaining mileage of a vehicle, characterized in that: The following steps are involved: Obtain the vehicle's current driving condition, current battery remaining energy status and historical operating data; Matching the basic average energy consumption of the vehicle according to the current driving condition, determining the historical average energy consumption of the vehicle according to the historical operation data, and calculating the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption; The current remaining mileage of the vehicle is obtained according to the current battery remaining energy state and the target average energy consumption.

2. The method according to claim 1, characterized in that The determining the historical average energy consumption of the vehicle according to the historical operation data comprises: Based on the historical operation data, obtaining energy consumption data within a preset mileage; Based on a preset mileage allocation strategy, the preset mileage is divided to obtain energy consumption data of the vehicle in each mileage segment; The weight value of each mileage section is determined, and the historical average energy consumption of the vehicle is calculated according to the weight value of each mileage section and the energy consumption data of each mileage section.

3. The method according to claim 1, characterized in that The obtaining of the current remaining battery energy state of the vehicle includes: Obtaining the current state of charge of the battery of the vehicle, the current health state of the battery, the theoretical power value of the battery, and the discharge coefficient value of the battery cell temperature; The current state of charge and the current state of health are filtered, and the current battery remaining energy state is obtained based on the filtered current state of charge, the filtered current state of health, the theoretical power value, and the discharge coefficient value of the battery cell temperature.

4. The method according to claim 1, characterized in that The calculating the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption includes: Determine a first weight coefficient of the historical average energy consumption and a second weight coefficient of the basic average energy consumption; Calculating a first product of the historical average energy consumption and the first weight coefficient, and calculating a second product of the basic average energy consumption and the second weight coefficient; The target average energy consumption is obtained according to the sum of the first product and the second product.

5. The method according to claim 1, characterized in that: After obtaining the current remaining mileage of the vehicle according to the current battery remaining energy state and the target average energy consumption, the method further includes: Determining whether the vehicle enters a power-off mode; If the vehicle enters the power-off mode, the current battery remaining energy state and the current remaining driving range are stored, and a new battery remaining energy state and a new remaining driving range of the vehicle are detected after the vehicle is powered on again; If the difference between the new battery remaining energy state and the current battery remaining energy state is within a first preset range, or the difference between the new remaining mileage and the current remaining mileage is within a second preset range, then the current remaining mileage is used; If the difference between the new battery remaining energy state and the current battery remaining energy state is not within the first preset range, and the difference between the new remaining mileage and the current remaining mileage is not within the second preset range, the new remaining mileage is used.

6. A vehicle remaining mileage prediction device, characterized in that: include: The first acquisition module is used to acquire the current driving condition of the vehicle, the current remaining battery energy state and historical operation data; a calculation module, configured to match the basic average energy consumption of the vehicle according to the current driving condition, determine the historical average energy consumption of the vehicle according to the historical operation data, and calculate the target average energy consumption of the vehicle according to the basic average energy consumption and the historical average energy consumption; The second acquisition module is used to obtain the current remaining mileage of the vehicle according to the current battery remaining energy state and the target average energy consumption.

7. The device according to claim 6, characterized in that The computing module comprises: A first acquisition unit, configured to acquire energy consumption data within a preset mileage based on the historical operation data; A division unit, configured to divide the preset mileage based on a preset mileage allocation strategy to obtain energy consumption data of the vehicle in each mileage segment; The first calculation unit is used to determine the weight value of each mileage section, and calculate the historical average energy consumption of the vehicle according to the weight value of each mileage section and the energy consumption data of each mileage section.

8. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting the remaining mileage of a vehicle as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the remaining mileage prediction method for a vehicle as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the remaining range prediction method for a vehicle according to any one of claims 1 to 5.

Images